literature review on biometric technology

Multimodal biometrie systems: A systematic review

Ieee account.

• Change Username/Password
• Update Address

Purchase Details

• Payment Options
• Order History
• View Purchased Documents

Profile Information

• Communications Preferences
• Profession and Education
• Technical Interests
• US & Canada: +1 800 678 4333
• Worldwide: +1 732 981 0060
• Contact & Support
• About IEEE Xplore
• Accessibility
• Terms of Use
• Nondiscrimination Policy
• Privacy & Opting Out of Cookies

A not-for-profit organization, IEEE is the world's largest technical professional organization dedicated to advancing technology for the benefit of humanity. © Copyright 2024 IEEE - All rights reserved. Use of this web site signifies your agreement to the terms and conditions.

• Open access
• Published: 08 February 2024

The utility of behavioral biometrics in user authentication and demographic characteristic detection: a scoping review

• O. L. Finnegan   ORCID: orcid.org/0000-0003-0625-1479 1 ,
• J. W. White III 1 ,
• B. Armstrong 1 ,
• E. L. Adams 1 ,
• S. Burkart 1 ,
• M. W. Beets 1 ,
• S. Nelakuditi 2 ,
• E. A. Willis 3 ,
• L. von Klinggraeff 1 ,
• H. Parker 1 ,
• M. Bastyr 1 ,
• Z. Zhong 2 &
• R. G. Weaver 1  

Systematic Reviews volume  13 , Article number:  61 ( 2024 ) Cite this article

949 Accesses

8 Altmetric

Metrics details

Objective measures of screen time are necessary to better understand the complex relationship between screen time and health outcomes. However, current objective measures of screen time (e.g., passive sensing applications) are limited in identifying the user of the mobile device, a critical limitation in children’s screen time research where devices are often shared across a family. Behavioral biometrics, a technology that uses embedded sensors on modern mobile devices to continuously authenticate users, could be used to address this limitation.

The purpose of this scoping review was to summarize the current state of behavioral biometric authentication and synthesize these findings within the scope of applying behavioral biometric technology to screen time measurement.

We systematically searched five databases (Web of Science Core Collection, Inspec in Engineering Village, Applied Science & Technology Source, IEEE Xplore, PubMed), with the last search in September of 2022. Eligible studies were on the authentication of the user or the detection of demographic characteristics (age, gender) using built-in sensors on mobile devices (e.g., smartphone, tablet). Studies were required to use the following methods for authentication: motion behavior, touch, keystroke dynamics, and/or behavior profiling. We extracted study characteristics (sample size, age, gender), data collection methods, data stream, model evaluation metrics, and performance of models, and additionally performed a study quality assessment. Summary characteristics were tabulated and compiled in Excel. We synthesized the extracted information using a narrative approach.

Of the 14,179 articles screened, 122 were included in this scoping review. Of the 122 included studies, the most highly used biometric methods were touch gestures ( n  = 76) and movement ( n  = 63), with 30 studies using keystroke dynamics and 6 studies using behavior profiling. Of the studies that reported age (47), most were performed exclusively in adult populations ( n  = 34). The overall study quality was low, with an average score of 5.5/14.

The field of behavioral biometrics is limited by the low overall quality of studies. Behavioral biometric technology has the potential to be used in a public health context to address the limitations of current measures of screen time; however, more rigorous research must be performed in child populations first.

Systematic review registration

The protocol has been pre-registered in the Open Science Framework database ( https://doi.org/10.17605/OSF.IO/92YCT ).

Peer Review reports

Introduction

Screen time is a critical health behavior related to a variety of health outcomes in children [ 1 , 2 , 3 , 4 , 5 , 6 ]. Historically, measuring screen time has been reliant on self-report or proxy-report measures [ 7 ], due in part to the nature of digital media consumption (e.g., in-home computer and TV use). The introduction of mobile devices (tablets, smartphones) has substantially altered the landscape of digital media consumption, and these devices have become the favored media choice for children due to their portability, interactivity, and capacity to stream a wide range of content [ 8 , 9 , 10 , 11 ]. Self-report measures are of limited validity in assessing mobile screen use due to the intermittent and on-demand use of mobile devices, which hamper one’s ability to retrospectively report screen time [ 7 , 12 , 13 ]. In addition to not being sensitive enough to sufficiently capture all mobile screen use, self-report measures are also subject to recall bias and social desirability bias [ 14 , 15 ]. Given the proliferation of mobile devices [ 8 , 10 ], there has been a growing demand to advance our current screen time measures to more effectively capture mobile screen use [ 16 ], specifically using objective measures [ 17 ].

Researchers have begun to use passive sensing applications (e.g., Chronicle) to overcome the limitations of subjective reports and which unobtrusively monitor mobile screen use on mobile devices [ 13 , 18 ]. Chronicle is an Android passive sensing application that tracks the duration, frequency, and timing of data, general application type, and application status (foreground vs. background) using Google API every 15 s [ 13 ]. Benefits of passive sensing applications include a reduced researcher and participant burden compared to self-report measures and lower cost for researchers to employ. However, while this data can be relevant for tracking the duration of use and the context of use, these passive sensing applications are not able to capture who specifically is using the device. For child screen time research, this limitation in identifying the user of a device is of particular concern as mobile devices are often shared between siblings or between the parent and the child [ 12 , 19 ]. Therefore, identifying the user of the device is critical to optimizing the potential for passive sensing methods in tracking objective screen use metrics in children.

Behavioral biometrics could be used to address this shortcoming of objective screen time measurement by identifying users of mobile devices. Modern mobile devices contain a variety of sensors (e.g., accelerometer, gyroscope, magnetometer, touch) that collect multiple data streams and can provide characteristic information about the user. These sensors provide the basis for behavioral biometric authentication [ 20 , 21 , 22 ]. Unlike physiological biometrics (e.g., fingerprint, iris, facial recognition), behavioral biometrics do not require additional hardware in modern mobile devices [ 23 , 24 ], making it a feasible research tactic for screen time measurement. Additionally, behavioral biometrics can provide continuous user authentication, whereas physiological biometrics are typically a one-time authentication for gaining access to a device [ 23 ]. There are several types of behavioral biometrics used for authentication, including behavior profiling, keystroke dynamics (typing dynamics), touch dynamics, and motion behavior [ 23 ]. Behavior profiling uses data such as the type of applications being used and battery life (host-based approach) as well as calls, texts, and GPS location (network-based approach) for user authentication [ 21 ]. This type of authentication has been used for fraud detection systems, in which unusual activities (e.g., calls, texts) and a new location can identify device theft and subsequently initiate a fraud protection mechanism [ 25 ]. Keystroke dynamics involves the characteristic way in which an individual types, specifically identifying the habitual typing pattern [ 21 ]. There are two types of keystroke dynamics, including static text, which analyzes a fixed text (e.g., a password), and dynamic text, which analyzes free-living text from participants [ 26 ]. Keystroke dynamics have largely been used for fraud detection and for authentication into computers or applications [ 26 ]. Touch dynamics, or touch gestures, evaluates touch strokes (size, length, speed, pressure, direction) and their corresponding coordinates on the touchscreen of a phone. Authentication using touch dynamics began as mobile devices were developed without a physical keyboard and rather a touchscreen [ 20 ]. Lastly, motion behavior authentication relies on the distinct movement patterns of individuals holding and interacting with a mobile device [ 27 , 28 ].

Data produced by these sensors can be harnessed without additional hardware, evidenced by the growing body of research in the field of behavioral biometric authentication [ 21 , 24 , 29 ]. In child screen time research, employing continuous user identification may prove useful, especially when the device is being shared among a child and their family. Furthermore, applying behavioral biometric technology to screen time may be a relatively inexpensive solution, as it leverages built-in technology [ 24 ]. These benefits of behavioral biometrics are important attributes to consider when applying this technology to other contexts.

Behavioral biometric authentication is a highly established field of literature within cybersecurity; however, this technology has not yet been applied to objective screen time measurement research, to continuously identify the user of the mobile device [ 21 , 30 ]. In order to begin applying this technology to screen time measurement, it is important to have an updated understanding of behavioral biometric technology and fit this updated understanding within the perspective of screen time research. The purpose of this scoping review was to first summarize the current state of behavioral biometric authentication, including identifying the behavioral biometric methods and data streams used, the characteristics predicted, and the model evaluation metrics used. This review also sought to characterize these findings within the scope of applying behavioral biometric technology to address the critical limitations of current measures of screen time to provide future directions for applying this technology to a public health context.

This systematic review was conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) Checklist [ 31 ] and was pre-registered in the Open Science Framework database ( https://doi.org/10.17605/OSF.IO/92YCT ).

Information sources, search, and screening

Literature searches were conducted in Web of Science Core Collection, Inspec in Engineering Village, Applied Science & Technology Source, IEEE Xplore, and PubMed, all of which were selected for their relevance to the topic and database size. The final database search was conducted on September 19, 2022. All authors and collaborators discussed the search strategy and the query strings specific to each database. Searches used keywords: smart device, tablet, phone, smartphone, handphone, mobile, Android, iOS, sensor, accelerometer, gyroscope, magnetometer, touch, biometric, hand, motion, move, swipe, keystroke, detect, verify, authenticate, infer, predict, determine, and classify, with Boolean operators, wildcard, and truncation used. The comprehensive list of search terms with notation specific to each database can be found in Additional file 2 : Supplementary Table 2. The primary author (OF) performed the initial search. The search yielded 6,161 results from Web of Science Core Collection, 11,181 results from Inspec, 787 from Applied Science & Technology Source, 3584 from IEEE Xplore, and 823 from PubMed, for a total of 22,537 studies. References were exported to EndNote (Clarivate, London, UK), where an initial duplicate screen was completed using the “remove duplicates” function. Following this, references were exported to Covidence (Melbourne, Australia) for title and abstract screening, where duplicates were also removed, bringing the total studies for title and abstract screening down to 14,179. The primary author (OF) and an additional research assistant screened the titles and abstracts of the 14,179 studies on Covidence. Both reviewers established quality control of their screening process prior to independently screening the articles. This was done by screening 600 of the same articles independently and ensuring reviewers had consistency above 80%. Consistency between reviewers was met (99.9%) and then reviewers divided the remaining articles and independently screened the title and abstracts of those articles. Following title and abstract screening, 13,972 articles were excluded, and 207 articles were left for full text retrieval and screening. Four articles were not able to be located using the Interlibrary Loan (ILL) service; therefore, 203 articles were retrieved for eligibility assessment. The primary author (OF) reviewed the full texts of the 203 articles to assess whether these articles fully met the predefined inclusion and exclusion criteria. Of the 203 articles, 122 articles were considered eligible for inclusion and were extracted (Fig. 1 ).

PRISMA flowchart

Eligibility criteria

Studies were required to focus on the sensors of mobile devices, defined as tablets or smartphones [ 32 ]. These sensors needed to be built-in to the device, including but not limited to motion sensors, accelerometer, gyroscope, magnetometer, and touch. Studies were eligible if they used these sensors for verification, detection, and/or authentication of the device user. Using an adapted version of the Meng 2015 [ 23 ] framework of biometric authentication, articles were required to focus on behavior profiling, keystroke dynamics (typing dynamics), touch dynamics, or motion behavior. Because the first smartphone (i.e., iPhone) was released in 2007 [ 33 ], and modern mobile tablets were developed after this in 2010, only articles after 2007 were included. Articles in a peer-reviewed academic source and published in English were eligible for inclusion. Articles that simulated data and did not collect data on human participants were excluded. Studies were excluded if they used other technology and/or required additional equipment beyond the mobile device (e.g., sensor glove, stylus) for verification. These articles were excluded because of their limited applicability to screen time measurement, as the goal of applying this technology would be to capture the typical way in which the child is interacting with their shared device. Articles that evaluated smart watches, fitness tracking devices, or wearable sensors were excluded. These articles were excluded because the tablet and smartphone are the preferred choice for digital media consumption in children [ 34 ] and are more closely related to health outcomes (e.g., sleep) [ 35 ]. Lastly, while the purpose of this review was to characterize these findings within the lens of child screen time measurement, we did not limit our search to only include studies on children. We included studies on adult populations since it is a relatively newer field and area of application to children and to inform future research on child populations from the current literature on adult biometric authentication.

Data extraction

The primary author (OLF) extracted study characteristics (sample size, age, gender), data collection methods, data stream, model evaluation metrics, and performance of models from the 122 studies. All extracted data was reviewed by a second author (RGW).

Study characteristics

The extraction of study characteristics included details on the sample population, including sample size, gender distribution (number of female participants), and age (mean, standard deviation, and range). There were studies in this review that used freely available dataset(s) for their sample ( n = 27), with several studies compiling data from more than one dataset ( n = 8). Studies that used publicly available datasets for their sample are presented with a superscript letter in Table 1 . Each superscript letter refers to a specific database, with several repeating databases used across studies, as depicted in Table 1 . For studies using more than one dataset for their sample, sample sizes of the datasets were pooled, and number of female participants (gender distribution) were pooled. Additionally, for studies that used more than one dataset, we compiled the age ranges into one comprehensive age range across all included datasets. Device brand (iOS or Android) and outcome predicted (identity, age, or gender) were also extracted from the studies. The protocol of each study included was evaluated to determine whether it was a free-living or in-lab protocol. We defined in-lab protocols as those completed in a researcher-supervised controlled setting, while free-living refers to protocols in which participants use the device in their typical environment (e.g., home, work). Furthermore, we extracted whether the protocol was structured or free-use. We defined structured protocols as those in which the researchers give the participant a specific task to complete on the device, such as a questionnaire, a game, or using a particular application. Free-use protocols refer to protocols in which the participant can interact with the device in their normal manner and select which applications they use, with no restrictions from the researchers.

Extraction of study characteristics also included identifying the biometric method(s) employed for authentication of the user and/or detection of demographic characteristics. We first recorded the biometric method described by each study in the precise language used by the authors. Given the lack of standardized terminology in the field of biometric authentication, these methods needed to be condensed into broader categories. The categories for biometric methods were consolidated into four categories, with agreement from all authors. These categories included movement (encompassing hand movement, arm gesture, hand gesture, and posture), behavior profiling , keystroke dynamics , and touch gestures .

Data stream

Extraction of study characteristics also included the identification of the specific data stream(s), or sensors, used for biometric authentication. We first recorded the data stream(s) used in each study using the precise language used by the authors. Similar to categorizing biometric methods, the categories for data streams also needed to be condensed to broader categories of similar characteristics. These categories included accelerometer (gravity, linear acceleration), orientation , gyroscope (rotation, angular velocity), touch , location , magnetometer , and other (ambient light, Bluetooth, temperature, proximity, application usage, power).

Model evaluation metrics

Extraction of study characteristics also included identifying the model evaluation metric(s) used in each study. We first identified the evaluation metric described in each study using the precise language used by the authors. Metrics were condensed into broader categories given the lack of consistent terminology in machine learning model performance metrics. These categories included area under the curve (receiver operating characteristic), equal error rate (EER), precision , recall (sensitivity, true acceptance rate, true positive rate), false rejection rate (FRR, false negative rate), false acceptance rate (FAR, false positive rate, “false alarm rate”), accuracy (correct recognition rate, mean recognition rate, success rate), F1 score (F-measure), and other (kappa, root mean square error H-mean, detection error tradeoff curve, specificity/true rejection rate, average match rate, mean square error rate, average number of impostor actions, and average number of genuine actions).

Quality assessment

The quality of the included studies was assessed using an adapted framework from Papi 2017 [ 158 ], which is a research quality scale specific to the field of engineering with a focus on sensor technology (Additional file 1 : Supplementary Table 1). The primary author (OLF) assessed the study quality of all 122 studies. Each question was scored as either 1, meeting the criteria, or 0, not meeting the criteria. Composite quality assessment scores were calculated by adding together the number of criteria met, with a score of 14 meaning that the study was of highest quality and a score of 0 meaning that the study was of lowest quality.

Data analysis

The characteristics of the included studies were tabulated in Excel (Microsoft, Version 2304). We then compiled summary statistics in Excel to describe our findings. Means and standard deviations were calculated for sample size and gender distribution across all studies.

Across all 122 studies, sample sizes ranged from 1 to 2221 participants, with an average of 89 participants per study (± 224.2). Android was the most common operating system, with 89 studies (73%) using Android devices for their protocol(s). The iOS operating system was used in 5 (4%) of protocols and the remaining 28 studies (23%) did not report the operating system used. Most of the studies ( n = 112, 92%) identified the specific user of the device, while 5 (4%) studies aimed to detect the gender of the user and 7 studies (6%) aimed to detect the age/age group of the user. For the study setting, most study protocols were conducted in a lab setting ( n = 99, 81%), while fewer studies were carried out in a free-living environment ( n = 17, 14%), one study used both lab and free-living settings, and 5 studies (4%) did not provide sufficient information to determine study setting. Most protocols were structured ( n = 96, 79%), with specific guidance and directions given to the participants on how to interact with the device (e.g., playing a game, watching a specific video). Few studies ( n = 19, 16%) allowed participants to interact with the device in their normal manner, considered “free use” of the device, one study had both structured and free-use, and 6 studies (5%) did not provide sufficient information to determine protocol format. Many studies did not report demographic characteristics of the sample; 75 (61%) did not report gender, 70 (57%) did not report age, and none reported race/ethnicity. Of those that did report gender, on average, the distribution of female participants was 39% of the sample. Of the 122 studies, 75 studies (61%) did not report an age range, 34 studies (28%) had a sample of adults, and 13 studies (11%) had age ranges that included children (< 18 years).

Of the 122 studies included in this review, 63 (52%) used movement (e.g., hand movement, hand or arm gesture and posture) as their biometric method for authentication. Thirty studies (25%) used keystroke dynamics for biometric authentication, while 76 studies (62%) used touch gestures. Behavior profiling, such as app usage, battery, and WiFi, was used in 7 studies (6%) for biometric authentication.

Touch was the most extensively used data stream, with 93 studies using touch behavior for biometric authentication. The accelerometer sensor was the second most frequently used sensor of this review, with n = 68 studies (56%). Other data streams employed include gyroscope ( n = 46 studies, 38%), orientation ( n = 9 studies, 7%), location ( n = 8 studies, 7%), and magnetometer ( n = 22 studies, 18%). As depicted in Additional file 3 : Supplementary Table 3, all other data streams that were used in less than 3 studies were combined into an “Other” category. These included ambient light ( n = 3, 2%), Bluetooth ( n = 3, 2%), temperature ( n = 1), proximity ( n = 3, 2%), application usage ( n = 1), power/battery level ( n = 2), motion quaternion ( n = 1), directional heading ( n = 1), and heat map ( n = 1).

When evaluating the performance of their models, the included studies used a wide range of evaluation metrics. Equal error rate (EER) and accuracy were the most highly used evaluation metrics, with 57 studies (47%) using EER and 56 studies (46%) using accuracy. Following EER and accuracy, false rejection rate (FRR) ( n = 42 studies, 34%) and false acceptance rate (FAR) ( n = 47 studies, 39%) were also highly used to evaluate model performance. Area under the curve (AUC) and the receiver operating characteristic curve (ROC) were used in 20 studies (16%). Recall/sensitivity ( n = 20, 16%), F1 score ( n = 14, 11%), and precision ( n = 10, 8%) were also frequently used among the included studies. As depicted in Additional file 3 : Supplementary Table 3, all other model evaluation metrics that were used in less than 4 studies were combined into an “Other” category. These included kappa ( n = 2), root mean square error (RMSE) ( n = 1), H-mean ( n = 1), detection error tradeoff (DET) curve ( n = 4, 3%), specificity/true rejection rate ( n = 3), average match rate ( n = 1), mean square error rate ( n = 1), average number of impostor actions (ANIA) ( n = 2), and average number of genuine actions (ANGIA/ANGA) ( n = 2).

Quality of the included studies

The average quality score of the included studies was 5.5 out of 14, with a high score of 11 and a low score of 3. The most commonly met criteria were #11, reporting main outcomes, with 122 out of 122 studies meeting this criterion, and #1, clearly stating research objectives, with 121 out of 122 studies meeting this criterion. Most studies also met the criteria for #12, reporting the main findings ( n = 119 studies), and for #13, clearly describing and justifying the statistical tests ( n = 118 studies). The selection of sensors (#9) was appropriately justified in 65 studies, while data handling was clearly described (#10) in 35 studies. Only some studies met the criteria for #14, clearly describing the limitations ( n = 33), or met the criteria for #8, clearly describing the equipment design ( n = 23). Few studies of this review met the criteria for #3, adequately describing the study population ( n = 19), as many did not report demographic characteristics such as age and gender. Only 8 out of the 122 studies met the criteria for #5, appropriately describing the sampling methodology, and only 7 out of the 122 studies met the criteria for #7, providing detailed methods that could be replicated. None of the included studies met the criteria for #4, specifying eligibility criteria, and for #6, providing a rationale for the sample size.

Behavioral biometrics have the potential to improve screen time measurement because researchers can capitalize on built-in mobile device sensors to determine who is using the device at specific time points to address a critical limitation in child screen time research. This scoping review sought to summarize the current state of behavioral biometric authentication, including identifying the behavioral biometric methods used, the data streams used, the characteristics predicted, and the model evaluation metrics used. On a larger scale, this updated understanding of the methodology of behavioral biometric studies can inform future research applying this technology to a public health context.

Overall, in the 122 included studies, the most highly used behavioral biometric methods were touch gestures and movement. The most highly used data streams for behavioral biometric authentication were touch and accelerometry. Motion sensors, such as accelerometer, gyroscope, and magnetometer, are straightforward to access and record with a sensor tracking application (e.g., Sensor Log) on mobile devices. Using touch sensors presents more challenges, both in terms of accessing this sensor stream as well as the privacy and security concerns of participants. Several of the studies using touch in this review used their own gaming application that only tracked touch behavior while the participant was using the application, which has limited applicability to screen time measurement, as it only records touch behavior during the use of one application. In addition to challenges in accessing this sensor stream, there are privacy concerns, as research participants may not feel comfortable with sensor data from their devices being collected continuously. While collecting motion behavior may not be as much of a concern, there may be a particular concern in tracking touch sensor data when using banking applications or typing passwords (e.g., concerns in researchers deciphering passwords). Therefore, while touch is a highly used behavioral biometric method, it may have more limited applicability to screen time measurement when compared to motion sensors (e.g., accelerometer, gyroscope, magnetometer).

Most behavioral biometric authentication studies in this review aimed to identify the user of the device, with fewer studies aiming to detect demographic characteristics, such as age and gender. Studies that used behavioral biometrics to detect age were designed to tailor technology interfaces towards children (e.g., widget layout) and to improve parental control options. Similarly, in studies examining the ability of behavioral biometrics to determine gender, their objective was to adapt interfaces to be more relevant for the user. Based on current evidence, behavioral biometrics are less accurate at detecting demographic characteristics compared to detecting a unique user [ 159 ]. It is likely more challenging to identify similar characteristics in user behavior across a group of individuals, as user interaction can vary substantially on an inter-individual level [ 60 , 159 ]. Relative to applied screen time measurement in a public health context, detecting the age of the user may be a relevant finding to distinguish between the parent and the child when they are sharing the device. However, the ability to detect only the age of a user would not be as useful when a child shares a device with a sibling of a similar age. Thus, determining the unique identity of a user of the device rather than demographic characteristics would be more relevant for research purposes.

Furthermore, of the included studies, a majority of studies used samples of adult participants, with fewer studies tested on samples of children. The lack of research on children highlights a gap in the literature, as there are inherent behavioral differences in the ways in which children interact with mobile devices compared to adults [ 159 ] (e.g., children are more active), and findings from adult studies cannot be universally applied to children. Therefore, we need additional research on biometrics among children before applying this technology to measure children’s screen time.

The most popular model evaluation metrics used in the included studies were equal error rate (EER), accuracy, false acceptance rate (FAR), and false rejection rate (FRR). There were a wide range of model evaluation metrics used across studies, with several reporting the same metric under different terms. For example, several studies used the term “Correct Recognition Rate,” instead of accuracy and “False Positive Rate” instead of false acceptance rate. This highlights a lack of standardization in terminology that is consistent across the field of behavioral biometric authentication, which limits our ability to compare findings across studies.

Of the studies included in this review, the average study quality was low (5.5/14), highlighting the lack of proper reporting in many of the studies in the field of behavioral biometric authentication. Overall, most authors did not provide sufficient information on equipment design, study population, sampling methodology, and eligibility criteria. Very few authors provided adequate justification for their sample size. The insufficiency in reporting key elements of study design limits the ability to replicate these findings in other samples and contexts. Furthermore, the lack of standardization in the terminology used across studies hampers the ability to make larger conclusions on the efficacy of behavioral biometrics and their application in the measurement of children’s screen time.

Behavioral biometric tools and innovative directions for future research

Though the purpose of this review is to examine the current scope of literature on behavioral biometrics through the lens of its application to public health (i.e., screen time measurement), it is necessary to also distinguish this from the domain of behavioral biometrics research for security. Given the vast amount of sensitive information stored on mobile devices, secure user authentication has become a prominent concern and a highly studied concept over recent years [ 22 , 160 , 161 ]. User authentication has shifted from “what you know,” such as an ID, PIN, or password, to “what you are,” or biometric authentication, with behavioral biometrics referring to the specific user-device interaction. A specific framework developed by Bo and colleagues in 2013, SilentSense, provides a touch-based biometrics model that leverages touch events from the system API [ 162 , 163 ]. This tool additionally integrated movement into its scheme, presenting a multi-modal authentication method. Another more recent development in behavioral biometrics is the generation of behavioral biometric datasets using engaging tools [ 164 , 165 ]. There have been challenges in collecting biometric data on participants due to the long protocols necessary to capture sufficient amount of data [ 164 ]. Therefore, researchers have developed gaming applications that collect a variety of behavioral biometric data (e.g., keystroke dynamics, touch gestures, motion) [ 164 , 165 ]. BrainRun, developed by Papamichail and colleagues, is a cognitive skills gaming application that collects touch data. BioGames, developed by Stylios and colleagues, is also a gaming application that collects touch, motion, and keystroke data [ 164 ]. These applications are important tools in the feasible generation of large-scale behavioral biometric data. Lastly, a challenge within behavioral biometrics research is the power usage concerns on mobile devices, particularly for continuous authentication methods. In future behavioral biometrics research, power consumption of individual applications should be monitored to ensure that the authentication application is not substantially impacting the device battery. Power consumption of individual applications can be monitored using a method from Murmuria and colleagues that uses per-subsystem time shares from the operating system, which can provide clarity on the feasibility of deploying behavioral biometric methods in a larger research context [ 166 ].

Advantages and disadvantages of the behavioral biometric methods

As these behavioral biometric methods have been highly studied and applied for use within the field of cybersecurity, this work has highlighted some of the advantages and disadvantages associated with each of these methods. While all methods are subject to privacy concerns [ 30 ], behavior profiling in particular has been scrutinized for its reliance on sensitive and private data (e.g., calls, texts, location). However, an advantage of behavior profiling is that unlike other methods (e.g., keystroke dynamics), it does not require the user to perform a specific activity for authentication [ 25 ]. A disadvantage of keystroke dynamics is that its accuracy for user authentication can be impacted by factors including injury, psychological state (e.g., stress), and distraction [ 167 ]. Additionally, the way in which an individual types on a keyboard is considered less permanent than other traits, such as physiological biometrics (e.g., facial and fingerprint recognition) [ 167 ]. However, relative to other authentication methods, keystroke dynamics is relatively low cost and does not rely on external hardware. Additionally, the way in which an individual types is challenging to replicate; therefore, this method can detect impostors more effectively [ 167 ]. An advantage of touch dynamics authentication is that the user does not need to complete a specific task for authentication; rather, this method works continuously in the background [ 20 ]. However, a disadvantage could be identifying the most salient features for user authentication, as using a large number of touch features increases data size and subsequently can slow down authentication speed [ 23 ]. Lastly, motion authentication can be impacted by behavioral variability, as this type of authentication is reliant on the user to interact with the device similarly over time [ 168 ]. However, similar to other methods, motion authentication can be an unobtrusive authentication method [ 168 ], and there may be less privacy concerns compared to touch-based authentication.

Methodological considerations and implications for future research

Subsequent research should examine the effectiveness of behavioral biometrics to determine the user of the device among children across development. Most of the studies included in this review exclusively used adult samples, which has limited applicability to child screen time research. The present review also highlighted the lack of studies being done on iOS devices (iPhone, iPad) in the field of behavioral biometrics. This is a limitation of the field because iOS use is highly prevalent, as 55% of tablets in the USA are iPads [ 169 ]. In 2022, over 50% of smartphone owners in the USA used an iPhone, surpassing Android for the first time in history [ 170 ]. A majority of the studies ( n = 85) tested Android devices, with only 5 studies using an iOS operating system, warranting further testing on a diversity of devices, including both iOS and Android.

When applying this technology to objective screen time measurement, participants may be apprehensive about researchers tracking mobile device usage data. However, there are practices in place to reduce concerns with tracking technology. Specifically with the passive-sensing application Chronicle, data are not associated with IP addresses or phone numbers and only indicate the type of application used (e.g., educational, social media), not the information on websites visited or the content of messages and emails. Parents are comfortable with using passive sensing technology when participating in a research study, as indicated by a feasibility study reporting no dropouts due to privacy concerns in using this technology [ 171 ]. While passive sensing applications have been shown to be accepted for use by families, future research can examine the extent to which families are comfortable with sensor tracking technology (e.g., accelerometer, gyroscope, touch) continuously monitoring user behavior on shared mobile devices. Prior to employing this technology in screen time measurement on a large scale, a necessary first step is to determine the feasibility and acceptability of this technology for families participating in research.

Additionally, research using this technology to measure screen time should consider the storage and battery life concerns inherent to using mobile device usage data. The computational burden of running applications to track sensor data may impact the feasibility of longitudinally monitoring screen time behavior in children [ 30 ]. Selecting the appropriate sensor tracking application and sampling frequency to use, as well as only recording sensor data when the device is unlocked must be a priority for researchers using behavioral biometric technology for screen time research [ 159 ].

Lastly, within the field of behavioral biometric authentication, there is a necessity to standardize the terminology used to describe various elements of behavioral biometrics. The lack of uniform language needs to be addressed to apply this technology on a larger scale. A way in which the field of behavioral biometrics can move towards more cohesive language is by adopting best-practice guidelines for reporting performance metrics, similar to the fields of physical activity measurement [ 172 ] and sleep measurement [ 173 ].

The present review has several strengths, including a comprehensive review of the current state of behavioral biometric authentication. This provided an updated evaluation of the most highly used behavioral biometric methods, data streams, and model evaluation metrics. The current review is limited by the low quality of the included studies and the lack of consistency in the terminology used across studies. Given the lack of standardization in model evaluation metrics, we were unable to sum results across studies and use meta-analytic methods to evaluate the overall efficacy of behavioral biometrics in identifying the user of a device. Furthermore, a limitation of the current review is the narrow focus on behavioral biometrics (touch, accelerometry, behavioral profiling) and not including studies on physiological biometrics. While physiological biometrics presents an important tool in authentication, these sensors (e.g., camera, video) are not freely available and feasible to use in public health research. Despite these limitations, behavioral biometric technology highlights a window of opportunity, as it shows the initial potential to harness sensor data to identify the user of a device. This review can inform future research applying behavioral biometric technology to contexts outside of cybersecurity and to address the limitations of objective measures of screen time.

Availability of data and materials

Not applicable; no datasets were generated or analyzed during the current study.

Domingues-Montanari S. Clinical and psychological effects of excessive screen time on children: effects of screen time on children. J Paediatr Child Health. 2017;53(4):333–8. https://doi.org/10.1111/jpc.13462 .

Article   PubMed   Google Scholar  

Liu W, Wu X, Huang K, et al. Early childhood screen time as a predictor of emotional and behavioral problems in children at 4 years: a birth cohort study in China. Environ Health Prev Med. 2021;26(1):3. https://doi.org/10.1186/s12199-020-00926-w .

Article   PubMed   PubMed Central   Google Scholar  

Saunders TJ, Vallance JK. Screen time and health indicators among children and youth: current evidence, limitations and future directions. Appl Health Econ Health Policy. 2017;15(3):323–31. https://doi.org/10.1007/s40258-016-0289-3 .

Tezol O, Yildiz D, Yalcin S, et al. Excessive screen time and lower psychosocial well-being among preschool children. Arch Pediatr. 2022;29(1):61–6. https://doi.org/10.1016/j.arcped.2021.10.003 .

Article   CAS   PubMed   Google Scholar  

Webster EK, Martin CK, Staiano AE. Fundamental motor skills, screen-time, and physical activity in preschoolers. J Sport Health Sci. 2019;8(2):114–21. https://doi.org/10.1016/j.jshs.2018.11.006 .

Muppalla SK, Vuppalapati S, Reddy Pulliahgaru A, Sreenivasulu H. Effects of excessive screen time on child development: an updated review and strategies for management. Cureus. https://doi.org/10.7759/cureus.40608 . Published online June 18, 2023.

K. Kaye L, Orben A, A. Ellis D, C. Hunter S, Houghton S. The conceptual and methodological mayhem of “screen time.” IJERPH. 2020;17(10):3661. https://doi.org/10.3390/ijerph17103661 .

Kabali HK, Irigoyen MM, Nunez-Davis R, et al. Exposure and use of mobile media devices by young children. Pediatrics. 2015;136(6):1044–50. https://doi.org/10.1542/peds.2015-2151 .

Radesky JS, Schumacher J, Zuckerman B. Mobile and interactive media use by young children: the good, the bad, and the unknown. Pediatrics. 2015;135(1):1–3. https://doi.org/10.1542/peds.2014-2251 .

Computer and Internet Use in the United States: 2018. Published online April 21, 2021. https://www.census.gov/newsroom/press-releases/2021/computer-internet-use.html#:~:text=Smartphones%20were%20present%20in%2084,ownership%20fell%20behind%20at%2063%25 .

Auxier B, Anderson M, Turner E. Children’s engagement with digital devices, screen time. Published online July 28, 2020.

Radesky JS, Weeks HM, Ball R, et al. Young children’s use of smartphones and tablets. Pediatrics. 2020;146(1):e20193518. https://doi.org/10.1542/peds.2019-3518 .

Barr R, Kirkorian H, Radesky J, et al. Beyond screen time: a synergistic approach to a more comprehensive assessment of family media exposure during early childhood. Front Psychol. 2020;11:1283. https://doi.org/10.3389/fpsyg.2020.01283 .

Guo N, Luk TT, Wang MP, et al. Self-reported screen time on social networking sites associated with problematic smartphone use in Chinese adults: a population-based study. Front Psychiatry. 2021;11:614061. https://doi.org/10.3389/fpsyt.2020.614061 .

Sewall CJR, Bear TM, Merranko J, Rosen D. How psychosocial well-being and usage amount predict inaccuracies in retrospective estimates of digital technology use. Mob Media Commun. 2020;8(3):379–99. https://doi.org/10.1177/2050157920902830 .

Article   Google Scholar  

Understanding how digital media affects child development. NIH Eunice Kennedy shriver national institute of child health and human development. 2023. https://www.nichd.nih.gov/about/org/od/directors_corner/prev_updates/digital-media-child-development-feb2023 .

Perez O, Garza T, Hindera O, et al. Validated assessment tools for screen media use: a systematic review. Karakulah AS, ed. PLoS One. 2023;18(4):e0283714. https://doi.org/10.1371/journal.pone.0283714 .

Article   CAS   PubMed   PubMed Central   Google Scholar  

Domoff SE, Banga CA, Borgen AL, et al. Use of passive sensing to quantify adolescent mobile device usage: feasibility, acceptability, and preliminary validation of the eMoodie application. Human Behav Emerg Tech. 2021;3(1):63–74. https://doi.org/10.1002/hbe2.247 .

Livingstone S, Mascheroni G, Dreier M, Chaudron S, Lagae K. How parents of young children manage digital devices at home: the role of income, education, and parental style. London: EU Kids Online, LSE; 2015. ISSN 2045-256X.

Teh PS, Zhang N, Teoh ABJ, Chen K. A survey on touch dynamics authentication in mobile devices. Comput Secur. 2016;59:210–35. https://doi.org/10.1016/j.cose.2016.03.003 .

Mahfouz A, Mahmoud TM, Eldin AS. A survey on behavioral biometric authentication on smartphones. J Inf Secur Applic. 2017;37:28–37. https://doi.org/10.1016/j.jisa.2017.10.002 .

Ibrahim TM, Abdulhamid SM, Alarood AA, et al. Recent advances in mobile touch screen security authentication methods: a systematic literature review. Comput Secur. 2019;85:1–24. https://doi.org/10.1016/j.cose.2019.04.008 .

Meng W, Wong DS, Furnell S, Zhou J. Surveying the development of biometric user authentication on mobile phones. IEEE Commun Surv Tutorials. 2015;17(3):1268–93. https://doi.org/10.1109/COMST.2014.2386915 .

Abdulhak SA, Abdulaziz AA. A systematic review of features identification and extraction for behavioral biometrie authentication in touchscreen mobile devices. In: 2018 20th International Conference on Advanced Communication Technology (ICACT). New York: IEEE; 2018. p. 68–73. https://doi.org/10.23919/ICACT.2018.8323648 .

Li F, Clarke N, Papadaki M, Dowland P. Active authentication for mobile devices utilising behaviour profiling. Int J Inf Secur. 2014;13(3):229–44. https://doi.org/10.1007/s10207-013-0209-6 .

Pisani PH, Lorena AC. A systematic review on keystroke dynamics. J Braz Comput Soc. 2013;19(4):573–87. https://doi.org/10.1007/s13173-013-0117-7 .

Kumar R, Kundu PP, Shukla D, Phoha VV. Continuous user authentication via unlabeled phone movement patterns. In: 2017 IEEE International Joint Conference on Biometrics (IJCB). New York: IEEE; 2017. p. 177–184. https://doi.org/10.1109/BTAS.2017.8272696 .

Davarci E, Anarim E. User identification on smartphones with motion sensors and touching behaviors. In: 2022 30th Signal Processing and Communications Applications Conference (SIU). New York: IEEE; 2022. p. 1–4. https://doi.org/10.1109/SIU55565.2022.9864837 .

Alzubaidi A, Kalita J. Authentication of smartphone users using behavioral biometrics. IEEE Commun Surv Tutorials. 2016;18(3):1998–2026. https://doi.org/10.1109/COMST.2016.2537748 .

Stylios I, Kokolakis S, Thanou O, Chatzis S. Behavioral biometrics & continuous user authentication on mobile devices: a survey. Inf Fusion. 2021;66:76–99. https://doi.org/10.1016/j.inffus.2020.08.021 .

Tricco AC, Lillie E, Zarin W, et al. PRISMA Extension for Scoping Reviews (PRISMA-ScR): checklist and explanation. Ann Intern Med. 2018;169(7):467–73. https://doi.org/10.7326/M18-0850 .

Silverio-Fernández M, Renukappa S, Suresh S. What is a smart device? - a conceptualisation within the paradigm of the Internet of Things. Vis in Eng. 2018;6(1):3. https://doi.org/10.1186/s40327-018-0063-8 .

Goggin G. Adapting the mobile phone: the iPhone and its consumption. Continuum. 2009;23(2):231–44. https://doi.org/10.1080/10304310802710546 .

Papadakis S, Alexandraki F, Zaranis N. Mobile device use among preschool-aged children in Greece. Educ Inf Technol. 2022;27(2):2717–50. https://doi.org/10.1007/s10639-021-10718-6 .

Domoff SE, Borgen AL, Foley RP, Maffett A. Excessive use of mobile devices and children’s physical health. Hum Behav Emerg Tech. 2019;1(2):169–75. https://doi.org/10.1002/hbe2.145 .

Davis SP, Ashayer A, Tabrizi N. Predicting sex and age using swipe-gesture data from a mobile device. In: 2020 IEEE Intl Conf on Parallel & Distributed Processing with Applications, Big Data & Cloud Computing, Sustainable Computing & Communications, Social Computing & Networking (ISPA/BDCloud/SocialCom/SustainCom). IEEE; 2020. p. 1136-1143. https://doi.org/10.1109/ISPA-BDCloud-SocialCom-SustainCom51426.2020.00169 .

Sun Z, Wang Y, Qu G, Zhou Z. A 3-D hand gesture signature based biometric authentication system for smartphones. Secur Comm Networks. 2016;9(11):1359–73. https://doi.org/10.1002/sec.1422 .

Liu Q, Wang M, Zhao P, Yan C, Ding Z. A behavioral authentication method for mobile gesture against resilient user posture. In: 2016 3rd International Conference on Systems and Informatics (ICSAI). IEEE; 2016. p. 324–331. https://doi.org/10.1109/ICSAI.2016.7810976 .

Maghsoudi J, Tappert CC. A behavioral biometrics user authentication study using motion data from android smartphones. In: 2016 European Intelligence and Security Informatics Conference (EISIC). IEEE; 2016. p. 184-187. https://doi.org/10.1109/EISIC.2016.047 .

Putri AN, Asnar YDW, Akbar S. A continuous fusion authentication for Android based on keystroke dynamics and touch gesture. In: 2016 International Conference on Data and Software Engineering (ICoDSE). New York: IEEE; 2016. p. 1–6. https://doi.org/10.1109/ICODSE.2016.7936146 .

Lamiche I, Bin G, Jing Y, Yu Z, Hadid A. A continuous smartphone authentication method based on gait patterns and keystroke dynamics. J Ambient Intell Human Comput. 2019;10(11):4417–30. https://doi.org/10.1007/s12652-018-1123-6 .

Smith-Creasey M, Rajarajan M. A continuous user authentication scheme for mobile devices. In: 2016 14th Annual Conference on Privacy, Security and Trust (PST). New York: IEEE; 2016. p .104–113. https://doi.org/10.1109/PST.2016.7906944 .

Shih DH, Lu CM, Shih MH. A flick biometric authentication mechanism on mobile devices. In: 2015 Internationalonal Conference on Informative and Cybernetics for Computational Social Systems (ICCSS). New York: IEEE; 2015. p. 31–33. https://doi.org/10.1109/ICCSS.2015.7281144 .

Zaidi AZ, Chong CY, Parthiban R, Sadiq AS. A framework of dynamic selection method for user classification in touch-based continuous mobile device authentication. J Inform Secur Applic. 2022;67:103217. https://doi.org/10.1016/j.jisa.2022.103217 .

Soni D, Hanmandlu M, Saini HC. A Machine learning approach for user authentication using touchstroke dynamics. In: Somani AK, Srivastava S, Mundra A, Rawat S, eds. Proceedings of First International Conference on Smart System, Innovations and Computing. Vol 79. Smart Innovation, Systems and Technologies. Singapore: Springer Singapore; 2018. p. 391–410. https://doi.org/10.1007/978-981-10-5828-8_38 .

Lin CC, Chang CC, Liang D, Yang CH. A new non-intrusive authentication method based on the orientation sensor for smartphone users. In: 2012 IEEE Sixth International Conference on Sofware Security and Reliability. New York: IEEE; 2012. p. 245–252. https://doi.org/10.1109/SERE.2012.37 .

Li G, Bours P. A novel mobilephone application authentication approach based on accelerometer and gyroscope data. In: 2018 International Conference of the Biometrics Special Interest Group (BIOSIG). New York City: IEEE; 2018. p. 1–4. https://doi.org/10.23919/BIOSIG.2018.8553503 .

Smith-Creasey M, Rajarajan M. A novel scheme to address the fusion uncertainty in multi-modal continuous authentication schemes on mobile devices. In: 2019 International Conference on Biometrics (ICB). New York City; IEEE; 2019. p. 1–8. https://doi.org/10.1109/ICB45273.2019.8987390 .

Salem A, Obaidat MS. A novel security scheme for behavioral authentication systems based on keystroke dynamics. Secur Priv. 2019;2(2):e64. https://doi.org/10.1002/spy2.64 .

Zhao S, Guo Z, Zhong C, Xian L, Liu Y. A novel smartphone identity authentication mechanism. In: Proceedings of the ACM Turing Celebration Conference - China. New York: ACM; 2020. p. 157–161. https://doi.org/10.1145/3393527.3393554 .

Qiao M, Zhang S, Sung AH, Liu Q. A novel touchscreen-based authentication scheme using static and dynamic hand biometrics. In: 2015 IEEE 39th Annual Computer Software and Applications Conference. New York: IEEE; 2015. p. 494–503. https://doi.org/10.1109/COMPSAC.2015.133 .

Smith-Creasey M, Rajarajan M. A novel word-independent gesture-typing continuous authentication scheme for mobile devices. Comput Secur. 2019;83:140–50. https://doi.org/10.1016/j.cose.2019.02.001 .

Alariki AA, Bt Abdul Manaf A, Khan S. A study of touching behavior for authentication in touch screen smart devices. In: 2016 International Conference on Intelligent Systems Engineering (ICISE). New York: IEEE; 2016. p. 216–221. https://doi.org/10.1109/INTELSE.2016.7475123 .

Lee SH, Roh JH, Kim S, Jin SH. A study on feature of keystroke dynamics for improving accuracy in mobile environment. In: Choi D, Guilley S, eds. Information Security Applications. Vol 10144. Lecture Notes in Computer Science. New York: Springer International Publishing; 2017. p. 366–375. https://doi.org/10.1007/978-3-31956549-1_31 .

Li W, Tan J, Meng W, Wang Y. A swipe-based unlocking mechanism with supervised learning on smartphones: design and evaluation. J Netw Comput Applic. 2020;165:102687. https://doi.org/10.1016/j.jnca.2020.102687 .

Saini BS, Singh P, Nayyar A, et al. A three-step authentication model for mobile phone user using keystroke dynamics. IEEE Access. 2020;8:125909125922. https://doi.org/10.1109/ACCESS.2020.3008019 .

Takahashi H, Ogura K, Bista BB, Takata T. A user authentication scheme using keystrokes for smartphones while moving. New York: IEEE; 2016.

Deb D, Ross A, Jain AK, Prakah-Asante K, Prasad KV. Actions speak louder than (Pass)words: passive authentication of smartphone users via deep temporal features. In: 2019 International Conference on Biometrics (ICB). IEEE; 2019. p. 1–8. https://doi.org/10.1109/ICB45273.2019.8987433 .

Leingang W, Gunn D, Kim JH, Yuan X, Roy K. Active authentication using touch dynamics. In: SoutheastCon 2018. New York: IEEE; 2018.p. 1–5. https://doi.org/10.1109/SECON.2018.8479298 .

Acien A, Morales A, Fierrez J, Vera-Rodriguez R, Hernandez-Ortega J. Active detection of age groups based on touch interaction. IET Biom. 2019;8(1):101–8. https://doi.org/10.1049/iet-bmt.2018.5003 .

Mahbub U, Sarkar S, Patel VM, Chellappa R. Active user authentication for smartphones: A challenge data set and benchmark results. In: 2016 IEEE 8th International Conference on Biometrics Theory, Applications and Systems (BTAS). New York: IEEE; 2016. p. 1–8. https://doi.org/10.1109/BTAS.2016.7791155 .

Guarino A, Lemeri N, Malandrino D, Zaccagnino R, Capo C. Adam or Eve? Automatic users’ gender classification via gestures analysis on touch devices. Neural Comput Applic. 2022;34(21):18473–95. https://doi.org/10.1007/s00521-022-07454-4 .

Wang S, Yuan J, Wen J. Adaptive phone orientation method for continuous authentication based on mobile motion sensors. In: 2019 IEEE 31st International Conference on Tools with Artificial Intelligence (ICTAI). New York: IEEE; 2019. p. 1623–1627. https://doi.org/10.1109/ICTAI.2019.00236 .

Davarci E, Soysal B, Erguler I, Aydin SO, Dincer O, Anarim E. Age group detection using smartphone motion sensors. In: 2017 25th European Signal Processing Conference (EUSIPCO). New York: IEEE; 2017. p. 2201–2205. https://doi.org/10.23919/EUSIPCO.2017.8081600 .

Chakraborty B, Nakano K, Tokoi Y, Hashimoto T. An approach for designing low cost deep neural network based biometric authentication model for smartphone user. In: TENCON 2019 - 2019 IEEE Region 10 Conference (TENCON). New York: IEEE; 2019. p. 772–777. https://doi.org/10.1109/TENCON.2019.8929241 .

Antal M, Szabo LZ. An Evaluation of One-class and two-class classification algorithms for keystroke dynamics authentication on mobile devices. In: 2015 20th International Conference on Control Systems and Computer Science. New York: IEEE; 2015. p. 343–350. https://doi.org/10.1109/CSCS.2015.16 .

Roy A, Halevi T, Memon N. An HMM-based multi-sensor approach for continuous mobile authentication. In: MILCOM 2015 - 2015 IEEE Military Communications Conference. New York: IEEE; 2015. p. 1311–1316. https://doi.org/10.1109/MILCOM.2015.7357626 .

Salem A, Zaidan D, Swidan A, Saifan R. Analysis of strong password using keystroke dynamics authentication in touch screen devices. In: 2016 Cybersecurity and Cyberforensics Conference (CCC). New York: IEEE; 2016. p. 15–21. https://doi.org/10.1109/CCC.2016.11 .

Roy S, Roy U, Sinha DD. Analysis of typing pabern in identifying sop biometric information and its impact in user recognition. In: Chandra P, Giri D, Li F, Kar S, Jana DK, eds. Information Technology and Applied Mathematics. Vol 699. Advances in Intelligent Systems and Computing. Singapore: Springer Singapore; 2019. p. 69–83. https://doi.org/10.1007/978-981-10-7590-2_5 .

Lee W. Analyzing motion of touching screen for inferring user characteristics. In: 2021 Twelfth International Conference on Ubiquitous and Future Networks (ICUFN). New York: IEEE; 2021. p. 78–80. https://doi.org/10.1109/ICUFN49451.2021.9528699 .

Buriro A, Crispo B, Conti M. AnswerAuth: a bimodal behavioral biometric-based user authentication scheme for smartphones. J Inform Secur Applic. 2019;44:89–103. https://doi.org/10.1016/j.jisa.2018.11.008 .

Praher C, Sonntag M. Applicability of keystroke dynamics as a biometric security feature for mobile touchscreen devices with virtualised keyboards. IJICS. 2016;8(1):72. https://doi.org/10.1504/IJICS.2016.075311 .

Baran M, Siwik L, Rzecki K. Application of elastic principal component analysis to person recognition based on screen gestures. In: Rutkowski L, Scherer R, Korytkowski M, Pedrycz W, Tadeusiewicz R, Zurada JM, eds. Artificial Intelligence and Soft Computing. Vol 11508. Lecture Notes in Computer Science. New York: Springer International Publishing; 2019. p. 553–560. https://doi.org/10.1007/978-3-030-20912-4_50 .

Ali Z, Payton J, Sritapan V. At your fingertips: considering finger distinctness in continuous touch-based authentication for mobile devices. In: 2016 IEEE Security and Privacy Workshops (SPW). New York: IEEE; 2016. p. 272–275. https://doi.org/10.1109/SPW.2016.29 .

Guerra-Casanova J, Sánchez-Ávila C, Bailador G, de Santos SA. Authentication in mobile devices through hand gesture recognition. Int J Inf Secur. 2012;11(2):65–83. https://doi.org/10.1007/s10207-012-0154-9 .

Primo A. Keystroke-based continuous authentication while listening to music on your smart-phone. In: 2017 IEEE 8th Annual Ubiquitous Computing, Electronics and Mobile Communication Conference (UEMCON). New York: IEEE; 2017. p. 217–225. https://doi.org/10.1109/UEMCON.2017.8249029 .

Yang Y, Guo B, Wang Z, Li M, Yu Z, Zhou X. BehaveSense: continuous authentication for security-sensitive mobile apps using behavioral biometrics. Ad Hoc Netw. 2019;84:9–18. https://doi.org/10.1016/j.adhoc.2018.09.015 .

Wolff M. Behavioral biometric identification on mobile devices. In: Schmorrow DD, Fidopiastis CM, eds. Foundations of Augmented Cognition. Vol 8027. Lecture Notes in Computer Science. Berlin: Springer Berlin Heidelberg; 2013. p. 783–791. https://doi.org/10.1007/978-3-642-39454-6_84 .

Tse KW, Hung K. Behavioral biometrics scheme with keystroke and swipe dynamics for user authentication on mobile platform. In: 2019 IEEE 9th Symposium on Computer Applications & Industrial Electronics (ISCAIE). New York: IEEE; 2019. p. 125–130. https://doi.org/10.1109/ISCAIE.2019.8743995 .

Antal M, Szabó LZ. Biometric authentication based on touchscreen swipe patterns. Proc Technol. 2016;22:862–9. https://doi.org/10.1016/j.protcy.2016.01.061 .

Laghari A, Waheed-ur-Rehman, Memon ZA. Biometric authentication technique using smartphone sensor. In: 2016 13th International Bhurban Conference on Applied Sciences and Technology (IBCAST). New York: IEEE; 2016. p. 381–384. https://doi.org/10.1109/IBCAST.2016.7429906 .

Tolosana R, Vera-Rodriguez R, Fierrez J, Morales A. BioTouchPass demo: handwriben passwords for touchscreen biometrics. In: Proceedings of the 27th ACM International Conference on Multimedia. New York: ACM; 2019. p. 1023–1025. https://doi.org/10.1145/3343031.3350578 .

Ray A, Hou D, Schuckers S, Barbir A. Continuous authentication based on hand micro-movement during smartphone form filling by seated human subjects: In: Proceedings of the 7th International Conference on Information Systems Security and Privacy.  Setubal, Portugal: SCITEPRESS - Science and Technology Publications; 2021. p. 424–431. https://doi.org/10.5220/0010225804240431 .

Ambol S, Rashad S. Continuous authentication of smartphone users using machine learning. In: 2020 11th IEEE Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON). New York: IEEE; 2020. p. 0056–0062. https://doi.org/10.1109/UEMCON51285.2020.9298040 .

Garbuz A, Epishkina A, Kogos K. Continuous authentication of smartphone users via swipes and taps analysis. In: 2019 European Intelligence and Security Informatics Conference (EISIC). New York: IEEE; 2019. p. 48–53. https://doi.org/10.1109/EISIC49498.2019.9108780 .

Dybczak J, Nawrocki P. Continuous authentication on mobile devices using behavioral biometrics. In: 2022 22nd IEEE International Symposium on Cluster, Cloud and Internet Computing (CCGrid). New York: IEEE; 2022. p. 1028–1035. https://doi.org/10.1109/CCGrid54584.2022.00125 .

Murmuria R, Stavrou A, Barbará D, Fleck D. Continuous authentication on mobile devices using power consumption, touch gestures and physical movement of users. In: Bos H, Monrose F, Blanc G, eds. Research in Attacks, Intrusions, and Defenses. Vol 9404. Lecture Notes in Computer Science. New York: Springer International Publishing; 2015. p. 405–424. https://doi.org/10.1007/978-3-319-26362-5_19 .

Karanikiotis T, Papamichail MD, Chatzidimitriou KC, Oikonomou NCI, Symeonidis AL, Saripalle SK. Continuous implicit authentication through touch traces modelling. In: 2020 IEEE 20th International Conference on Software Quality, Reliability and Security (QRS). New York: IEEE; 2020. p. 111–120. https://doi.org/10.1109/QRS51102.2020.00026 .

Zhao X, Feng T, Shi W. Continuous mobile authentication using a novel graphic touch gesture feature. In: 2013 IEEE Sixth International Conference on Biometrics: Theory, Applications and Systems (BTAS). IEEE; 2013. p. 1–6. https://doi.org/10.1109/BTAS.2013.6712747 .

Zhao X, Feng T, Lu X, Shi W, Kakadiaris IA. Continuous mobile authentication using user-phone interaction. Inst Eng Technol. 2017;3:209–33. https://doi.org/10.1049/PBSE003E_ch8 . 

Leyfer K, Spivak A. Continuous user authentication by the classification method based on the dynamic touchscreen biometrics. In: 2019 24th Conference of Open Innovations Association (FRUCT). New York City IEEE; 2019. p. 228–234. https://doi.org/10.23919/FRUCT.2019.8711941 .

Herath HMCKB, Dulanga KGC, Tharindu NVD, Ganegoda GU. Continuous user authentication using keystroke dynamics for touch devices. In: 2022 2nd International Conference on Image Processing and Robotics (ICIPRob). New York: IEEE; 2022. p. 1–6. https://doi.org/10.1109/ICIPRob54042.2022.9798728 .

Kumar R, Kundu PP, Shukla D, Phoha VV. Continuous user authentication via unlabeled phone movement patterns. In: 2017 IEEE International Joint Conference on Biometrics (IJCB). New York: IEEE; 2017. p. 177–184.  https://doi.org/10.1109/BTAS.2017.8272696 .

Barlas Y, Basar OE, Akan Y, Isbilen M, Alptekin GI, Incel OD. DAKOTA: continuous authentication with behavioral biometrics in a mobile banking application. In: 2020 5th International Conference on Computer Science and Engineering (UBMK). New York: IEEE; 2020. p. 1–6. https://doi.org/10.1109/UBMK50275.2020.9219365 .

Incel OD, Gunay S, Akan Y, et al. DAKOTA: sensor and touch screen-based continuous authentication on a mobile banking application. IEEE Access. 2021;9:38943–60. https://doi.org/10.1109/ACCESS.2021.3063424 .

Hernandez-Ortega J, Morales A, Fierrez J, Acien A. Detecting age groups using touch interaction based on neuromotor characteristics. Electron Lea. 2017;53(20):1349–50. https://doi.org/10.1049/el.2017.0492 .

Article   ADS   Google Scholar  

Nguyen TV, Sae-Bae N, Memon N. DRAW-A-PIN: authentication using finger-drawn PIN on touch devices. Comput Secur. 2017;66:115–28. https://doi.org/10.1016/j.cose.2017.01.008 .

Al-Showarah SA. Dynamic recognition for user age-group classification using hand-writing based finger on smartphones. In: 2019 10th International Conference on Information and Communication Systems (ICICS). New York: IEEE; 2019. p. 140–146. https://doi.org/10.1109/IACS.2019.8809083 .

Ng’ang’a A, Musuva PMW. Enhancing accuracy in a touch operation biometric system: a case on the android pabern lock scheme. Mobile Inform Syst. 2020;2020:1–12. https://doi.org/10.1155/2020/4165457 .

Ray-Dowling A, Hou D, Schuckers S, Barbir A. Evaluating multi-modal mobile behavioral biometrics using public datasets. Comput Secur. 2022;121:102868. https://doi.org/10.1016/j.cose.2022.102868 .

Buriro A, Gupta S, Crispo B. Evaluation of motion-based touch-typing biometrics for online banking. In: 2017 International Conference of the Biometrics Special Interest Group (BIOSIG). New York: IEEE; 2017. p. 1–5. https://doi.org/10.23919/BIOSIG.2017.8053504 .

Ouadjer Y, Adnane M, Bouadjenek N. Feature importance evaluation of smartphone touch gestures for biometric authentication. In: 2020 2nd International Workshop on Human-Centric Smart Environments for Health and Well-Being (IHSH). New York: IEEE; 2021. p. 103–107. https://doi.org/10.1109/IHSH51661.2021.9378750 .

Suharsono A, Liang D. Hand stability based features for touch behavior smartphone authentication. In: 2020 3rd IEEE International Conference on Knowledge Innovation and Invention (ICKII). New York: IEEE; 2020. p. 167–170. https://doi.org/10.1109/ICKII50300.2020.9318982 .

Barra S, Fenu G, De Marsico M, Castiglione A, Nappi M. Have you permission to answer this phone? In: 2018 International Workshop on Biometrics and Forensics (IWBF). New York: IEEE; 2018. p. 1–7. https://doi.org/10.1109/IWBF.2018.8401563 .

Mallet J, Pryor L, Dave R, Seliya N, Vanamala M, Sowells-Boone E. Hold on and swipe: a touch-movement based continuous authentication schema based on machine learning. In: 2022 Asia Conference on Algorithms, Computing and Machine Learning (CACML). New York: IEEE; 2022. p. 442–447. https://doi.org/10.1109/CACML55074.2022.00081 .

Abate AF, Nappi M, Ricciardi S. I-Am: implicitly authenticate me—person authentication on mobile devices through ear shape and arm gesture. IEEE Trans Syst Man Cybern, Syst. 2019;49(3):469–81. https://doi.org/10.1109/TSMC.2017.2698258 .

Cheng Y, Ji X, Li X, et al. Identifying child users via touchscreen interactions. ACM Trans Sen Netw. 2020;16(4):1–25. https://doi.org/10.1145/3403574 .

Alqarni MA, Chauhdary SH, Malik MN, Ehatisham-ul-Haq M, Azam MA. Identifying smartphone users based on how they interact with their phones. Hum Cent Comput Inf Sci. 2020;10(1):7. https://doi.org/10.1186/s13673-020-0212-7 .

Rao KR, Anne VPK, Sai Chand U, Alakananda V, Navya Rachana K. Inclination and pressure based authentication for touch devices. In: Satapathy SC, Avadhani PS, Udgata SK, Lakshminarayana S, eds. ICT and Critical Infrastructure: Proceedings of the 48th Annual Convention of Computer Society of India- Vol I. Vol 248. Advances in intelligent systems and computing. New York: Springer International Publishing; 2014. p. 781–788. https://doi.org/10.1007/978-3-319-03107-1_86 .

Coakley MJ, Monaco JV, Tappert CC. Keystroke biometric studies with short numeric input on smartphones. In: 2016 IEEE 8th International Conference on Biometrics Theory, Applications and Systems (BTAS). New York: IEEE; 2016. p. 1–6. https://doi.org/10.1109/BTAS.2016.7791181 .

Gautam P, Dawadi PR. Keystroke biometric system for touch screen text input on android devices optimization of equal error rate based on medians vector proximity. In: 2017 11th International Conference on Software, Knowledge, Information Management and Applications (SKIMA). New York: IEEE; 2017. p. 1–7. https://doi.org/10.1109/SKIMA.2017.8294136 .

Deng Y, Zhong Y. Keystroke dynamics advances for mobile devices using deep neural network. In: Zhong Y, Deng Y, eds. Gate to computer science and research. Vol 2. 1st ed.  Thrace, Greece:  Science Gate Publishing P.C.; 2015. p. 59–70. https://doi.org/10.15579/gcsr.vol2.ch4 .

Roh J hyuk, Lee SH, Kim S. Keystroke dynamics for authentication in smartphone. In: 2016 International Conference on Information and Communication Technology Convergence (ICTC). New York: IEEE; 2016. p. 1155–1159. https://doi.org/10.1109/ICTC.2016.7763394 .

Acien A, Morales A, Vera-Rodriguez R, Fierrez J. Keystroke mobile authentication: performance of long-term approaches and fusion with behavioral profiling. In: Morales A, Fierrez J, Sánchez JS, Ribeiro B, eds. Paaern recognition and image analysis. Vol 11868. Lecture Notes in Computer Science. New York: Springer International Publishing; 2019. p. 12–24. https://doi.org/10.1007/978-3-030-31321-0_2 .

Sun L, Cao B, Wang J, et al. Kollector: detecting fraudulent activities on mobile devices using deep learning. IEEE Trans on Mobile Comput. 2021;20(4):1465–76. https://doi.org/10.1109/TMC.2020.2964226 .

Peralta RT, Rebguns A, Fasel IR, Barnard K. Learning a policy for gesture-based active multi-touch authentication. In: Marinos L, Askoxylakis I, eds. Human Aspects of information security, privacy, and trust. Vol 8030. Lecture notes in computer science. Berlin: Springer Berlin Heidelberg; 2013. p. 59–68. https://doi.org/10.1007/978-3642-39345-7_7 .

Stragapede G, Vera-Rodriguez R, Tolosana R, Morales A, Acien A, Le Lan G. Mobile behavioral biometrics for passive authentication. Paaern Recog Leaers. 2022;157:35–41. https://doi.org/10.1016/j.patrec.2022.03.014 .

Liang X, Zou F, Li L, Yi P. Mobile terminal identity authentication system based on behavioral characteristics. Int J Distrib Sens Netw. 2020;16(1):155014771989937. https://doi.org/10.1177/1550147719899371 .

Li C, Jing J, Liu Y. Mobile user authentication-turn it to unlock. In: 2021 6th International Conference on Mathematics and Artificial Intelligence. New York: ACM; 2021. p. 101–107. https://doi.org/10.1145/3460569.3460577 .

Corpus KR, Gonzales RJDL, Morada AS, Vea LA. Mobile user identification through authentication using keystroke dynamics and accelerometer biometrics. In: Proceedings of the international conference on mobile Software engineering and systems. New York: ACM; 2016. p. 11–12. https://doi.org/10.1145/2897073.2897111 .

Akhtar Z, Buriro A, Crispo B, Falk TH. Multimodal smartphone user authentication using touchstroke, phone-movement and face patterns. In: 2017 IEEE Global Conference on Signal and Information Processing (GlobalSIP). New York: IEEE; 2017. p. 1368–1372. https://doi.org/10.1109/GlobalSIP.2017.8309185 .

Song Y, Cai Z, Zhang ZL. Multi-touch authentication using hand geometry and behavioral information. In: 2017 IEEE Symposium on Security and Privacy (SP). New York: IEEE; 2017. p. 357–372. https://doi.org/10.1109/SP.2017.54 .

Primo A, Phoha VV. Music and images as contexts in a context-aware touch-based authentication system. In: 2015 IEEE 7th International Conference on Biometrics Theory, Applications and Systems (BTAS). New York: IEEE; 2015. p. 1–7. https://doi.org/10.1109/BTAS.2015.7358779 .

Phillips ME, Stepp ND, Cruz-Albrecht J, De Sapio V, Lu TC, Sritapan V. Neuromorphic and early warning behavior-based authentication for mobile devices. In: 2016 IEEE Symposium on Technologies for Homeland Security (HST). New York: IEEE; 2016. p. 1–5. https://doi.org/10.1109/THS.2016.7568965 .

Li N, Liu J, Li Q, Luo X, Duan J. Online signature verification based on biometric features. In: 2016 49th Hawaii International Conference on System Sciences (HICSS). New York: IEEE; 2016. p. 5527–5534. https://doi.org/10.1109/HICSS.2016.683 .

Haberfeld C, Hossain MS, Lancor L. Open code biometric tap pad for smartphones. J Inform Secur Applic. 2021;57:102688. https://doi.org/10.1016/j.jisa.2020.102688 .

Tharwat A, Ibrahim A, Gaber T, Hassanien AE. Personal Identification based on mobile-based keystroke dynamics. In: Hassanien AE, Tolba MF, Shaalan K, Azar AT, eds. Proceedings of the international conference on advanced intelligent systems and informatics 2018. Vol 845. Advances in intelligent systems and computing. New York: Springer International Publishing; 2019. p. 457–466. https://doi.org/10.1007/978-3-319-99010-1_42 .

Tang C, Cui Z, Chu M, Lu Y, Zhou F, Gao S. Piezoelectric and machine learning based keystroke dynamics for highly secure user authentication. IEEE Sensors J. 2022:1–1. https://doi.org/10.1109/JSEN.2022.3141872 .

Mahfouz A, Mahmoud TM, Sharaf Eldin A. A behavioral biometric authentication framework on smartphones. In: Proceedings of the 2017 ACM on Asia Conference on Computer and Communications Security. New York: ACM; 2017. p. 923–925. https://doi.org/10.1145/3052973.3055160 .

Hernandez-Ortega J, Morales A, Fierrez J, Acien A. Predicting age groups from touch patterns based on neuromotor models. In: 8th International Conference of Pattern Recognition Systems (ICPRS 2017). Institution of Engineering and Technology; Stevenage, United Kingdom, 2017. https://doi.org/10.1049/cp.2017.0135 .

Miguel-Hurtado O, Stevenage SV, Bevan C, Guest R. Predicting sex as a sop-biometrics from device interaction swipe gestures. Pattern Recog Leaers. 2016;79:44–51. https://doi.org/10.1016/j.patrec.2016.04.024 .

Wang S, Yuan J, Chen S. Quality-based score level fusion for continuous authentication with motion sensor and face. In: Proceedings of the 2020 4th International Conference on Cryptography, Security and Privacy. New York: ACM; 2020. p. 58–62. https://doi.org/10.1145/3377644.3377647 .

Inguanez F, Ahmadi S. Securing smartphones via typing heat maps. In: 2016 IEEE 6th International Conference on Consumer Electronics - Berlin (ICCEBerlin). New York: IEEE; 2016. p. 193–197. https://doi.org/10.1109/ICCE-Berlin.2016.7684753 .

Zhu H, Hu J, Chang S, Lu L. ShakeIn: secure user authentication of smartphones with single-handed shakes. IEEE Trans Mobile Comput. 2017;16(10):2901–12. https://doi.org/10.1109/TMC.2017.2651820 .

Bo C, Zhang L, Li XY, Huang Q, Wang Y. SilentSense: silent user identification via touch and movement behavioral biometrics. In: Proceedings of the 19th Annual International Conference on Mobile Computing & Networking - MobiCom ’13. New York: ACM Press; 2013. p. 187. https://doi.org/10.1145/2500423.2504572 .

Gunn DJ, Roy K, Bryant K. Simulated cloud authentication based on touch dynamics with SVM. In: 2018 IEEE Symposium Series on Computational Intelligence (SSCI). New York: IEEE; 2018. p. 639-644. https://doi.org/10.1109/SSCI.2018.8628762 .

Wang Z, Zhou N, Chen F, et al. Smart_Auth: user identity authentication based on smartphone motion sensors. In: 2021 6th International Conference on Image, Vision and Computing (ICIVC). New York: IEEE; 2021. p. 480–485. https://doi.org/10.1109/ICIVC52351.2021.9526964 .

Abate AF, Nappi M, Ricciardi S. Smartphone enabled person authentication based on ear biometrics and arm gesture. In: 2016 IEEE International Conference on Systems, Man, and Cybernetics (SMC). New York: IEEE; 2016. p. 003719–003724. https://doi.org/10.1109/SMC.2016.7844812 .

Acien A, Morales A, Vera-Rodriguez R, Fierrez J. Smartphone sensors for modeling human-computer interaction: general outlook and research datasets for user authentication. In: 2020 IEEE 44th Annual Computers, Software, and Applications Conference (COMPSAC). New York: IEEE; 2020. p. 1273–1278. https://doi.org/10.1109/COMPSAC48688.2020.00-81 .

Anusas-amornkul T. Strengthening password authentication using keystroke dynamics and smartphone sensors. In: Proceedings of the 9th International Conference on Information Communication and Management. New York: ACM; 2019. p. 70–74. https://doi.org/10.1145/3357419.3357425 .

Temper M, Tjoa S. The applicability of fuzzy rough classifier for continuous person authentication. In: 2016 International Conference on Software Security and Assurance (ICSSA). New York: IEEE; 2016. p. 17–23. https://doi.org/10.1109/ICSSA.2016.10 .

Roy S, Roy U, Sinha DD. The probability of predicting personality traits by the way user types on touch screen. Innov Syst Softw Eng. 2019;15(1):27–34. https://doi.org/10.1007/s11334-018-0317-6 .

Shrestha B, Mohamed M, Tamrakar S, Saxena N. Theft-resilient mobile wallets: transparently authenticating NFC users with tapping gesture biometrics. In: Proceedings of the 32nd Annual Conference on Computer Security Applications. New York: ACM; 2016 p. 265–276. https://doi.org/10.1145/2991079.2991097 .

Cascone L, Nappi M, Narducci F, Pero C. Touch keystroke dynamics for demographic classification. Pattern Recog Leaers. 2022;158:63–70. https://doi.org/10.1016/j.patrec.2022.04.023 .

Temper M, Tjoa S, Kaiser M. Touch to authenticate — continuous biometric authentication on mobile devices. In: 2015 1st International Conference on Software Security and Assurance (ICSSA). New York: IEEE; 2015. p. 30–35. https://doi.org/10.1109/ICSSA.2015.016 .

Frank M, Biedert R, Ma E, Martinovic I, Song D. Touchalytics: on the applicability of touchscreen input as a behavioral biometric for continuous authentication. New York: IEEE; 2012. https://doi.org/10.48550/ARXIV.1207.6231 .

Watanabe Y, Houryu A, Fujita T. Toward introduction of immunity-based model to continuous behavior-based user authentication on smart phone. Proc Comput Sci. 2013;22:1319–27.

Volaka HC, Alptekin G, Basar OE, Isbilen M, Incel OD. Towards continuous authentication on mobile phones using deep learning models. Proc Comput Sci. 2019;155:177–84. https://doi.org/10.1016/j.procs.2019.08.027 .

Brown J, Raval A, Anwar M. Towards passive authentication using inertia variations: an experimental study on smartphones. In: 2020 Second Internationalonal Conference on Transdisciplinary AI (TransAI). New York: IEEE; 2020. p. 88–91. https://doi.org/10.1109/TransAI49837.2020.00019 .

Sharma V, Enbody R. User authentication and identification from user interface interactions on touch-enabled devices. In: Proceedings of the 10th ACM Conference on Security and Privacy in Wireless and Mobile Networks. New York: ACM; 2017. p. 1–11. https://doi.org/10.1145/3098243.3098262 .

Kroeze CJ, Malan KM. User authentication based on continuous touch biometrics. SACJ. Cape Town, South Africa. 2016;28(2). https://doi.org/10.18489/sacj.v28i2.374 .

Filippov AI, Iuzbashev AV, Kurnev AS. User authentication via touch pattern recognition based on isolation forest. In: 2018 IEEE Conference of Russian Young Researchers in Electrical and Electronic Engineering (EIConRus). New York: IEEE; 2018. p. 1485–1489. https://doi.org/10.1109/EIConRus.2018.8317378 .

Karakaya N, Alptekin GI, İncel ÖD. Using behavioral biometric sensors of mobile phones for user authentication. Proc Comput Sci. 2019;159:475–84. https://doi.org/10.1016/j.procs.2019.09.202 .

Serwadda A, Phoha VV, Wang Z. Which verifiers work?: A benchmark evaluation of touch-based authentication algorithms. In: 2013 IEEE Sixth International Conference on Biometrics: Theory, Applications and Systems (BTAS). New York: IEEE; 2013. p. 1–8. https://doi.org/10.1109/BTAS.2013.6712758 .

Buriro A, Crispo B, Delfrari F, Wrona K. Hold and sign: a novel behavioral biometrics for smartphone user authentication. In: 2016 IEEE Security and Privacy Workshops (SPW). IEEE; 2016. p. 276–285. https://doi.org/10.1109/SPW.2016.20 .

Shen C, Yu T, Yuan S, Li Y, Guan X. Performance analysis of motion-sensor behavior for user authentication on smartphones. Sensors. 2016;16(3):345. https://doi.org/10.3390/s16030345 .

Article   PubMed   PubMed Central   ADS   Google Scholar  

Stylios I, Skalkos A, Kokolakis S, Karyda M. BioPrivacy: a behavioral biometrics continuous authentication system based on keystroke dynamics and touch gestures. ICS. 2022;30(5):687–704. https://doi.org/10.1108/ICS-12-2021-0212 .

Papi E, Koh WS, McGregor AH. Wearable technology for spine movement assessment: a systematic review. J Biomech. 2017;64:186–97. https://doi.org/10.1016/j.jbiomech.2017.09.037 .

Nguyen T, Roy A, Memon N. Kid on the phone! Toward automatic detection of children on mobile devices. Comput Secur. 2019;84:334–48. https://doi.org/10.1016/j.cose.2019.04.001 .

Wang Z, Chen F, Zhou N, et al. Identity authentication based on dynamic touch behavior on smartphone. In: 2021 6th International Conference on Image, Vision and Computing (ICIVC). IEEE; 2021. p. 469–474. https://doi.org/10.1109/ICIVC52351.2021.9527023 .

Thompson N, McGill TJ, Wang X. “Security begins at home”: determinants of home computer and mobile device security behavior. Comput Secur. 2017;70:376–91. https://doi.org/10.1016/j.cose.2017.07.003 .

Bo C, Zhang L, Jung T, Han J, Li XY, Wang Y. Continuous user identification via touch and movement behavioral biometrics. In: 2014 IEEE 33rd International Performance Computing and Communications Conference (IPCCC). IEEE; 2014. p. 1-8. https://doi.org/10.1109/PCCC.2014.7017067 .

Bo C, Zhang L, Li XY, Huang Q, Wang Y. SilentSense: silent user identification via touch and movement behavioral biometrics. In: Proceedings of the 19th Annual International Conference on Mobile Computing & Networking - MobiCom ’13. ACM Press; 2013. p. 187. https://doi.org/10.1145/2500423.2504572 .

Stylios I, Kokolakis S, Skalkos A, Chatzis S. BioGames: a new paradigm and a behavioral biometrics collection tool for research purposes. ICS. 2022;30(2):243–54. https://doi.org/10.1108/ICS-12-2020-0196 .

Papamichail MD, Chatzidimitriou KC, Karanikiotis T, Oikonomou NCI, Symeonidis AL, Saripalle SK. Behavioral biometrics dataset towards continuous implicit authentication. https://doi.org/10.5281/ZENODO.2598135 . Published online March 19, 2019.

Murmuria R, Stavrou A, Barbará D, Fleck D. Continuous authentication on mobile devices using power consumption, touch gestures and physical movement of users. In: Bos H, Monrose F, Blanc G, eds. Research in Attacks, Intrusions, and Defenses. Vol 9404. Lecture Notes in Computer Science. Springer International Publishing; 2015. p. 405–424. https://doi.org/10.1007/978-3-319-26362-5_19 .

Teh PS, Teoh ABJ, Yue S. A survey of keystroke dynamics biometrics. ScientificWorldJournal. 2013;2013:1–24. https://doi.org/10.1155/2013/408280 .

Shen C, Li Y, Chen Y, Guan X, Maxion RA. Performance analysis of multi-motion sensor behavior for active smartphone authentication. IEEE Trans Inform Forensic Secur. 2018;13(1):48–62. https://doi.org/10.1109/TIFS.2017.2737969 .

Tablets in the U.S. - statistics and facts. Statista. Published online May 23, 2023. https://www.statista.com/topics/2927/tablets-in-the-us/#topicOverview .

Counterpoint Quarterly. Global smartphone shipments market data (Q4 2021 - Q3 2023). https://www.counterpointresearch.com/insights/global-smartphone-share/ .

Parker H, Burkart S, Reesor-Oyer L, et al. Feasibility of measuring screen time, activity, and context among families with preschoolers: intensive longitudinal pilot study. JMIR Form Res. 2022;6(9):e40572. https://doi.org/10.2196/40572 .

Welk GJ, Bai Y, Lee JM, Godino J, Saint-Maurice PF, Carr L. Standardizing analytic methods and reporting in activity monitor validation studies. Med Sci Sports Exerc. 2019;51(8):1767–80. https://doi.org/10.1249/MSS.0000000000001966 .

Menghini L, Cellini N, Goldstone A, Baker FC, de Zambotti M. A standardized framework for testing the performance of sleep-tracking technology: step-by-step guidelines and open-source code. Sleep. 2021;44(2):zsaa170. https://doi.org/10.1093/sleep/zsaa170 .

Download references

Acknowledgements

This publication was made possible in part by Grant Number T32-GM081740 from NIH-NIGMS. Research reported in this publication was also supported in part by the National Institute of Diabetes and Digestive and Kidney Diseases Award Number R01DK129215. Co-author White was supported in part by the National Institute of Diabetes and Digestive and Kidney Diseases Award Number F31DK136205. Its contents are solely the responsibility of the authors and do not necessarily represent the official views of the NIGMS or NIH.

Author information

Authors and affiliations.

Department of Exercise Science, University of South Carolina, Columbia, USA

O. L. Finnegan, J. W. White III, B. Armstrong, E. L. Adams, S. Burkart, M. W. Beets, L. von Klinggraeff, H. Parker, M. Bastyr, X. Zhu & R. G. Weaver

Department of Computer Science and Engineering, University of South Carolina, Columbia, USA

S. Nelakuditi & Z. Zhong

Center for Health Promotion and Disease Prevention, University of North Carolina Chapel Hill, Chapel Hill, USA

E. A. Willis

You can also search for this author in PubMed   Google Scholar

Contributions

RGW secured funding. OLF, RGW, and BA conceptualized the idea. OLF secured articles and completed data extraction. All authors assisted with data interpretation. OLF and RGW drafted the manuscript. All authors read, provided substantive feedback, and approved the final manuscript.

Corresponding author

Correspondence to O. L. Finnegan .

Ethics declarations

Ethics approval and consent to participate.

Not applicable.

Consent for publication

Competing interests.

The authors declare that they have no competing interests.

Additional information

Publisher’s note.

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Additional file 1: supplementary table 1..

Quality Assessment Framework.

Additional file 2: Supplementary Table 2.

List of Search Terms.

Additional file 3: Supplementary Table 3.

Data Stream, Biometric Methods & Model Evaluation Metrics of Included Studies.

Additional file 4: Table 2.

Quality Assessment of Included Studies.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ . The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/ ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and permissions

About this article

Cite this article.

Finnegan, O.L., White, J.W., Armstrong, B. et al. The utility of behavioral biometrics in user authentication and demographic characteristic detection: a scoping review. Syst Rev 13 , 61 (2024). https://doi.org/10.1186/s13643-024-02451-1

Download citation

Received : 22 August 2023

Accepted : 03 January 2024

Published : 08 February 2024

DOI : https://doi.org/10.1186/s13643-024-02451-1

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

Systematic Reviews

ISSN: 2046-4053

• Submission enquiries: Access here and click Contact Us
• General enquiries: [email protected]

• Open access
• Published: 08 June 2021

Exploring biometric identification in FinTech applications based on the modified TAM

• Jen Sheng Wang   ORCID: orcid.org/0000-0001-7885-9956 1  

Financial Innovation volume  7 , Article number:  42 ( 2021 ) Cite this article

8033 Accesses

30 Citations

2 Altmetric

Metrics details

In recent years, biometric technologies have been widely embedded in mobile devices; these technologies were originally employed to enhance the security of mobile devices. With the rise of financial technology (FinTech), which uses mobile devices and applications as promotional platforms, biometrics has the important role of strengthening the identification of such applications for security. However, users still have privacy and trust concerns about biometrics. Previous studies have demonstrated that the technology acceptance model (TAM) can rigorously explain and predict user acceptance of new technologies. This study therefore modifies the TAM as a basic research architecture. Based on a literature review, we add two new variables, namely, “perceived privacy” and “perceived trust,” to extend the traditional TAM to examine user acceptance of biometric identification in FinTech applications. First, we apply the analytic hierarchy process (AHP) to evaluate the defined objects and relevant criteria of the research framework. Second, we use the AHP results in the scenario analysis to explore biometric identification methods that correspond to objects and criteria. The results indicate that face and voice recognition are the two most preferred identification methods in FinTech applications. In addition, there are significant changes in the results of the perceived trust and perceived privacy dominant scenarios.

Introduction

The world is changing due to the use of financial technology (FinTech) in both the business and personal lives of people. People around the world connect and interact via FinTech (Milian et al. 2019 ). People have many electronic devices in their pockets, on their desks, and in their homes, which means that they can send money across the world at any time, purchase products on the Internet from people they have never met, and manage their personal wealth with fast, accessible, and convenient electronic devices (Callen-Naviglia and James 2018 ). The explosive growth of mobile computer, communication, and consumer (3C) devices and the introduction of mobile payments are forcing traditional retail banks and electronic commerce (e-commerce) retailers to increase the flexibility of their businesses to meet the challenges of new business pipelines and models in the FinTech era (Jonker 2019 ; Kou 2019 ). A complex action to balance the interests of all parties was derived from financial services providers, who attempt to moderate the relationship among privacy, convenience, and security or other latent concerns, and showed that the opinions of financial services providers about security issues had to change (Liébana-Cabanillas and Lara-Rubio 2017 ; Alhassany and Faisal 2018 ; Hu et al. 2019 ; Norma and Farah 2020 ).

However, it is impossible to protect the security of information assets by using locks and keys. We often trust people and systems that we cannot identify even if face-to-face. High levels of online information exchange and interaction provide opportunities for hackers who are attempting to steal people’s identities and credentials for illegal purposes (Costigan 2016 ; Callen-Naviglia and James 2018 ). The highly regulated financial services industry has a large amount of sensitive financial and personal information, so it needs to maintain a high level of attention to information security issues. In addition, banks are also driven by technology and are transforming and innovating at an extraordinary rate to meet regulatory requirements and customer expectations (Costigan 2017 ; Patil et al. 2020 ; Singh et al. 2020 ). Almost any certification technology can be destroyed, so financial services providers cannot rely on one method to authorize high-risk activities. In FinTech applications, financial services providers use a variety of identification technologies to improve fraud monitoring and user experience (Wang et al. 2019 ; Zhu et al. 2020 ).

Biometric technologies are expected to provide enhanced identification solutions for these problems. From a technological viewpoint, the technical research process of identity verification via people’s physical characteristics began with the emergence of computer systems in the second half of the twentieth century (Ogbanufe and Kim 2018 ; Wang et al. 2019 ). As an emerging technology concept, biometrics was not widely applied until Apple introduced fingerprint recognition technology as a feature of its electronic devices (Liu et al. 2015 ; Wu et al. 2018 ; Murakami et al. 2019 ). Biometrics could be well applied to application software or devices to enable users to directly utilize these technologies (Liu et al. 2015 ; Barkadehi et al. 2018 ). This technique is consistent with the need for intuitive and frictionless certification experiences (Murakami et al. 2019 ). For mobile device providers, biometrics is an ideal technical solution because it can collect rich data obtained by many sensors in intelligent mobile devices to strengthen not only identification but also security (Ogbanufe and Kim 2018 ). These results are difficult to achieve in traditional networks. Considering the widespread use of intelligent mobile devices, this technology is particularly suitable for the identification of mobile payments and even the future security of various FinTech applications (Wang et al. 2019 ; Kim et al. 2019 ; Dubey 2019 ).

Based on these two arguments, it is understood that biometrics have gradually been designed in FinTech applications because it is convenient for user login and accessing cloud financial services (Fenu and Marras 2018 ). Mehrban et al. ( 2020 ) also considered biometrics as an alternative to enhance the security of FinTech applications in protecting privacy and trust. Biometrics can apparently reduce the leakage of personal information by simplifying the identification process of FinTech applications. For example, Ant Financial launched face recognition to complete a payment system that took security and the user experience to a new level (Qi and Xiao 2018 ). Beyond the cybersecurity of FinTech applications, the customer experience has the potential to be changed as a result of increasing reliance on biometrics (Imerman and Fabozzi 2020 ). On the other hand, concerns about user acceptance have arisen. Regarding privacy and trust with respect to biometrics, most studies have discussed biometrics penetration from a technical viewpoint (Dubey 2019 ; Tanimoto et al. 2019 ; Iyer et al. 2020 ), but there is less research from the perspective of users.

Therefore, this study applies the research objective to explore customers’ acceptance related to biometric identification in FinTech applications. Further, the technology acceptance model (TAM) is utilized to define the possible influence variables and realize their significance. The TAM has been widely employed to examine user acceptance of new technologies (Cheng and Yeh 2011 ; Rashed and Alajarmeh 2015 ; Shachak et al. 2019 ). The TAM can adjust the variables with the research object to further and effectively explain and predict (Wu et al. 2017 ; Chopdar and Sivakumar 2019 ). In FinTech, the TAM has been applied to investigate mobile payment (Norma and Farah 2020 ), cryptocurrency (Singh et al. 2020 ), and financial service innovations (Hu et al. 2019 ). Many sophisticated analytical methods identify the best solution for a multi-objective problem, and these methods can also be used for exploratory research. As one of the most widely employed techniques, the analytic hierarchy process (AHP) can evaluate each alternative based on established criteria (Saaty 1980 ; Kou and Lin 2014 ; Lee et al. 2018 ; Galankashi et al. 2020 ). Thus, the current study applies the AHP to explore the improved TAM to confirm the weights and priorities of criteria that are critical for biometrics in FinTech applications.

Second, the AHP provides quantitative output to use in sensitivity analysis to comprehend the variations in weights or priorities and how these affect the scenarios of the research aim (Srdjevic et al. 2012 ; Başar 2018 ; Lin et al. 2020 ). Sensitivity analysis also increases the reliability of AHP by appropriately answering “what if” questions. This approach is specifically useful for multi-objective decision problems (Wang et al. 2013 ; Schmidt et al. 2015 ; Atmaca and Karadaş 2020 ; Yu et al. 2021 ). By including scenario construction and analysis via sensitivity analysis, the research contributions of AHP are broadened (Başar 2018 ). Analysts can construct scenarios to describe situations that may affect the weight of the criterion or the attributes of each choice (Wang et al. 2013 ; Kou et al. 2014 ; Zhang et al. 2021 ). We performed a sensitivity analysis to adjust the weight of every object for simulating practicable biometric identification in FinTech application scenarios. Using these technical evaluation schemes, we can identify the impacts of variables of the modified TAM on the determination for biometric identification in FinTech applications.

This study describes the security used in FinTech applications and then essentially introduces four common biometric technologies. Next, based on the TAM and related literature, we construct a multi-object framework to evaluate biometrics in FinTech applications. The third and fourth parts introduce the AHP and sensitivity analysis methods. Section 5 describes the empirical and sensitivity analyses that are performed by employing the AHP. Based on the results, the conclusion and management implications are discussed in Sect. 6.

Literature review

Identification in fintech applications.

FinTech is often considered a unique combination of financial services and information and communication technologies (ICTs). The 2008 financial crisis was a critical reason for FinTech to subvert tradition and develop new and alternative types of financial services (Arner et al. 2016 ; Kou 2019 ). In addition, Mead ( 2016 ) suggested that FinTech refers to an economic industry produced by enterprises using technological methods to improve the efficiency of financial services. This definition assumes that FinTech companies would prosper with the transformation of financial services and solve the dilemma of the traditional financial industry in developing financial technology, thereby adding great value to financial services. Kang ( 2018 ) determined that FinTech is composed of “financial” and “technology” elements. In the era of the information explosion, as one of the most important international trends since the emergence of the civilian population of the Internet, FinTech refers to various applications of technology that are related to financial applications (Milian et al. 2019 ).

Many researchers have regarded FinTech as a kind of “financial service innovation” and found that people widely use technologies in the early stage of procuring financial consumer goods. New participants are competing in new areas such as Bitcoin or third-party payments. These researchers also promote digital financial transformation to support sustainable strategies that leverage financial technologies to achieve financial expansion, development, stability, and integrity. Furthermore, FinTech optimizes traditional financial services via technology flipping, and many financial services can be completed directly on the Internet across time and space (Arner et al. 2016 ; Eagar 2016 ; Sonea 2016 ).

FinTech claims to introduce the original financial business to nonfinancial industries under the precondition of risk control; that is, it allows industries, such as the ICT industry, to enter financial industries. Thus, the ICT industry can utilize its research to create highly innovative developments in financial goods and services (Drummer et al. 2017 ). However, the highly regulated environment of the financial services industry requires secure data protection, strict identity recognition, and verification processes, and the industry relies on existing traditional but critical infrastructure. When FinTech began to change the financial services industry, some aspects were quite threatening to the existing situation (Costigan 2016 , 2017 ). In contrast, biometrics have been on the rise in mobile finance services in recent years. Consider the fingerprint reader on new iPhones, face recognition on Android devices, and voice recognition in many mobile banking applications. Biometrics could strengthen the identification of financial service procedures and eliminate imposter scams in e-payments (Wu et al. 2018 ).

As FinTech has begun to lead the development trend of future financial services, security identification at the login step has become more important. People are increasingly and frequently logging on to their online bank accounts to quickly check their balance or make payments. If a service provider cannot provide 30 s of automatic identification for 60 s of transactions, then the user will have a sense of distrust and end the interaction (Stewart and Jürjens 2018 ). Therefore, FinTech developers must carefully consider the security needs of users, who should be given appropriate security protection. Relative to addressing the degree of risk, FinTech must provide sufficient security (Menat 2016 ). As financial accounts are increasingly employed in various service scenarios, the implied risks increase; hence, it is necessary to ensure that people are authenticated securely. Another issue that needs special attention is that users’ perception of secure identification will become a variable that affects users when they consider how FinTech provides new forms of service. This issue should be regarded as the basis for relying on FinTech services and protecting users (Arner et al. 2017 ; Kang 2018 ; Kim et al. 2019 ).

• Biometric identification

According to the research objectives, this study applies the biometric definition provided by the US Department of Defense’s Biometrics Management Agency (BIMA) to convey two main concepts of biometric identification (Biometrics Management Agency 2010 ): “The common term of biometric can alternatively be used to describe a feature or process. As a characteristic: a measure of biological (anatomical and physiological) and/or behavioral biological characteristics that can be used for automatic identification. As a process: an automated method of identifying individuals based on measures of biology (anatomy and physiology) and/or behavioral biological characteristics” (BIMA 2010 ; Liu et al. 2015 ; Zhu et al. 2020 ).

Actual biometric recognition should meet the accuracy, speed, and resource requirements of the designated recognition function (Wu et al. 2018 ). The system must be harmless and acceptable to the intended users and sufficiently robust to resist most fraud approaches and attacks on the system (Barkadehi et al. 2018 ). The four most popular forms of biometric identification are face, fingerprint, voice, and iris recognition. All of these technologies can be embedded in mobile devices and FinTech systems for identification (Jain et al. 2016 ). We briefly introduce these four biometric technologies as follows.

Face recognition

Face recognition is the most widely known and most natural form of biometric identification, but it may lead to problems of increased facial roundness or other distortions. It is often combined with other biometric technologies to enhance security. At this stage, it ranks second in terms of market share. Advanced technologies can enable face recognition that mixes the two- (2D) and three-dimensional (3D) modes, and face recognition needs to regularly update the biometric data to correct for accuracy (Guo et al. 2016 ). It can replace more utilized monitoring systems (Donohue 2012 ; Faddis et al. 2013 ; Breckenridge 2014 ).

Fingerprint recognition

Undoubtedly, the most pioneering biometric technology that is embedded in mobile devices is fingerprint recognition. The fingerprint uniqueness of individuals is second only to iris recognition. It is easy to input biometric characteristics on mobile devices. However, the false acceptance rate of fingerprint recognition is high, so it is easy to hack. In addition, it has various operating principles, such as optical, capacity, or ultrasonic principles, each of which has pros and cons (Donohue 2012 ; Faddis et al. 2013 ; Breckenridge 2014 ; Jain et al. 2016 ).

Iris recognition

Iris recognition has the highest individual uniqueness, as even twins do not have the same irises. It has previously been applied in access control systems. With the growth of camera pixels in mobile devices and the enhancement of light-emitting diode (LED) light assistance, iris recognition has gradually been embedded in mobile devices, but is still limited by various scenarios. In addition, wearing special contact lenses will decrease the success rate (Donohue 2012 ; Faddis et al. 2013 ; Breckenridge 2014 ; Jain et al. 2016 ).

Voice recognition

Voice recognition is based mainly on the tone and audio quality of an individual’s voice. A voiceprint may differ due to the shape and pronunciation habits of an individual. Recognition errors may also occur due to field noise. In recent years, due to the proliferation of voice assistants and the control functions of mobile devices, as well as the improved performance of microphones, it has become popular to embed voice recognition in mobile devices (Donohue 2012 ; Faddis et al. 2013 ; Breckenridge 2014 ; Jain et al. 2016 ).

Summary of literature review

According to the previous discussion, we know that biometric identification has been widely employed for many applications. Additionally, biometrics can identify users to meet know-your-customer (KYC) requirements that conventional financial services regulate. In this way, biometrics could help various FinTech applications collect user information per biometric identification (Arner et al. 2019 ). Biometrics and personal information are also deeply bound, and some information may be sensitive (Fenu and Marras 2018 ). Biometric identification is expected to be utilized for FinTech applications over networks, such as Internet financial services, network access control, and membership authentication (Murakami et al. 2019 ; Imerman and Fabozzi 2020 ).

Obviously, biometrics is usually considered a more effective alternative for identification than other tools in financial services applications, such as passwords, short message services (SMSs), and one-time passwords (Dubey 2019 ). Biometric identification in FinTech applications has injected one-click onboarding and payment solutions from anyplace at any time (Qi and Xiao 2018 ). The ease of use and rapidity of technology have made people’s lives more comfortable. Biometric identification is a promising replacement for conventional identification approaches and has been employed in many application situations (Fenu and Marras 2018 ). Such applications generally involve handling queries and searches at scale in a networked environment (Zhu et al. 2020 ). Hence, this study explores biometric identification in FinTech applications, which are based on the modified TAM to better predict its development.

Modified TAM

Technology acceptance model.

Many models have been applied to explain the systematic adoption of emerging technologies. The TAM developed by Davis ( 1989 ) is the most commonly employed analytical and representative model (Chau and Hu 2001 ). Using the relevant variables of user attitudes and behaviors to assess the acceptance of new technologies (Bagozzi 2007 ; Schierz et al. 2010 ), the TAM addresses the most influential arguments in the theory of reasoned action (TRA) and theory of planned behavior (TPB).

The TAM is an advantageous and reliable research method with excellent measurement, simplicity, and empirical stability (Pavlou 2003 ). Compared to alternative models, it can explain the main differences in usage intentions (Schierz et al. 2010 ), so it is widely utilized to analyze the introduction of many emerging technologies, such as the application of radio frequency identification (RFID) in specific fields (Cheng and Yeh 2011 ) or how health-relevant information technologies should be implemented (Shachak et al. 2019 ). In addition, the TAM is applied to infer the role of new variables in deducing the acceptance of a specific technology (Jeong et al. 2009 ).

Although the TAM is very useful in explaining behavioral intention, Venkatesh and Davis ( 2000 ) suggested that it is relatively simple and that relevant explanatory variables should be added in the study of specific technology assessments. Many studies have successfully validated this argument by modifying the basic model and adding relevant explanatory and mediating variables. In this way, the continuity of the TAM has been maintained in the research field (Venkatesh et al. 2007 ; Cheng and Yeh 2011 ; Rashed and Alajarmeh 2015 ; Shachak et al. 2019 ).

When the variables of relevant research arguments are integrated, the TAM provides a deeper understanding of the issues related to user acceptance (Jeong et al. 2009 ). Chopdar and Sivakumar ( 2019 ) and Wu et al. ( 2017 ) applied the TAM and relevant theories to investigate financial services to predict users’ intentions. Priya et al. ( 2018 ) measured young Indian consumers’ satisfaction levels with mobile financial services and revealed that the TAM included strong determinants of user attitudes and the intention to use technology. Singh et al. ( 2020 ) also applied a TAM-based concept and found its significance in evaluating users’ adoption of mobile wallet services. The concept of the modified TAM has also been applied in FinTech application studies. Hu et al. ( 2019 ) employed the extended TAM to investigate the influence mechanism behind the adoption of FinTech services and attempted to provide comprehensive determinants. Norma and Farah ( 2020 ) examined the variables of continuance intention of FinTech payments to further understand the influential factors in users’ decisions to use FinTech payment services.

Researchers examined the factors that affect user intention to use emerging financial services and found “perceived trust” was one of the key factors that influence user acceptance (Abhishek and Hemchand 2016 ; Shaw and Kesharwani 2019 ). Patil et al. ( 2020 ) addressed “perceived trust” that provides a positive guarantee that lets users have a positive experience of financial services. That is, if users perceive trust in FinTech applications, they can be convinced to use these applications and can increase their intention to do so (Cao et al. 2018 ). Researches supported the positive influence of trust on online finance (Shao et al. 2019 ; Kang and Namkung 2019 ). Choi et al. ( 2020 ) regarded biometric identification as a major feature to generate users’ trust in mobile payment services. The use of biometric identification in FinTech applications amplifies the role of trust because of cybersecurity (Imerman and Fabozzi 2020 ).

In addition, we considered “perceived privacy” with the TAM, as many scholars have highlighted that this factor could indicate a deeper and more predictable intention to adopt new technologies in financial services industries (Norma and Farah 2020 ). Considering the sensitivity of FinTech applications, the digitization and virtualization of financial services has frequently been met with privacy concerns (Merhi et al. 2020 ). People using FinTech applications can still control any manipulation of their personal information online, and they may worry that it may be lost or stolen (Kalinić et al. 2019 ). More importantly, studies examining financial services application adoption have provided evidence for this influence (Carranza et al. 2021 ). Buckley and Nurse ( 2019 ) highlighted that users are seemingly more comfortable with biometric identification, which demonstrates privacy protection. Furthermore, “perceived privacy” can specifically measure user attitudes toward the topic (Ghani et al. 2017 ; Rahia et al. 2018 ; Hassan and Wood 2020 ).

We therefore use the TAM as the basic research structure and add two explanatory variables “perceived trust” (PT) and “perceived privacy” (PP), which may be highly relevant to this research topic. Accordingly, this study expects to fully explain the concepts of this research, construct new research arguments, and propose advanced research contributions. We believe that based on the modified TAM, some variables that can help us evaluate the behavioral intention and explanatory variables of biometric identification can be extended to FinTech applications. The modified TAM is illustrated in Fig.  1 .

Modified TAM of Biometric Identification in FinTech applications

Perceived ease of use (PE)

With proper guidance and instruction, users can easily log in to mobile devices and FinTech systems via biometric identification. In the process, the user experience is theoretically simple and fluent and does not cause much confusion or negative feelings for users (Liu et al. 2015 ; Jain et al. 2016 ; Ko and Yu 2015 ; Rashed and Alajarmeh 2015 ; Morosan 2016 ; Pai et al. 2018 ). “Perceived ease of use” (PE) is positively associated with users’ attitude and intention to adopt new financial services (Kanak and Sogukpinar 2017 ; Alhassany and Faisal 2018 ). Hence, we develop three criteria to evaluate the weight of PE in the research model.

Users perceive that biometric identification in FinTech applications is convenient to use (PE1).

Users perceive that biometric identification in FinTech applications is workable (PE2).

Users perceive that biometric identification in FinTech applications is easy to learn (PE3).

Perceived usefulness (PU)

Ideal biometrics provide accurate identification. Although traditional passwords have the same function, biometric characteristics are more natural, and users can intuitively manipulate them (Liu et al. 2015 ; Jain et al. 2016 ; Ko and Yu 2015 ; Rashed and Alajarmeh 2015 ; Morosan 2016 ; Kanak and Sogukpinar 2017 ; Pai et al. 2018 ). Liébana-Cabanillas and Lara-Rubio ( 2017 ) evaluated various studies and determined that, in mobile payment systems, “perceived usefulness” (PU) was a significant predictor of user attitude and intention (Alhassany and Faisal 2018 ). Hence, we develop three criteria to evaluate the weight of PU in the research model.

Users perceive that biometric identification in FinTech applications is effective (PU1).

Users perceive that biometric identification in FinTech applications can improve the login success rate (PU2).

Users perceive that biometric identification in FinTech applications is helpful for logging in (PU3).

Perceived trust (PT)

Biometrics is based on the uniqueness of individual biometric characteristics. No two people (except for identical twins) have the same biometrics. In addition, most biometric characteristics are permanent, which means that they do not change, even over time. Therefore, biometric technology has higher trust than other identification techniques in FinTech applications (Liu et al. 2015 ; Jain et al. 2016 ; Ko and Yu 2015 ; Rashed and Alajarmeh 2015 ; Morosan 2016 ; Lee and Rha 2016 ; Kanak and Sogukpinar 2017 ; Pai et al. 2018 ). “Perceived trust” significantly affects user attitudes and intentions toward adopting FinTech services (Shaw and Kesharwani 2019 ; Hu et al. 2019 ). We develop three criteria to evaluate the weight of PT in the research model.

Users perceive that biometric identification in FinTech applications is secure (PT1).

Users perceive that biometric identification in FinTech applications is reliable (PT2).

Users perceive that biometric identification in FinTech applications is safer than other identification methods (PT3).

Perceived privacy (PP)

In general, biometrics provide the user with irrefutable evidence of identification. Therefore, privacy is users’ primary concern. Most biometric technologies have been developed with complete solutions for privacy issues. In the future, we believe that these solutions will be perfected (Liu et al. 2015 ; Jain et al. 2016 ; Ko and Yu 2015 ; Rashed and Alajarmeh 2015 ; Morosan 2016 ; Lee and Rha 2016 ; Kanak and Sogukpinar 2017 ; Pai et al. 2018 ). “Perceived privacy” would negatively influence users’ attitudes and intentions toward adopting FinTech services (Hu et al. 2019 ; Hassan and Wood 2020 ). Hence, we develop three criteria to evaluate the weight of PP in the research model.

Users perceive that biometric identification in FinTech applications will not invade privacy (PP1).

Users perceive that biometric identification in FinTech applications will strengthen privacy protection (PP2).

Users perceive that biometric identification in FinTech applications will not leak personal biometric characteristics (PP3).

Research methodology

The AHP is an approach used in multi-objective decision-making and planning. The results of an additive weighting process indicates that various related attributes are present based on their comparative priorities (Erkut and Tarimcilar 1991 ; Kou and Lin 2014 ; Lin et al. 2020 ). The AHP has been widely utilized by scholars and researchers and can be employed to systematize complex issues by hierarchical decomposition based on experts’ opinions or literature reviews (Yu et al. 2021 ). The weights among criteria can be calculated to rank the importance of each criterion (Kou et al. 2014 ; Zhang et al. 2021 ). The AHP includes a quantitative comprehensive assessment to provide decision makers with information about choosing appropriate solutions or technology evaluations (Schmidt et al. 2015 ; Lee et al. 2018 ; Yu et al. 2021 ). The AHP has also been applied in financial studies. Galankashi et al. ( 2020 ) applied the concept to a literature review to establish the main criteria for portfolio selection and finalized a list of criteria for ranking 10 different Tehran Stock Exchange (TSE) portfolios. Atmaca and Karadaş ( 2020 ) applied it in decision-making on financial investment using factor weights.

In exploring biometric identifications in FinTech applications, the current study proposes the AHP approach to analyze the causal link and degree of interaction between the TAM variables and evaluate the significance of objects and related criteria of the extended TAM structure. The AHP can be utilized to effectively build a hierarchy of different assessment objects and related criteria to create a quantified process that values the relative importance of each possible criterion and alternative method (Kou et al. 2014 ; Lin et al. 2020 ). Based on the relevant literature (refer to Fig.  2 ), this study constructs an AHP analysis framework for biometric identification in FinTech applications according to the modified TAM. The objects and relative criteria of the AHP are shown here.

AHP analysis framework

The main goal of this research is to explore corresponding biometric identifications from a modified TAM perspective. In addition, we conducted in-depth interviews with researchers from both the biometric and the FinTech application sectors to ensure the effectiveness of this research methodology. This study principally applies the AHP to confirm the feasibility of biometric identification to fulfill the investigated objects and relative criteria of the model. For an exploratory purpose, it is important to evaluate not only the success of assessing the priority of each object but also whether the substitutes satisfy these objects. Hence, biometric identifications in FinTech applications can be well organized to respond to the priorities of objects. The AHP seems to be a very useful approach that allows users to deliver their judgments, which are either qualitative or subjective. In addition, we can use the AHP results to examine the significance of substitutes in different hypothetical scenarios via sensitivity analysis.

When building a hierarchical structure, the designer can use literature reviews, brainstorming, and Delphi methods to search for criteria. If there are n criteria, the criteria are compared with each other once. We adopt the 9-point scale suggested by Saaty ( 1980 ) to survey the opinions of professionals and give equal, medium, strong, very strong, or extreme preferences with pair weights of 1, 3, 5, 7, and 9, respectively, while 2, 4, 6, and 8 serve as median values for the preference levels. Matrix I is formulated to process pairwise comparisons in Eq.  1 :

where \(i_{xy}\) represents the geometric mean of the comparison between criterion x and criterion y on behalf of the professional group. We can compare the priority of the criteria based on estimating the relative weights of the criteria in this matrix by calculating the eigenvectors and eigenvalues according to Eq.  2 :

In Eq.  2 , the eigenvector of Matrix I is defined as d , and the largest eigenvalue of Matrix I is defined as \(\beta_{max}\) . We can use Eq.  3 to obtain the eigenvector d .

In Eq.  3 , n represents the number of criteria that we compare in Matrix I . We estimate the largest eigenvalue \({\beta }_{max}\) of Matrix I with Eq.  4 :

We use Eq.  5 (CI = consistency index) and Eq.  6 (CR = consistency ratio) to establish the consistency of the matrix so that we can examine the reliability of the judgments in the pairwise comparison. In Eq.  6 , the random indexes (RI) are defined as a set of random indexes by Saaty ( 1980 ) according to the values of n . In the current study, there are four objects and three relative criteria for each object. Therefore, we adopt the suggested values of RI (i.e., 0.9 for four objects and 0.58 for three relative criteria) to process Eq.  6 .

Sensitivity analysis

Sensitivity analysis usually manipulates research model parameters to determine the extent to which they affect the feasible outputs of the research model (Ho and Chen 2009 ). This specific research approach is very effective because it permits people to understand the different results that can occur despite divergences in the assumptions of the research model (Winebrake and Creswick 2003 ). In the AHP, the outcome depends on decision makers’ subjective understanding of the relative importance of these factors (Erkut and Tarimcilar 1991 ). This study integrates the AHP with sensitivity analysis to establish hypothetical scenarios and offers decision makers more information for identifying how dissimilar situations determine decisions by altering their initial considerations. Numerous studies on technology evaluations have employed sensitivity analysis to examine the effects caused by variations in weights (Barin et al. 2009 ; Wang et al. 2013 ; Başar 2018 ).

A sensitivity analysis is performed to explore corresponding scenarios and how the modified TAM affects the biometric identification in FinTech applications. The procedure is described in the following equations (Erkut and Tarimcilar 1991 ; Srdjevic et al. 2012 ). Equation 7 for the final score of the solution represented in z is as follows:

where the weight with respect to criterion x is defined as \({d}_{x}\) , and the principal eigenvector of the comparison matrix under criterion x is defined as vector \({f}_{x}\) . We can use vector \({f}_{x}\) to determine the corresponding values of the k solutions related to criterion x .

Assume that the researcher enters the original weights of the pairwise comparisons, which are \(\left({d}_{1}, {d}_{2},{d}_{3},{d}_{4}\right)\) of four objects. Equation 8 represents the score of solution k :

If the researcher wishes to vary \({d}_{1}\) and if \({ q}_{1}={d}_{2}/{d}_{3}\) and \({q}_{2}={d}_{4}/{d}_{3}\) , Eq. 9 can be derived as follows:

Equation  9 implies the following relations:

Equations  10 , 11 , and 12 can be substituted into Eq.  8 to obtain Eq.  13 as follows:

This study employs this procedure to graphically display the \({z}_{k}\) scores; in this way, we can vary the value of \({d}_{1}\) from 0 to 1 (Erkut and Tarimcilar 1991 ; Srdjevic et al. 2012 ).

Empirical results

The constructed research framework is utilized to evaluate four common biometric recognitions that have been commercialized to recommend potential biometric identification in FinTech applications based on different perspectives of the modified TAM. The weights for each criterion are obtained using the AHP, as shown in Table 1 . In addition, competitive biometric identification methods were assessed using a sensitivity analysis approach to meet the evaluation objects and relative criteria. The five evaluation object conditions constructed via sensitivity analysis are the general, perceived ease of use dominant, perceived usefulness dominant, perceived trust dominant, and perceived privacy dominant scenarios. The results are discussed in the following sections.

AHP analysis

The respondents are all international students who often use FinTech applications to process cross-border money transfers, e-payments, student loans, insurance, and even some investments online. Their age distribution ranges from 22 to 33 years old. For research purposes, 361 respondents were surveyed via a paper-based questionnaire, but only 264 respondents had user experience in the four biometric technologies. In this way, we can confirm that respondents have a certain level of knowledge about both the biometric technologies and FinTech applications. The survey period is from August 1, 2019, to October 31, 2019.

The questionnaire is used to evaluate the biometrics in FinTech applications according to the modified TAM. As suggested by Saaty ( 1980 ), we verify the consistency of 264 questionnaires and that the valid questionnaire’s CI and CR values are less than 0.1 to meet the requirement. As a result, the final standard weight of the evaluation framework was obtained using the AHP.

As shown in Table 1 , the PP object (0.283) is the most emphasized object when using the modified TAM to explore biometric identification in FinTech applications, followed by the perceived ease of use (0.276), PU (0.257), and PT (0.184) objects. Nevertheless, PP, perceived ease of use, and PU were over 0.25, but PT did not exceed 0.25. This finding indicates that when exploring this research topic, the particular aspects of how users perceive trust should be considered (Hassan and Wood 2020 ).

Within the perceived privacy object, PP1 (“Users perceive that biometric identification in FinTech applications will not invade privacy” (0.374)) was highlighted as the most critical criterion. According to some studies, the major expectations of biometric identification development and popularization should include the protection of personal privacy during commercialization (Ogbanufe and Kim 2018 ; Wang et al. 2019 ). In compliance with the first criterion of the analysis, not invading privacy is the first PP object to further accentuate the effort to realize and promote biometric identification in FinTech applications.

PE1, “Users perceive that biometric identification in FinTech applications is convenient to use” (0.369), was expressed as the highest priority factor in perceived usability objects. Compared with passwords or other methods, people are increasingly concerned about convenience, so the demand for reliable user identification technology has increased. Therefore, biometrics is considered an effective tool for logging into FinTech applications (Mead 2016 ). In some cases, biometrics can be used in conjunction with the interface of FinTech applications to reduce the login time provided by system mechanisms. Therefore, biometrics can be utilized to not only improve user convenience but also enhance security. This implies that perceived ease of use has helped promote the popularity of biometrics and FinTech applications (Costigan 2017 ).

PU1, “Users perceive that biometric identification in FinTech applications is effective” (0.395), was the main criterion for exploring the perceptually useful objects of this research topic. It is necessary to examine the setting of thresholds of a recognition system for effective matching because both registration and acquisition failures (in recognition processes) mean that the system cannot “extract” and distinguish the appropriate feature characteristics of the user’s biometrics. Failure to register and/or obtain access indicates that a person’s biometrics may not be of sufficient quality for identification; however, the user would then consider the biometric technology to be useless. Alternatively, auxiliary applications, software, or mechanisms can be adjusted to provide a better user experience while increasing login success rates. As a result, users will be more likely to accept biometrics in FinTech applications (Stewart and Jürjens 2018 ).

PT3 of the perceived trust object, “Users perceive that biometric identification in FinTech applications is safer than other identification methods,” ranks first. PT3 plays the most important role in being perceived as trustworthy by users (Jain et al. 2016 ). With the competition of various types of identification methods, biometric technologies that have been developed can gain user trust (Menat 2016 ). However, trust needs to be established over a long period; this need may explain why PT has less weight in the model. The weights of the other two criteria within this object are also lower than those of other objects. However, by separately reviewing the criteria, we can still comprehend the strengths of biometric identification in FinTech applications. When users perceive trust as time passes, the advantages of biometric identification will become one of the important core competencies of these technologies. Regarding the results of this study, there is still room for improvement in PT, which could then increase use intention.

General scenario

This scenario represents the user’s opinion on the evaluation object related to the corresponding biometric method in the FinTech application. The combined results indicate how to simultaneously achieve all four assessment goals. After completing the modified TAM via the AHP, our interviewees evaluated these four biometric technologies to determine the most recommendable and more potential biometric technology. Respondents compared the performance of each biometric technology in pairs. As shown in Table 1 , face recognition (0.262) is the most recommended, followed by voice recognition (0.255), fingerprint recognition (0.250), and iris recognition (0.233).

In addition, a score for every solution according to the criteria is calculated in each column in Table 1 . These scores represent a performance distribution of the particular assessment of biometric methods. Several important explanations of the general condition can be made based on the results. Because face recognition may meet the requirements of the perceived ease of use object and PP object, this technology performs best among the four biometric technologies. Fingerprint recognition is the most commonly employed and stably performing technology in every object (refer to Fig.  3 ). In addition to meeting the requirement of perceived ease of use, face and voice recognition also work well in terms of PU; these originate from biometric characteristics in human behaviors. This finding explains why face and voice recognition received higher scores on the AHP. However, fingerprint recognition does not rank as well as most people predicted possibly because it is more familiar to people, so the respondents could not clearly recognize its specialty. The results suggest that iris recognition can be improved.

General condition

Perceived ease of use dominant scenario

By varying the weights assigned to the four analysis objects, this study confirms the preference of biometric recognition methods in FinTech applications with specific conditions. In this case, it is assumed that easy-to-use objects are predominant. As illustrated by Fig.  4 , the weight of perceived ease of use increases from the original weight of 0.276 to a maximum weight of 1, while the weights of the other three objects decrease proportionally to zero, as shown in Eqs.  9 – 13 .

When the perceived ease of use weight is 1, we note that the score of face recognition is ranked first (0.279), followed by voice recognition (0.260), fingerprint recognition (0.254), and iris recognition (0.206). Although the ranking was consistent, the face recognition score increased, the iris recognition score decreased, and the other two scores underwent minimal changes. Face recognition was ranked second in the current biometric technology market (Wu et al. 2018 ). Initially, face recognition scored higher in easy-to-use criteria, but iris recognition was weaker in the object’s criteria. Therefore, as the weight of perceived ease increases, face recognition scores better. The obvious conclusion is that the biometrics evaluated in different situations will produce different results. In this case, face recognition is undoubtedly the preferred biometric recognition method to meet this object requirement. In commercialization, fingerprint recognition identification is most commonly adopted in current applications. However, due to user behavior, face recognition and voice recognition may become popular in future FinTech applications.

Perceived usefulness dominant scenario

In the PU dominant scenario, its weight gradually increases to be the dominant evaluation object in the same way as in the previous scenario. As shown in Fig.  5 , voice recognition (0.298) is the most advantageous biometric identification, but iris recognition is still disadvantageous. This scenario is relevant when discussing the benefits of utilizing biometric usefulness, and voice recognition performs well compared to the other technologies due to its high performance with the criteria for this object. According to this result, voice recognition can be developed and its rank improved given its high level of usefulness. Unsurprisingly, voice recognition has attracted attention because of the recent increase in artificial intelligence (AI) voice assistants (Sriwati et al. 2019 ). Assuming that there is a desire for one form of biometric identification to better facilitate its usefulness, the odds of amplifying its penetration and popularity in FinTech applications can be overcome. Voice recognition is applied not only in identification but also in machine or application control. Such functions have recently been embedded in some FinTech applications (Li and Mills 2019 ).

Perceived trust dominant scenario

When the PT object emphasizes its related importance in this scenario as the dominator, as shown in Fig.  6 , iris recognition has the highest score (0.299) and is the most significant identification in this scenario, fingerprint recognition (0.252) remains stable, followed by voice recognition (0.227), and face recognition (0.222). Compared with other biometric identification methods, the biometric characteristics of iris recognition are more special and difficult to imitate (Ross 2010 ); thus, it gains a higher degree of trust. However, owing to limited ease of use and usefulness, the application of iris recognition is not as popular as the other three types of recognition. Although fingerprint recognition does not stand out for each object, it balances every requirement of each object. Therefore, fingerprint recognition is still the more popular biometric identification in all kinds of applications (Ogbanufe and Kim 2018 ). We believe that fingerprint recognition will not be absent in FinTech applications.

Perceived privacy dominant scenario

In the PP dominant scenario, PP is emphasized as the main evaluation target. As illustrated in Fig.  7 , face recognition (0.272) ranks first, followed by iris recognition (0.257), fingerprint recognition (0.240), and voice recognition (0.231). Because every biometric has a specific principle and mechanism, it is hard to assess which is more prominent corresponding to each criterion of the PP object. Therefore, the difference between any two scores is slight. Unsurprisingly, based on the perspective of user behavior, face recognition still ranks first and completely demonstrates its competitiveness in biometrics in FinTech applications. It should be highlighted that faces are easy to see; therefore, face recognition involves minor privacy issues (Trivikram et al. 2017 ). The integrity of a biometric identifier in all objects will lead to its prosperity in FinTech applications.

In recent years, biometric technology has been vigorously promoted globally to enhance security in information technology (IT) and promote the development of emerging industries (Wang et al. 2019 ). Although biometric technologies have been employed in particular fields for a long time, they have gradually gained popularity to enhance the security of consumers and consumer electronics (Jain et al. 2016 ; Dubey 2019 ). To meet the various needs of FinTech applications, since user experience has an important role in FinTech applications, each form of biometric recognition should be carefully reviewed based on user perception (Milian et al. 2019 ). The current study identifies how different evaluation objects of the improved TAM determine the corresponding biometrics in FinTech applications. The AHP was applied to assess evaluation objects and confirm their relative importance. In addition, the results generated by the AHP were applied to collect corresponding biometrics in the FinTech applications for five different evaluation target scenarios via sensitivity analysis. A total of five conditions are obtained by separately adjusting the weights of the four evaluation objects. In this way, we deduce which biometrics can perform better and the corresponding conditions. These findings can help readers understand how users view biometrics in FinTech applications.

Research contributions

This research makes several contributions as follows:

The research results indicate that face recognition generally received higher scores than the other types of recognition; voice and fingerprint recognition received the next highest scores. Face recognition also performed the best in the perceived ease of use dominant scenario. Voice recognition became a much more recommendable biometric identification in the PU dominant scenario and significantly outperformed the other three technologies. In the PT dominant scenario, iris recognition was regarded as the best form of identification owing to its biometric competence. In the last scenario, the PP dominant scenario, face recognition still ranked first and completely demonstrated its competitiveness based on user behavior perspectives. According to these scenarios, the ranking of biometric identifications in the suggested list for FinTech applications, from most to least beneficial, is face recognition, voice recognition, fingerprint recognition, and iris recognition.

In all scenarios, we observe that fingerprint recognition has relatively stable performance, thus explaining why it has a greater market share. However, the results indicate that most people consider that face recognition will have more merit in the future. Therefore, developers should consider strategic approaches to expand FinTech applications with embedded face recognition and then increase the penetration of this technology.

The research results indicate that voice recognition scored the highest in the PU scenario. This finding is consistent with those of other reports, which indicate that voice recognition has ranked second in multi-biometric systems (Trivikram et al. 2017 ). Since AI voice assistant devices, such as Amazon Echo, have become popular, voice recognition has become the most noticeable biometric because it demonstrates the best usefulness of identification (Sriwati et al. 2019 ).

The future of biometric identification in FinTech applications demands rigorous identification of individuals in high-security environments. However, this study concludes with some considerations, such as those of the PT and PP objects, as presented in Table 1 . When biometric identification has obtained more working credit, these considerations of the PT object may decrease.

Finally, researchers have focused on the criteria for predicting which biometric identification to evaluate so that they can utilize it. Nevertheless, technologies that they predicted may be different from biometric approaches but less applicable to commerce and the market continue to be applied in FinTech. This study not only constructs a modified TAM but also suggests strategies for developing biometric forms of identification in FinTech applications. Developers of biometric identification methods should take the advantages or disadvantages discovered from research results into account in the future for strengthening, improving, or even eliminating other potential technologies. For instance, face recognition should address its weakness in the criteria of the PT object.

The results indicate that face recognition, voice recognition, and fingerprint recognition can simultaneously achieve all four object requirements. While face recognition is regarded as the best form of biometric identification for FinTech applications, fingerprint recognition is a stable alternative, and voice recognition is a potential alternative.

Research limitations and future work

This study has some limitations. First, most of the respondents are international students, so the results may not represent the overall opinions of users. Those performing further research may consider conducting studies worldwide. Second, we did not categorize the participants by demographic segmentation, thus possibly affecting the generalizability of the results. Hence, future research may include demographic segmentation to proceed with a typical structural equation modeling analysis. The third limitation is the hypothetically stated criteria. Although all participants were experienced in all four biometric identification methods, the results may be affected if some participants did not clearly remember their previous user experience. Hence, future research could examine whether frequency influences user perception.

Abhishek A, Hemchand S (2016) Adoption of sensor-based communication for mobile marketing in India. J Indian Business Res 8(1):65–76. https://doi.org/10.1108/JIBR-08-2015-0091

Article   Google Scholar  

Alhassany H, Faisal F (2018) Factors influencing the internet banking adoption decision in North Cyprus: an evidence from the partial least square approach of the structural equation modeling. Financ Innov. https://doi.org/10.1186/s40854-018-0111-3

Arner DW, Barberis J, Buckley RP (2016) The evolution of fintech: new post-crisis paradigm. Georget J Int Law 47(4):1271–1320

Google Scholar  

Arner DW, Barberis J, Buckey RP (2017) Fintech, regtech, and the reconceptualization of financial regulation. Northwest J Int Law Bus 37(3):371–414

Arner DW, Zetzsche DA, Buckley RP, Barberis J (2019) The identity challenge in finance: from analogue identity to digitized identification to digital KYC utilities. Eur Bus Organ Law Rev 20:55–80

Atmaca S, Karadaş HA (2020) Decision making on financial investment in Turkey by using ARDL long-term coefficients and AHP. Financ Innov. https://doi.org/10.1186/s40854-020-00196-z

Bagozzi RP (2007) The legacy of the technology acceptance model and a proposal for a paradigm shift. J Assoc Inf Syst 8(4):243–254

Barin A, Canha LN, da Rosa Abaide A, Magnago KF (2009) Selection of storage energy technologies in a power quality scenario—the AHP and the fuzzy logic. In: Paper presented at the industrial electronics, 2009. IECON '09. 35th annual conference of IEEE. https://doi.org/10.1109/IECON.2009.5415150

Barkadehi MH, Nilashi M, Ibrahim O, Fardi AZ, Samad S (2018) Authentication systems: a literature review and classification. Telemat Inform 35(5):1491–1511

Başar A (2018) Aligning business and IT strategies in banking: a case study. J Glob Strat Manag 12(1):5–16

Biometrics Identity Management Agency (BIMA) (2010) Biometrics glossary version 4.0. Software Engineering Center CECOM Life Cycle Management Command. https://www.marines.mil/Portals/1/MCRP%203-33.1J%20BIOMETRICS%201.pdf . Accessed 23 June 2019

Breckenridge K (2014) Biometric state the global politics of identification and surveillance in South Africa, 1850 to the present. Cambridge University Press, Cambridge

Book   Google Scholar  

Buckley O, Nurse JCR (2019) The language of biometrics: analysing public perceptions. J Inf Secur Appl 47:112–119

Callen-Naviglia J, James J (2018) FinTech, RegTech and the importance of cybersecurity. Issues Inf Syst 19(3):220–225

Cao X, Yu L, Liu Z, Gong M, Adeel L (2018) Understanding mobile payment users’ continuance intention: a trust transfer perspective. Internet Res 28(2):456–476

Carranza R, Díaz E, Sánchez-Camacho C, Martín-Consuegra D (2021) e-Banking adoption: an opportunity for customer value co-creation. Front Psychol. https://doi.org/10.3389/fpsyg.2020.621248

Chau PYK, Hu PJH (2001) Information technology acceptance by individual professionals: a model comparison approach. Decis Sci 32(4):699–719

Cheng YH, Yeh YI (2011) Exploring radio frequency identification technology’s application in international distribution centers and adoption rate forecasting. Technol Forecast Soc Change 78:661–673

Choi H, Park J, Kim J, Jung Y (2020) Consumer preferences of attributes of mobile payment services in South Korea. Telemat Inform. https://doi.org/10.1016/j.tele.2020.101397

Chopdar PK, Sivakumar VJ (2019) Understanding continuance usage of mobile shopping applications in India: the role of espoused cultural values and perceived risk. Behav Inf Technol 38(1):42–64

Costigan N (2016) Behavioural biometrics—a new era of security. In: Chishti S, Barberis J (eds) The Fintech book: the financial technology handbook for investors, entrepreneurs and visionaries. Wiley, pp 116–117

Costigan N (2017) Assessing the impact of advanced sensors and behavioural biometric authentication technology on the reach of financial institutions. J Digit Bank 2(2):163–170

Davis FD (1989) Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Q 13:319–340

Donohue LK (2012) Technological leap, statutory gap, and constitutional abyss: remote biometric identification comes of age. Minn Law Rev 97(2):407–559

Drummer D, Feuerriegel S, Neumann D (2017) Crossing the next frontier: the role of ICT in driving the financialization of credit. J Inf Technol 32(3):218–233

Dubey V (2019) Fintech—digital way of ID verification and biometric verification in 2020. Int J Innov Appl Stud 27(4):896–901

Eagar M (2016) FinTech þ digital currency—convergence or collision? In: Chishti S, Barberis J (eds) The Fintech book: the financial technology handbook for investors, entrepreneurs and visionaries. Wiley, pp 212–216

Chapter   Google Scholar  

Erkut E, Tarimcilar M (1991) On sensitivity analysis in the analytic hierarchy process. IMA J Math Appl Bus Ind 3(1):61–83

Faddis NK, Matey RJ, Stracener J (2013) Improving tactical biometric systems through the application of systems engineering. IET Biom 2(1):1–9

Fenu G, Marras M (2018) Controlling user access to cloud-connected mobile applications by means of biometrics. IEEE Cloud Comput 5(4):47–57

Galankashi RM, Rafiei FM, Ghezelbash M (2020) Portfolio selection: a fuzzy-ANP approach. Financ Innov. https://doi.org/10.1186/s40854-020-00175-4

Ghani MA, Rahi S, Yasin NM, Alnaser FM (2017) Adoption of internet banking: extending the role of technology acceptance model (TAM) with E-customer service and customer satisfaction. World Appl Sci J 35(9):1918–1929

Guo G, Wechsle H, Shan S, Poh N (2016) Guest editorial special issue on mobile biometrics. IET Biom 5(1):1–2

Hassan HE, Wood VR (2020) Does country culture influence consumers’ perceptions toward mobile banking? A comparison between Egypt and the United States. Telemat Inform 46:101312. https://doi.org/10.1016/j.tele.2019.101312

Ho CJ, Chen JS (2009) Forecasting VoWLAN technology for the Taiwan mobile telecommunication industry. Technol Anal Strateg Manag 21(2):213–232

Hu Z, Ding S, Li S, Chen L, Yang S (2019) Adoption intention of Fintech services for bank users: an empirical examination with an extended technology acceptance model. Symmetry. https://doi.org/10.3390/sym11030340

Imerman MB, Fabozzi FJ (2020) Cashing in on innovation: a taxonomy of FinTech. J Asset Manag 21:167–177. https://doi.org/10.1057/s41260-020-00163-4

Iyer AP, Karthikeyan J, Khan RH, Binu PM (2020) An analysis of artificial intelligence in biometrics—the next level of security. J Crit Rev 7(1):571–576

Jain AK, Nandakumar K, Ross A (2016) 50 years of biometric research: accomplishments, challenges, and opportunities. Pattern Recogn Lett 79:80–105

Jeong N, Yoo Y, Heo TY (2009) Moderating effect of personal innovativeness on mobile-RFID services: based on Warshaw’s purchase intention model. Technol Forecast Soc Change 76:154–164

Jonker N (2019) What drives the adoption of crypto-payments by online retailers? Electron Commer Res Appl 35:100848. https://doi.org/10.1016/j.elerap.2019.100848

Kalinić Z, Marinković V, Djordjevic A, Liebana-Cabanillas F (2019) What drives customer satisfaction and word of mouth in mobile commerce services? A UTAUT2-based analytical approach. J Enterp Inf Manag 33(1):71–94

Kanak A, Sogukpinar I (2017) BioTAM: a technology acceptance model for biometric authentication systems. IET Biom 6(6):457–467

Kang J (2018) Mobile payment in Fintech environment: trends, security challenges, and services. Hum-Cent Comput Inf Sci. https://doi.org/10.1186/s13673-018-0155-4

Kang JW, Namkung Y (2019) The role of personalization on continuance intention in food service mobile apps. Int J Contemp Hosp Manag 31(2):734–752

Kim M, Kim S, Kim J (2019) Can mobile and biometric payments replace cards in the Korean offline payments market? Consumer preference analysis for payment systems using a discrete choice model. Telemat Inform 38:46–58

Ko CH, Yu CC (2015) Exploring employees’ perception of biometric technology adoption in hotels. Int J Organ Innov 8(2):187–199

Kou G (2019) Introduction to the special issue on FinTech. Financ Innov. https://doi.org/10.1186/s40854-019-0161-1

Kou G, Lin C (2014) A cosine maximization method for the priority vector derivation in AHP. Eur J Oper Res 235(1):225–232. https://doi.org/10.1016/j.ejor.2013.10.019

Kou G, Ergu D, Shang J (2014) Enhancing data consistency in decision matrix: adapting Hadamard model to mitigate judgment contradiction. Eur J Oper Res 236(1):261–271. https://doi.org/10.1016/j.ejor.2013.11.035

Lee JM, Rha JY (2016) Personalizatione-privacy paradox and consumer conflict with the use of location-based mobile commerce. Comput Hum Behav 63:453–462

Lee S, Kim BS, Kim Y, Kim W, Ahn W (2018) The framework for factors affecting technology transfer for suppliers and buyers of technology in Korea. Technol Anal Strateg Manag 30(2):172–185

Li X, Mills M (2019) Vocal features: from voice identification to speech recognition by machine. Technol Cult 60(2):129–160

Liébana-Cabanillas F, Lara-Rubio J (2017) Predictive and explanatory modeling regarding adoption of mobile payment systems. Technol Forecast Soc Change 120:32–40

Lin C, Kou G, Peng Y (2020) Aggregation of the nearest consistency matrices with the acceptable consensus in AHP-GDM. Ann Oper Res. https://doi.org/10.1007/s10479-020-03572-1

Liu CH, Wang JS, Peng CC, Shyu JZ (2015) Evaluating and selecting the biometrics in network security. Secur Commun Netw 8(5):727–739

Mead W (2016) Banking and the E-book moment. In: Chishti S, Barberis J (eds) The Fintech book: the financial technology handbook for investors, entrepreneurs and visionaries. Wiley, pp 7–9

Mehrban S et al (2020) Towards secure FinTech: a survey, taxonomy, and open research challenges. IEEE Access 8:23391–23406. https://doi.org/10.1109/ACCESS.2020.2970430

Menat R (2016) Why we’re so excited about fintech. In: Chishti S, Barberis J (eds) The Fintech book: the financial technology handbook for investors, entrepreneurs and visionaries. Wiley, pp 10–12

Merhi M, Hone K, Tarhini A, Ameen N (2020) An empirical examination of the moderating role of age and gender in consumer mobile banking use: a cross-national, quantitative study. J Enterp Inf Manag. https://doi.org/10.1108/JEIM-03-2020-0092

Milian EZ, de Spinola M, de Carvalho MM (2019) Fintechs: a literature review and research agenda. Electron Commer Res Appl 34:100833. https://doi.org/10.1016/j.elerap.2019.100833

Morosan C (2016) An empirical examination of U.S. travelers’ intentions to use biometric e-gates in airports. J Air Transp Manag 55:120–128

Murakami T, Fujita R, Ohki T, Kaga Y, Fujio M, Takahashi K (2019) Cancelable permutation-based indexing for secure and efficient biometric identification. IEEE Access 7:45563–45582. https://doi.org/10.1109/ACCESS.2019.2908456

Norma D, Farah ML (2020) Factors affecting continuance intention of FinTech payment among Millennials in Jakarta. Eur J Bus Manag Res. https://doi.org/10.24018/ejbmr.2020.5.4.444

Ogbanufe O, Kim DJ (2018) Comparing fingerprint-based biometrics authentication versus traditional authentication methods for e-payment. Decis Support Syst 106:1–14

Pai CK, Wang TW, Chen SH, Cai KY (2018) Empirical study on Chinese tourists’ perceived trust and intention to use biometric technology. Asia Pac J Tour Res 23(9):880–895

Patil P, Tamilmani K, Rana NP, Raghavan V (2020) Understanding consumer adoption of mobile payment in India: extending Meta-UTAUT model with personal innovativeness, anxiety, trust, and grievance redressal. Int J Inf Manag 54:102144. https://doi.org/10.1016/j.ijinfomgt.2020.102144

Pavlou PA (2003) Consumer acceptance of electronic commerce. Integrating trust and risk with the technology acceptance model. Int J Electron Commer 7(3):101–134

Priya R, Gandhi AV, Shaikh A (2018) Mobile banking adoption in an emerging economy: an empirical analysis of young Indian consumers. Benchmark Int J 25(2):743–762

Qi Y, Xiao J (2018) AI powers financial services to improve people’s lives. Commun ACM 61(11):65–69

Rahia S, Ghani MA, Alnaser FM, Ngah AH (2018) Investigating the role of unified theory of acceptance and use of technology (UTAUT) in internet banking adoption context. Manag Sci Lett 8:173–186

Rashed A, Alajarmeh N (2015) Towards understanding user perceptions of biometrics authentication technologies. Int J Comput Sci Inf Secur 13(6):25–33

Ross A (2010) Iris recognition: the path forward. IEEE Comput 43(2):30–35

Saaty TL (1980) The analytic hierarchy process. McGraw-Hill

Schierz PG, Schilke O, Wirtz BW (2010) Understanding consumer acceptance of mobile payment services: an empirical analysis. Electron Commer Res Appl 9:209–216

Schmidt K, Aumann I, Hollander I, Damm K, von der Schulenburg JMG (2015) Applying the analytic hierarchy process in healthcare research: a systematic literature review and evaluation of reporting. BMC Med Inform Decis Mak 15:112–139

Shachak A, Kuziemsky C, Petersen D (2019) Beyond TAM and UTAUT: future directions for HIT implementation research. J Biomed Inform 100:103315. https://doi.org/10.1016/j.jbi.2019.103315

Shao Z, Zhang L, Li X, Guo Y (2019) Antecedents of trust and continuance intention in mobile payment platforms: the moderating effect of gender. Electron Commer Res Appl. https://doi.org/10.1016/j.elerap.2018.100823

Shaw B, Kesharwani A (2019) Moderating effect of smartphone addiction on mobile wallet payment adoption. J Internet Commer 18(3):291–309

Singh N, Sinha N, Liébana-Cabanillas FJ (2020) Determining factors in the adoption and recommendation of mobile wallet services in India: analysis of the effect of innovativeness, stress to use and social influence. Int J Inf Manag 50:191–205

Sonea A (2016) So, you think the innovation lab is the answer? In: Chishti S, Barberis J (eds) The Fintech book: the financial technology handbook for investors, entrepreneurs and visionaries. Wiley, pp 181–186

Srdjevic Z, Samardzic M, Srdjevic B (2012) Robustness of AHP in selecting wastewater treatment method for the coloured metal industry: Serbian case study. Civ Eng Environ Syst 29(2):147–161

Sriwati S, Eruinsyah E, Karim S, Rahman F (2019) Control of electronic devices using smartphone-based voice identification. IOP Conf Ser Mater Sci Eng 662:022004. https://doi.org/10.1088/1757-899X/662/2/022004

Stewart H, Jürjens J (2018) Data security and consumer trust in FinTech innovation in Germany. Inf Comput Secur 26(1):109–128

Tanimoto S, Toriyama S, Iwashita M, Endo T, Chertchom P (2019) Secure operation of biometric authentication based on user’s viewpoint. In: 2019 IEEE international conference on big data, cloud computing, data science and engineering (BCD), Honolulu, HI, USA, 2019, pp 166–171. https://doi.org/10.1109/BCD.2019.8885177

Trivikram C, Samarpitha S, Madhavi K, Moses D (2017) Evaluation of hybrid face and voice recognition systems for biometric identification in areas requiring high security. I-Manag J Pattern Recogn 4:9–16

Venkatesh V, Davis FD (2000) A theoretical extension of the technology acceptance model: four longitudinal field studies. Manag Sci 46(2):186–204

Venkatesh V, Davis FD, Morris MG (2007) Dead or alive? The development, trajectory and future of technology adoption research. J Assoc Inf Syst 8:267–286

Wang JS, Liu CH, Shyu JZ (2013) Biometrics technology evaluating and selecting model building. Technol Anal Strateg Manag 25(9):1067–1083

Wang X, Xue H, Liu X, Pei Q (2019) A privacy-preserving edge computation-based face verification system for user authentication. IEEE Access 7:14186–14197

Winebrake JJ, Creswick BP (2003) The future of hydrogen fueling systems for transportation: an application of perspective-based scenario analysis using the analytic hierarchy process. Technol Forecast Soc Change 70(4):359–384

Wu J, Liu L, Huang L (2017) Consumer acceptance of mobile payment across time: antecedents and moderating role of diffusion stages. Ind Manag Data Syst 117(8):1761–1776

Wu SC, Chen PT, Swindlehurst AL, Hung PL (2018) Cancelable biometric recognition with ECGs: subspace-based approaches. IEEE Trans Inf Forensics Secur 14(5):1323–1336

Yu D, Kou G, Xu Z, Shi S (2021) Analysis of collaboration evolution in AHP research: 1982–2018. Int J Inf Technol Decis Mak 20(1):7–36. https://doi.org/10.1142/S0219622020500406

Zhang J, Kou G, Peng Y, Zhang Y (2021) Estimating priorities from relative deviations in pairwise comparison matrices. Inf Sci 552:310–327. https://doi.org/10.1016/j.ins.2020.12.008

Zhu Y, Li X, Wang J, Li J (2020) Cloud-assisted secure biometric identification with sub-linear search efficiency. Soft Comput 24:5885–5896. https://doi.org/10.1007/s00500-019-04401-9

Download references

Author information

Authors and affiliations.

Institute of Technology Management, National Yang Ming Chiao Tung University, 1001 Ta-Hsueh Road, Hsinchu, 300, Taiwan

Jen Sheng Wang

You can also search for this author in PubMed   Google Scholar

Corresponding author

Correspondence to Jen Sheng Wang .

Additional information

Publisher's note.

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ .

Reprints and permissions

About this article

Cite this article.

Wang, J.S. Exploring biometric identification in FinTech applications based on the modified TAM. Financ Innov 7 , 42 (2021). https://doi.org/10.1186/s40854-021-00260-2

Download citation

Received : 02 October 2020

Accepted : 24 May 2021

Published : 08 June 2021

DOI : https://doi.org/10.1186/s40854-021-00260-2

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• FinTech applications
• Perceived privacy
• Perceived trust

Services on Demand

Related links, south african journal of information management, on-line version  issn 1560-683x print version  issn 2078-1865, sajim (online) vol.23 n.1 cape town  2021, http://dx.doi.org/10.4102/sajim.v23i1.1407 .

ORIGINAL RESEARCH

Biometric technology in banking institutions: 'The customers' perspectives'

Abraham Morake; Lucas T. Khoza; Tebogo Bokaba

Department of Applied Information Systems, College of Business and Economics, University of Johannesburg, Auckland Park, South Africa

Correspondence

BACKGROUND : Over the years, attention has been focused on digital banking and financial technology with little or no attention being paid to biometric banking technology. OBJECTIVE : The study aimed to investigate the need for security and simplicity in the authentication of retail payments, digital banking and financial technology through the application of biometric systems. METHOD : The study employed quantitative research methodology and a response rate of 52% was achieved. A set of questionnaires was distributed for data collection. RESULTS : The study's findings indicated it is imperative for all businesses that participate in financial businesses to fully implement the best possible security measures or systems to ensure or enhance security for financial business activities CONCLUSION : Based on the findings of the study, it is recommended that businesses must adopt the new innovative and secured mechanisms of financial dealings to enhance innovation, security and flexibility.

Keywords : biometrics; financial technology; security; authentication methods; digital banking.

Introduction

There are increasing concerns related to the security in authenticity and integrity of banking systems (De Souza Faria & Kim 2013; Petrlic & Sorge 2013). The weakness of the current verification or authentication methods such as pin numbers and passwords contributes significantly towards information leakage stored in Automated Teller Machine (ATM) smartcard which results in loss of money in bank account (Jaiswal & Bartere 2014). The word biometrics originates from ancient Greek and implies measures - bios mean life, whilst metrics mean measuring, therefore in full it means measuring life (Prabhakar, Pankanti & Jain 2003). It can be described as the process of identifying human uniqueness employing physical traits that include the face, fingerprint, iris and behavioural traits (Jain, Flynn & Ross 2007). There are various biometrics classifications: fingerprint scrutiny, face examination, hand geometry, iris observation, voice recognition, and signature acknowledgement (Clodfelter 2010).

The birth of biometrics can be traced back to the 19th century where it mainly focused on gaining knowledge of people's physical traits to secure their identity (Maguire 2009). Earlier biometrics was mainly applied within high-security applications. However, it is currently applied within a wider variety of public-facing applications, for example, in prisons, by police for drivers' license verification, canteen administration, payment systems, in the borders for verification control, including electoral system (Ashbourn 1999). Since the late 1990s, there have been changes in biometrics as a primary security replacement technology from an older form of identification such as passwords and security pin-codes (Maguire 2009). Biometrics initially was used to measure the physical and behavioural features of a person (Galton 1901). Upcoming biometric verification applications comprise ATM use, workplace authentication, network access, travel and tourism, world wide web connections, and mobile connections (Ashbourn 1999).

Over the last few years, more studies have been done on digital banking, financial technology and other areas rather than the impact of biometrics within banking and retailing in South Africa. Digital is the separation of information from physical data storage to the technical potential or digital (Legner et al. 2017). There are various characteristics of digitisation, namely collaboration, sharing, co-creation, connectivity, communication, mobility, and flexibility (Syler & Baker 2016). Digital networks started to join retailers together with traders, clients and customers to develop the identified needs for the first online connected catalogues and inventory software systems (Kelman 2016). Digital banking refers to the process of shifting into online banking and the digitisation of the entire outdated banking activities, including plans that were historically offered to the bank customers and required customers to physically visit the bank to do specific activities such as money deposit, withdrawals, money transfers and account management (Coetzee 2018). Digital banking has facilitated customers to overcome controlled time banking and local area operations (Das 2018). Digital banks use advanced banking systems that can swiftly implement new services allowing for seamless mobility for bank users (Varga & David 2017).

The main problem with this research is that there is a demand for more innovative and secured banking systems that will enable customers to access their money at any given time and location. In this fourth industrial revolution (4IR) era and the need for transformation within the banking sectors, technological advancement has provided better opportunities for financial institutes to tap. In contrast, many financial institutions have conformed to the traditional digital banking platforms as a mode of operation. This digital banking platform enables customers to make money deposits, withdrawals, transfers and account management without physically visiting the bank. However, none of these banking sectors have been able to take full advantage of the capacity and possibilities of the 4IR for a more innovative and simplified banking platform.

There are only few studies which have studies and covered biometric banking and payment systems. To bridge the gap, this study seeks to evaluate the innovative and secured methods of paying for items at retail stores and accessing money without physically having a bank card and hard cash through the application of biometrics. The study focuses on biometrics digital banking financial technology as an alternative means of authentication for mobile baking transactions such as payments and bank transfers. Current authentication methods still use traditional password authentication. In addition, this article seeks to create awareness in banks and retailers on the significant role of biometrics as an essential mechanism in providing speedy, secured, flexible and innovative authentication process to protect the funds/money of customers and the organisation, which can result in crime being lowered or prevented.

This article is structured as follows: section literature review, discusses the current knowledge and findings around biometric technology in the banking system. Section Challenges of biometrics covers the research problem that this research study attempts to address. Section Research method and design discusses the research methodology. Lastly, sections Results and analysis through 11 is the data analysis.

Literature review: Biometric technology in the banking system

As submitted by Ateba et al. (2013), for banks to remain relevant, successful and competitive in today's competitive world, they must provide innovative and best-secured services to their customers.

Customer and organisational perspectives

A customer can be described as a stakeholder of an organisation who provides payment in exchange for products or services (Ateba et al. 2013). In addition, a customer cannot only be described as a person but also an organisation (e.g. university, bank, construction company, school, legal firm and hospital) that buys goods and services from other retailers (Rahman & Safeena 2016). Organisations (banks and retail) need to understand that customers come from various occupations (Rahman & Safeena 2016). More banking and other financial transactions are being done online by customers and fraudsters have followed suit, initiating ever-more sophisticated attacks. With the risk of digital fraud and theft increasing many organisations have searched for solutions to stop fraudsters from launching ever-more sophisticated attacks. Banks cannot stop or limit the high rate of transaction scams and security breaks by using traditional security systems such as password/pin and identification cards; therefore, digital banking solutions appear to be a perfect mechanism to defeat these threats (Hosseini & Mohammadi 2012). Pin code verification alone cannot be regarded as a strong defence mechanism against security breaches. Using digital banking solutions, the operator's data or information is securely kept in an encrypted container or sandbox (Johnson 2019).

Digital perspective

Digital banking solutions have proven to be more innovative for end-users, who appreciate replacing a complicated password with a fingerprint or face scan, which features biometric technologies (Agidi 2018). By applying biometrics, traditional passwords are becoming a thing of the past; biometrics is taking over banking security. To achieve safeguarding of operations and customer transactions, one solution is to secure banking using a consistent authentication method such as biometric (Hosseini & Mohammadi 2012). Biometrics characteristics include fingerprints, veins, palm veins, iris, retina, face, voice, and handwritten signature. The patterns of blood vessels in the palm finger are so different that no two or more individuals possess the same, and this can serve as a trusted security system (Ahmad, Ali & Adnan 2012). Biometrics is still in its early stages in developing countries, but it has been developed and adopted by businesses to increase the security and efficiency of the adopter's operations (Agidi 2018).

Usage of biometrics in banking institutions is popular in developed countries thus, the adoption rate of biometrics is growing significantly (Venkatraman & Delpachitra 2008). There is no hesitation that biometrics are escalating for banking security, to an extant identifying authentication through biometric application is highly secured compared to password authentication (Liang, Samtani, Guo & Yu 2020). Biometric authentication is also coming to physical payments cards; biometrics are progressively being used for account access, even replacing debit cards at the ATMs (Lee 2016). Biometrics provides a much more reliable and efficient method of verification than relying only on human agents. The security and efficiency principles of biometrics make the adoption of biometrics an attractive prospect to banking institutions across the world (Agidi 2018). With the average banking customer managing a broader range of financial transactions online through desktop and mobile devices, the need for simple and secured access to their banking data is becoming a top priority for banking service providers intending to differentiate themselves from the direct rivals. As the digital age expands, banks need to balance security and accessibility (Varga 2018).

Major South African banks include: ABSA, FNB, Nedbank, Standard Bank, and Capitec (Coetzee 2018). This is based on their revenue generation, large base of customers, services and products they offer and marketing strategies they deliver. Without the successful implementation and adaption of e-banking by the South African banking industry, most banks will struggle to perform optimally through the adaption of the 4IR and FinTech (Abukhzam & Lee 2010). Businesses have realised the increasing value digitisation provides towards the growth of businesses (Neumeier et al. 2017).

It is important that digital payment service providers (banks) have a comprehensive cybersecurity strategy aided by a robust framework to assist all stakeholders participating in the ecosystem (Kristensen & Solvoll 2019). There is a demand for managing service interface and customisation of products and services influenced by the input of technology offered in business settings (De Farias et al. 2014). Advanced biometric payment methods enhance the convenience, choice of payments and alternative payment methods for customers. Payment methods allow customers to conduct business and commercial activities with ease and flexibility at any given time (Kristensen & Solvoll 2019). Payment experts concur that electronic payment techniques are efficient, convenient and fast (Crowe, Schuh & Stavins 2006).

Biometrics application in automated teller machines

Biometrics in banking for ATM authentication provides both the banks and the customers with an outstanding benefit through providing customers with the flexibility to do transactions without physically having their bank cards; thus, banks can avoid the costs and liabilities of customer problems because of lost and/or stolen bank cards (Vernkatraman & Delpachitra 2008). Using biometrics in banking, ATMs are popular in developed countries; thus, the adoption rate of biometrics is growing significantly (Venkatraman & Delpachitra 2008). There is no doubt that biometrics is escalating in banking security, but it might be a while before identifying authentication without passwords is completely secure (Furnell & Evangelatos 2007). Biometric authentication is also coming to physical payment cards; thus, biometrics is progressively being used for account access, even replacing debit cards at the ATMs (Choi et al. 2007).

Challenges of biometrics

Biometric challenges can negatively impact people and businesses or customers and organisations. Bank crises and failures can be attributed to the growing extent to which scammers and fraudsters operate (Bhasin 2015). Fraud is considered a global phenomenon that negatively challenges all sectors of the economy (Bhasin 2015). A rapid increase in security cracks and transactional breaches within traditional security systems such as pin codes and passwords is speedily influencing the evolution of a strong biometric authentication method (Hosseini & Mohammadi 2012).

In addition, a factor that can contribute towards the challenges of adopting biometrics is too much time and money spent to educate people who are technologically and biometrically illiterate (Ahmad et al. 2012). New deployments or the premature phase of biometric technology are quite similar to the introduction of any other system, since it might take a while for general users to accept it, depending on the system's impact on them (Wayman et al. 2005).

Any form of change in the customers finger (a user cuts him-/herself by mistake) may lead to the users being denied access to their respective systems that has been created by the users with their normal fingerprint (Ahmad et al. 2012).

Another significant challenge of biometrics includes a scenario whereby, should the user be involved in an accident and lose an eye, finger or facial changes occur because of scratches or cuts, the biometric system will not recognise the user and will reject the user as a result of the physical changes or damages (Aly et al. 2008; Buddharaju, Pavlidis & Manohar 2008).

Biometrics has difficult challenges that may impact the human rights of a person negatively, for example, when a thief decides to cut off a victim's finger to gain access to their information and the system (Choi et al. 2007; Chetty & Wagner 2009; Jin, Kim & Elliott 2007; Pacut & Czaika 2006; Tan et al. 2010; Toth 2005). Dust and grime on the fingerprint scanner may impact the quality of the system negatively, which may result in a situation where the system does not recognise the user (Ahmad et al. 2012).

There are various issues that characterise the challenges of biometrics in problems such as signature authentication forgery, the high cost of implementing liveness detection, dust dropped on scanners, poor quality of the scanner to recognise the user, a time-consuming system, poor human machine interaction, lack of guidance for interacting with the system and a lack of proper information security policies and procedures (Brooks 2010; Jain & Kumar 2010; Koppenhaver 2007; Park 2008).

The main contributing factors to the challenges of biometric information usage is the misuses, negative interpretation, and failure to complying to the Protection of Personal Information Act (POPIA). The purpose of the Act is to protect people from harm by protecting their personal information, to stop their money being stolen, to stop their identity being stolen, and generally to protect their privacy, which is a fundamental human right (POPIA Act 2021). In South Africa, a person's fingerprints and blood type are considered personal information under the Electronic Communications and Transactions Act (ECTA 2002).

Since early 2020, the COVID-19 pandemic has impacted on and disrupted many aspects of peoples daily life. Touch-based technologies such as fingerprint and facial recognition scanners can be considered as indirect contributing factors for COVID-19, because they are used by many people for authentication and verification purposes at ATMs, stores and banks (Gomez-Barrero et al. 2021). Hygiene related fears have increased the societal resistance towards the use of touch-based biometrics sensors (Priesnitz et al. 2021). In addition, it is important to note that such fears have in turn fuelled research efforts in 2D or 3D touchless fingerprint recognition systems (Gomez-Barrero et al. 2021).

Benefits of biometrics

Biometric benefits can impact both people and businesses or customers and organisations. Moreover, biometrics can be considered a quicker information tracer and recovery method than manual or traditional verification methods carried out at the counter (Ahmad et al. 2012; Jain & Kumar 2010; Jain, Ross & Pankanti 2006).

Biometric security can be considered a method that contributes significantly towards ensuring the integrity, confidentiality and availability of information (Ahmad et al. 2012). Biometrics protects both logical and physical access controls. Logical access controls include the protection of network facilities, computers and information systems against unauthorised admission (Jain et al. 2006), whilst physical access controls ensure that only authorised people have access to IT infrastructures and document filing (Jain et al. 2006).

Forensic accounting is a requirement for banks to decrease the speedy growth of financial frauds (Bhasin 2015). In addition, biometric authentication methods offer a natural, unforgettable and rarely breached verification (Hosseini & Mohammadi 2012). Password, pin and code word authentication can be forgotten, cracked and guessed by hackers or scammers (Jain et al. 2006). In addition, fingerprint authentication is more secure, as it provides users with quicker verification and is impossible to forget compared to a password (Johnson 2019). Smartcards are also at risk of being lost, stolen and duplicated (Jain et al. 2006). Therefore, biometrics can be considered a solution for enhanced security, as the authentication relies on a person's physical traits (Jain et al. 2006). Physiological biometric features include retina, fingerprint, hand vein, iris, hand geometry facial recognition, and ear shape. These features are unique, and no one in the world shares them (Ahmad et al. 2012). Behavioural biometric features include voice recognition and signature verification (Ahmad et al. 2012; Jain & Kumar 2010).

Biometric security systems can assist banks with various benefits such as forensic application, criminal identification, border control and surveillance (Rhodes 2003). Various impacts may characterise the benefits of biometrics, for example, human signature authentication, being user-friendly, convenient and flexible, maintaining accuracy, faster information retrieval, strong matching algorithm and speaker recognition (Koppenhaver 2007; Park 2008; Wang et al. 2011).

Multi-factor authentication methods

The use of a password (or a PIN) to confirm the ownership of the user ID could be considered as a single-factor authentication (SFA) method (Ometov et al. 2018). Evidently, this is the weakest level of authentication (Dasgupta, Roy & Nag 2016; Bonneau et al. 2015).

Authentication with just a single-factor method is not reliable to provide enough protection because of several security threats such as rainbow table and dictionary attacks (Gunson et al. 2011). Two-factor authentication (2FA) methods consist of something the user has, such as cards, smartphones, or other tokens (Sun et al. 2014; Bruun, Jensen & Kristensen 2014). Multi-factor authentication (MFA) methods consist of something the user/customer is, specifically, biometric data or behaviour patterns such as fingerprint, face recognition, behaviour recognition and others (Ometov et al. 2018).

The need for reliable user authentication method has increased in the wake of intensified concerns about security and rapid advancements in communication, mobility, and networking (Yadav & Gothwal 2011). Frequently, MFA is based on biometrics, which is automated recognition of individuals based on their behavioural and biological characteristics (Frank, Biedert, Ma, Martinovic & Song 2012). Biometrics challenges and benefits will be further discussed in detail, because the term can be considered as a key technique of MFA. Figure 1 shows the evolution from SFA factor to MFA.

Research method and design

The research design that was used in this study was quantitative. Quantitative research refers to a numerical illustration of explanations of the phenomena (Sukamolson 2000). Throughout the study, data have been collected by means of:

A literature review and comparing a list of similar work done over the years.

Conducting an online survey to evaluate the use of biometrics to authenticate payment and day-to-day personal banking transactions.

Consulting with shoppers, bank users, financial institutions such as banks and general societies (students, employed and unemployed community members) with bank accounts.

A questionnaire survey was conducted on a sample population of respondents who have knowledge on biometrics, digital banking, financial technology, retail and customers. The overall number of the questionnaires shared received 336 responses. Out of the 336 responses, only 173 respondents submitted fully completed questionnaire, the remaining 162 respondents did not complete the survey. This process gave the questionnaire a successful completion response rate of 52%. The questionnaire was designed into four sections which are: A, B, C and D. Section A gathered the background information of the respondents, Section B collected the challenges of biometric, Section C collected benefits of biometric, and the final Section D gathered biometric solutions to enhance secured and innovative means of accessing, transferring and sharing money. The survey was distributed electronically via different social media platforms. The selected sample technique for this study is the probability sampling technique which facilitates study of a large population, and therefore was relevant for this study as its targeted sample size was 300 responses. Furthermore, quantitative research is commonly aligned with the probability sampling technique to enhance generalisability (Saunders et al. 2019). The reason for the study to employ students is because financial decision-making is very important for the success of students in their lives and careers; therefore, it is critical for students to understand funds management (Sachitra, Wijesinghe & Gunasena 2019). Another contributing reason for the study to use bank members such as managers is because they value financial information and have key financial knowledge (Akhtar & Liu 2018).

The study employed the random sampling technique in preference of the systematic, stratified and cluster random sampling techniques. The inclusion criterion for the study was shoppers with one or more bank accounts. The study mainly focused on the city of Johannesburg in Gauteng province. Johannesburg has an estimated population of 5 782 747. Out of this population, about 30% are below the standard age of owning a bank account (Department of Statistics South Africa 2019), totalling 1 734 824. From the remaining 4 047 923 shoppers with bank accounts, the sample size of the research was limited to 300 respondents because of issues such as time and resource constraints. The study only targeted the age group of 18-60. The study also targeted the population using payment mechanisms such as:

Electronic Fund Transfers (EFTs)

Credit and cheque cards

Internet banking transfers

Card-based payments

Debit cards

Visa Checkout

Samsung Pay/ Mobile Pay

Validity of the data collection tool used

The validity of the collected data was demonstrated through questionnaires and surveys. Content validity will be determined based on the reliably collected data provided by respondents (bank managers, retail managers and customers). Thus, constructive validity will be determined through evaluating the views of customers, bank managers and retail managers using biometrics authentication for payments and other activities. Both Cronbach's alpha and Statistical Package for the Social Sciences (SPSS Version 26) were used to ensure that the collected data is accurate, logical and factual (Scherbaum & Shockley 2015).

Using the Cronbach's alpha analytical tool on SPSS, it was found that the validity of the response regarding 'usage of biometrics in terms of financial sector' is 0.857. Table 1 shows the Cronbach's alpha values.

Ethical considerations

Approval to conduct the study was obtained from the College of Business and Economics, the University of Johannesburg. During data collection, personal information was not requested and participation in this research work was voluntary, and participants were allowed to withdraw upon completing the questionnaires.

Results and analysis

This section of the study presents findings of the study obtained during questionnaire distribution.

Descriptive statistics

Figure 2 describes the sector or occupation in which respondents are involved. In a practical example, the people belonging to academic and education sectors visit retail stores to purchase books, laptops and other academic or education-related merchandise. In construction sector, there must be a purchase of building or construction materials; the same applies to other occupations. This shows that data supplied by these categories of respondents are reliable.

The education sector has the highest percentage of 19.7%, followed by Other with 19.1%. Academia is the third-ranked sector with 16.2%, followed by the unemployed respondents with 14.5%. The information technology sector is ranked fifth with 9.8%, followed by the banking sector with 5.8%. Both the health and consulting sectors have a percentage of 4.6%. The manufacturing and insurance sectors both have a percentage of 2.3%. The construction sector has the lowest percentage of 1.1%. Table 2 shows the sector or occupation values to clarify values in Figure 2 .

The results show that the study has covered various organisational sectors. Academia, construction, consulting, education, health, information technology, insurance, manufacturing and other sectors, including the unemployed individuals, are all represented. This concludes that most organisational sectors have been represented in the study.

Highest qualification

Figure 3 describes the highest qualification of respondents to evaluate their potential level of understanding new topics that impact their daily lives and activities in this modern era of the 4IR. The results are arranged from the highest to the lowest percentage. Results reveal that 31.8% of the respondents have obtained a bachelor's degree, 16.8% matric or Grade 12, 15.0% an honours degree, another 13.3% a university diploma, 11.0% a Master's degree, 6.4% college diplomas, 4.0% other qualifications, 1.2% with a Ph.D. degree and 0.5% without matric.

The results indicate that a large percentage of the respondents have obtained a bachelor's degree. This indicates that most respondents have a good education and are more knowledgeable (Bosupeng 2017). A question on rating the educational level of the respondents has been included to evaluate their level of understanding new topics impacting their daily lives and activities in this modern era of the fourth industrial revolution.

It is important to be educated, well-informed and technologically exposed because education contributes significantly to developing a person's opinions, character, trading with others and preparing one for life experiences (Al-Shuaibi 2014). Additional literature aligned with the study provides that promising stages to prevent fraud activities are educating customers with various processes of avoiding being a victim of fraudsters (Bhasin 2015).

Correlation statistics

The purpose of this section is to describe the relationship between variables. Thus, extensive literature was used to analyse other sections of the article; the Pearson's correlation was conducted to explore statistical relationships amongst variables. Moreover, data analysis was conducted through matching and comparing the benefits variables together with the challenge variables. The Pearson's correlation was used because it works with the raw data values of the variables, whereas Spearman works with rank-ordered variables. Moreover, the Pearson's correlation evaluates the linear relationship between two continuous variables, whilst the Spearman correlation coefficient is based on the ranked values for each variable rather than the raw data (De Winter, Gosling & Potter 2016).

The data analysis technique used to analyse the data was correlation to predict the strength and direction between two variables. The strength of correlation between the variables is shown under the Pearson's correlation, whilst Sig. (2-tailed) represents the significance of the influence amongst the variables. Sig. (2-tailed) below 0.05 indicates that the relationship between the variables is significant, whilst Sig. (2-tailed) above 0.05 indicates that there is no significant relationship between the variables (Pallant 2020). Table 3 illustrates the Sig. (2-tailed) declaration, whilst Table 4 demonstrates the Pearson's correlation declaration.

Biometric challenges and benefits analysis

The Pearson's correlation was conducted to examine the relationship between biometric challenges and biometric benefits.

Appendix 1 shows the Pearson's correlation for enhanced surveillance with involvement in an accident, sensitivity of sensor performance and biometric characteristics. There is a weak positive significant relationship between enhanced surveillance and involvement in an accident ( r = 0.004; p = 0.216), enhanced surveillance and sensitivity of sensor performance ( r = 0.008; p = 0.202), and enhanced surveillance and biometric characteristics ( r = 0.004; p = 0.220).

These findings indicate that should the user be involved in an accident and have cuts on their biometric features such as fingers, face or iris, it will be difficult for a biometric reader to fully recognise the authorised user to gain access to a system. The biometric security system can assist banks and retailers with a wide range of benefits such as surveillance, as reported by Rhodes (2003).

Appendix 1 also shows the Pearson's correlation for enhanced border control with involvement in an accident, sensitivity of sensor performance and biometric characteristics. Statistical results indicate that there is a weak positive significant relationship between enhanced border control and involvement in accident ( r = 0.001; p = 0.253), enhanced border control and sensitivity of sensor performance ( r = 0.007; p = 0.205), and enhanced border control and biometric characteristics ( r = 0.048; p = 0.150).

These findings indicate that enhanced border control can be challenged by the fact that biometric characteristics such as face, fingerprint and voice recognition can be copied. The biometric security system can assist banks and retailers with a wide range of benefits such as forensic application, criminal identification, border control and surveillance (Rhodes 2003).

Appendix 1 next shows the Pearson's correlation for criminal identification with sensitivity of sensor performance and biometric characteristics. Statistical results indicate that there is a weak positive significant relationship between criminal identification and sensitivity of sensor performance ( r = 0.017; p = 0.181) and criminal identification and biometric characteristics ( r = 0.032; p = 0.163).

These findings imply that the process of effortlessly providing information about the criminal record of the individual can be challenged by a sensitivity of sensor performance. The biometric security system can assist banks and retailers with a wide range of benefits such as forensic applications, criminal identification, border control and surveillance (Rhodes 2003).

From Appendix 1 , the Pearson's correlation for ease of information retrieval with involvement in an accident and biometric characteristics can be observed. Statistical results show that there is a weak positive significant relationship between ease of information retrieval and being involved in an accident ( r = 0.014; p = 0.166) and ease of information retrieval and biometric characteristics ( r = 0.003; p = 0.224).

These findings indicate that the process of providing users with quicker verification can be hindered by damages or changes to the users' physical biometric features such as face, eyes and fingers caused by accidents. The biometric security system can assist banks with features maintaining accuracy, convenience, faster information retrieval, strong matching algorithm and speaker recognition (Koppenhaver 2007; Park 2008; Wang et al. 2011).

Appendix 1 shows the Pearson's correlation for strong matching algorithm with involvement in an accident, sensitivity of sensor performance and non-technologically inclined individuals. Moreover, statistical results indicate that there is a weak positive significant relationship between strong matching algorithm and being involved in an accident ( r = 0.017; p = 0.161), strong matching algorithm and sensitivity of sensor performance ( r = 0.007; p = 0.204), and strong matching algorithm and non-technologically inclined individuals ( r = 0.008; p = 0.200).

These findings indicate that biometric systems, which can easily differentiate between two or more biometric traits such as hands, eyes and face, can also be hindered by damages or changes to the user's physical biometric features such as the face, eyes and fingers caused by accidents. Pin code verification alone cannot be regarded as a strong defence mechanism against security breaches. However, by using biometric verification, the operator is secured to their data or information which is securely kept in an encrypted container or sandbox (Johnson 2019).

Appendix 1 shows the Pearson's correlation for lost or stolen smartcards and mobile devices with scammers, fraudsters and non-technologically inclined individuals. There is a weak positive significant relationship between lost or stolen smartcards and mobile devices and scammers ( r = 0.013; p = 0.188), lost or stolen smartcards and mobile devices and fraudsters ( r = 0.003; p = 0.224), and lost or stolen smartcards and mobile devices and non-technologically inclined individuals ( r = 0.005; p = 0.214).

These findings indicate that individuals, such as scammers, who participate in dishonest schemes by committing fraudulent activities may intend to exploit lost or stolen smartcards and mobile devices and steal funds of individuals. Biometrics in banking for ATM authentication provides both banks and customers with an outstanding benefit through providing customers with the flexibility to do transactions without physically having their bank cards; thus, banks can avoid the costs and liabilities of customer problems because of lost and/or stolen bank cards (Vernkatraman & Delpachitra 2008).

Appendix 1 also shows the Pearson's correlation for impossible to forget fingerprint authentication, non-technologically inclined individuals and biometric characteristics. There is a weak positive significant relationship between impossible to forget fingerprint authentication and non-technologically inclined individuals ( r = 0.014; p = 0.187) and impossible to forget fingerprint authentication and biometric characteristics ( r = 0.041; p = 0.155).

These findings indicate that fingerprint authentication is impossible to forget compared to a password. Moreover, non-technologically inclined individuals still trust that the pin code or password authentication method is the best technique for security authorisation (Bhasin 2015).

Appendix 1 further shows the Pearson's correlation for uniqueness, involvement in accident, sensitivity of sensor performance, non-technologically inclined individuals and biometric characteristics. Statistical results reveal that there is a weak positive significant relationship between uniqueness and being involved in an accident ( r = 0.011; p = 0.194), uniqueness and sensitivity of sensor performance ( r = 0.042; p = 0.155), uniqueness and non-technologically inclined individuals ( r = 0.001; p = 0.247), and uniqueness and biometric characteristics ( r = 0.002; p = 0.253).

These findings indicate that the uniqueness and benefits of the biometric authentication systems are supported by variables such as being involved in an accident, sensitivity of sensor performance, non-technologically inclined individuals and biometric characteristics such as the face, fingerprint and voice recognition (Hosseini & Mohammadi 2012). Physiological biometric features include retina, fingerprint, hand vein, iris, hand geometry, facial recognition and ear shape. These features are unique and no one in the world shares them (Ahmad et al. 2012).

Appendix 1 shows the Pearson's correlation for forensic application and dust dropped on the fingerprint scanner, involvement in an accident, sensitivity of sensor performance, non-technologically inclined individuals, and fake fingerprint forgery. There is a weak positive significant relationship between forensic application and dust dropped on the fingerprint scanner ( r = 0.015; p = 0.185), forensic application and involvement in an accident ( r = 0.002; p = 0.236), forensic application and sensitivity of sensor performance ( r = 0.000; p = 0.278), forensic application and non-technologically inclined individuals ( r = 0.000; p = 0.273), and lastly forensic application and fake fingerprint forgery ( r = 0.045; p = 0.153).

These findings indicate that because of physical biometric changes acquired by the users through an accident, it will be difficult for a biometric scanner system to easily recognise the user in a system. Forensic accounting is a requirement for banks to decrease the speedy growth of financial fraud (Bhasin 2015). In addition, the biometric authentication method offers natural, unforgettable, and hardly breached verification (Hosseini & Mohammadi 2012).

Biometric connections as solutions to deliver secured and innovative means of accessing, transferring and sharing money

Table 5 represents biometrics connections, including the level of agreeing and disagreeing by the respondents that the above-mentioned biometrics connections can be labelled as solutions that can assist banks and retailers in delivering secured and more innovative means of accessing, transferring and sharing money. From the 173 surveyed respondents, 93.1% of the respondents agreed that advanced authentications systems/single authentication that a user shares with no one (such as fingerprint compared to the old traditional authentication such as pins and passwords that can be guessed or traced) could be labelled as one of the solutions that can assist banks and retailers in delivering secured and innovative means of accessing, transferring and sharing money, whilst 6.9% disagreed on the statement. A majority (94.2%) of the respondents agreed that simple and secured access (ability to manage a broader range of financial transactions online) can be labelled as one of the solutions that can assist banks and retailers to deliver secured and innovative means of accessing, transferring and sharing money. In comparison, 5.8% disagreed with the statement. Whilst, 97.1% of the respondents agreed that enhanced convenience could be labelled as one solution that can assist banks and retailers in delivering secured and innovative means of accessing, transferring and sharing money, 2.9% disagreed with the statement. A higher percentage (91.9%) of the respondents agreed that increased security could be labelled as one of the solutions that can assist banks and retailers in delivering secured and innovative means of accessing, transferring and sharing money, whilst 8.1% disagreed with the statement. Regarding the final connection, 92.5% of the respondents agreed that reliable and efficient verification relying only on human agents could be labelled as one of the solutions that can assist banks and retailers in delivering secured and innovative means of accessing, transferring and sharing money, whilst 7.5% disagreed on the statement.

Literature postulates that banks must provide customers with more innovative and secured banking services (Hosseini & Mohammadi 2012). Biometric authentication or verification method that includes face and fingerprint recognition is considered a precise security solution for accessing, transferring and sharing money (Hosseini & Mohammadi 2012).

Pearson's correlation for enhanced surveillance indicates that should the user be involved in an accident and have cuts on biometric features such as fingerprint, face, and iris, it will be difficult for a biometric reader to fully recognise the authorised user to gain access into a system. Biometric security systems can assist banks and retailers with a wide range of benefits such as surveillance, as reported by Rhodes (2003).

Pearson's correlation for enhanced border control indicates that enhanced border control can be challenged by the fact that biometric characteristics such as face recognition, fingerprint and voice can be copied none are 100%. Biometric security systems can assist banks and retailers with a wide range of benefits such as forensic application, criminal identification, border control and surveillance (Rhodes 2003).

Pearson's correlation for criminal identification shows that the process of effortlessly providing information about the criminal record of the individual can also be challenged by the sensitivity of sensor performance. A biometric security system can assist banks and retailers with a wide range of benefits such as forensic application, criminal identification, border control and surveillance (Rhodes 2003).

Pearson's correlation for ease of information retrieval indicates that the process of providing users with quicker verification can be hindered by damages or changes to the user's physical biometric features such as face, eyes and fingers caused by accidents. Biometric security systems can assist banks with the following features maintaining accuracy, convenience, faster information retrieval, strong matching algorithm and speaker recognition (Koppenhaver 2007; Park 2008; Wang et al. 2011).

Pearson's correlation for strong matching algorithm findings indicate that biometric systems that can easily differentiate between two or more biometric traits such as hands, eyes and iris, can also be hindered by damages or changes to the user's physical biometric features such as face, eyes and fingers caused by accidents. Pin code verification alone cannot be regarded as a strong defence mechanism against security breaches, using biometric verification, the operator is secured to their data or information which is securely kept in an encrypted container or sandbox (Johnson 2019).

Pearson's correlation for lost or stolen smartcards and mobile devices findings indicate that individuals who participate in dishonest schemes through committing fraudulent activities such as scammers may intend to exploit lost or stolen smartcards and mobile devices of other users and steal funds of other individuals. Biometrics in banking for ATMs authentication provides both banks and customers with an outstanding benefit through providing customers with the flexibility to make transactions without physically having their bank cards. Thus, banks can avoid the costs and liabilities of customer's problems because of lost and stolen bank cards (Vernkatraman & Delpachitra 2008).

Pearson's correlation for impossible to forget fingerprint authentication indicates that fingerprint authentication is impossible to forget as compared to a password. Moreover, non-technologically inclined individuals still trust that pin code or password authentication methods are the best security authorisation techniques (Bhasin 2015).

Findings for the Pearson's correlation for uniqueness indicate that the uniqueness and benefits of the biometric authentication systems can be astounded by matters such as, involved in an accident, sensitivity of sensor performance, non-technologically inclined individuals and biometric characteristics such as face recognition, fingerprint and voice can be copied none are 100% (Hosseini & Mohammadi 2012). Physiological biometric features include retina, fingerprint, hand vein, iris, hand geometry, facial recognition and ear shape, these features are unique and no one in the world shares them or have the same (Ahmad et al. 2012).

Finally, Pearson's correlation for forensic application findings indicate that, because of physical biometric changes acquired by the users through an accident, it will be difficult for a biometric scanner system to recognise the user in a system easily. Forensic accounting is a requirement for banks to decrease financial fraud's speedy growth (Bhasin 2015). In addition, the biometric authentication method offers a natural, unforgettable and hardly breached verification (Hosseini & Mohammadi 2012).

This study was carried out to investigate the need for security and simplicity in the authentication of retail payments, digital banking and financial technology through the application of the biometric system. Furthermore, the study assessed the possible challenges, benefits and solutions to the biometrics authentication payment system. From the findings, the study further elaborated and discussed the biometric solutions that can assist banks and retailers in enhancing secured and innovative means of accessing, transferring, and sharing money. It is concluded that biometric technology is the innovative technology that different banking institutions can use to enhance security and innovation, protect the funds of their customers against scammers, fraudsters, hackers, and other constraints. Therefore, further studies can focus on the combined relationship amongst biometrics, digital banking and financial technology.

Acknowledgements

My genuine gratitude to Alpha and Omega, Creator of heaven and earth. Thank you to my supervisor, Mr Lucas Khoza and co-supervisor Mrs Tebogo Bokaba, for their patience, guidance, and continuous support towards completing this Journal.

Competing interests

The authors have declared that no competing interest exist.

Authors' contributions

All authors contributed equally to this work.

Funding information

This study received no specific funding from any agency in public, commercial or non-profit sectors. Because of the budget and time constraints, the study sampled only 173 respondents. The authors acknowledge that this could have impacted the ability to generalise the results of the study. It is therefore recommended that future studies should look at larger sample size. In addition, because of the limited number of individuals who are technologically inclined in the South African society, it was difficult to find respondents who fitted the criteria used to select respondents for the study. The study has not been extended to other provinces, as it is limited to the Gauteng province of South Africa.

Data availability

Data that support the findings of the study can be obtained from the corresponding author L.T.K.

The views and opinions expressed in this article are those of the authors and do not necessarily reflect the official policy or position of any affiliated agency of the authors.

Adewole, K.S., Abdulsalam, S.O., Babatunde, R.S., Shittu, T.M. & Oloyede, M.O., 2014, 'Development of fingerprint biometric attendance system for non-academic staff in a tertiary institution', Development 5(2), 62-70.         [  Links  ]

Agidi, R.C., 2018, 'Using biometric in solving terrorism and crime activities in Nigeria', Techsplend Journal of Technology 1(12), 91-105.         [  Links  ]

Ahmad, S.M.S., Ali, B.M. & Adnan, W.A.W., 2012, 'Technical issues and challenges of biometric applications as access control tools of information security', International Journal of Innovative Computing, Information and Control 8(11), 7983-7999.         [  Links  ]

Akhtar, S. & Liu, Y., 2018, 'SMEs' use of financial statements for decision making: Evidence from Pakistan', Journal of Applied Business Research (JABR) 34(2), 381-392.         [  Links  ]

Aly, S., Sagheer, A., Tsuruta, N., & R.I. Taniguchi, 2008, 'Face recognition across illumination', Artificial Life and Robotics 12(1-2), 33-37.         [  Links  ]

Ashbourn, J., 1999, The biometric white paper , viewed 30 July 2021, from http://homepage.ntlworld.com/avanti/whitepaper.htm .

Ateba, B.B., Maredza, A., Ohei, K., Deka, P. & Schutte, D., 2015, 'Marketing mix: it's role in customer satisfaction in the South African banking retailing', Banks and Bank Systems (open-access) 10(1), 82-91.         [  Links  ]

Bhasin, M.L., 2015, 'An empirical study of frauds in the banks', European Journal of Business and Social Sciences 4(7), 1-12.         [  Links  ]

Brooks, D.J., 2010, 'Assessing vulnerabilities of biometric readers using an applied defeat evaluation methodology', Paper presented at the Proceedings of the 3rd Australian Security and Intelligence Conference, Perth, November 30, 2010.

Board of Governors of the Federal Reserve System, Consumers and Mobile Financial Services, 2016, Board of Governors of the Federal Reserve System Washington, DC, pp. 1-86.

Bonneau, J., Herley, C., Van Oorschot, P.C. & Stajano, F., 2015, 'Passwords and the evolution of imperfect authentication', Communications of the ACM 58(7), 78-87.         [  Links  ]

Bosupeng, M., 2017, 'How Relevant Are Academic Degrees In The Workplace?', Munich Personal RePEc Archive, MPRA Paper No. 77914. pp. 2-7.

Bruun, A., Jensen, K. and Kristensen, D., 2014, 'Usability of single-and multi-factor authentication methods on tabletops: a comparative study', in International Conference on Human-Centred Software Engineering, Springer, Berlin, Heidelberg, pp. 299-306.

Buddharaju, P., Pavlidis, I. & Manohar, C., 2008, 'Face recognition beyond the visible spectrum', in Advances in Biometrics , pp. 157-180, Springer, London.

Chandran, R., 2014, 'Pros and cons of mobile banking', International Journal of Scientific and Research Publications 4(10), 1-5.         [  Links  ]

Chetty, G. & Wagner, M., 2009, 'Biometric person authentication with liveness detection based on audio-visual fusion', International Journal of Biometrics 1(4), 463-478.         [  Links  ]

Choi, H., Kang, R., Choi, K. & Kim, J., 2007, 'Aliveness detection of fingerprints using multiple static features', in Proc. of World Academy of Science, International Journal of Biological and Medical Sciences , vol. 2, pp. 200-205, Engineering and Technology.

Crowe, M.D., Schuh, S.D. & Stavins, J., 2006, 'Consumer behavior and payment choice: A conference summary', FRB of Boston Public Policy Discussion Paper, (06-1).

Das, S.S., 2018, 'A study of digital banking facilities: With reference to Guwahati in kamrup (metro) district of assam', Journal of Management 5(1), 6-13.         [  Links  ]

Dasgupta, D., Roy, A. & Nag, A., 2016, 'Toward the design of adaptive selection strategies for multi-factor authentication', Computers & Security 63, 85-116.         [  Links  ]

De Farias, S.A., Aguiar, E.C. & Melo, F.V.S., 2014, 'Store atmospherics and experiential marketing: A conceptual framework and research propositions for an extraordinary customer experience', International Business Research 7(2), 87-99.         [  Links  ]

De Winter, J.C., Gosling, S.D. & Potter, J., 2016, 'Comparing the Pearson and Spearman correlation coefficients across distributions and sample sizes: A tutorial using simulations and empirical data', Psychological Methods 21(3), 273-290.         [  Links  ]

Electronic Communications and Transactions Act (ECTA), 2002, South Africa Government Gazette 446(23708), 1-41.

Eze, S.G. & Chijioke, E.O., 2016, 'Public enlightenment education on the acceptance of fingerprint biometric technologies for administration in academic institutions and other organisations', Journal of Education and Practice 7(28), 158-163.         [  Links  ]

Frank, M., Biedert, R., Ma, E., Martinovic, I. & Song, D., 2012, 'Touchalytics: On the applicability of touchscreen input as a behavioral biometric for continuous authentication', IEEE transactions on information forensics and security 8(1), 136-148.         [  Links  ]

Gomez-Barrero, M., Drozdowski, P., Rathgeb, C., Patino, J., Todisco, M., Nautsch, A. et al., 2021, 'Biometrics in the era of COVID-19: Challenges and opportunities', arXiv preprint arXiv:2102.09258 , 1-14.

Gunson, N., Marshall, D., Morton, H. & Jack, M., 2011, 'User perceptions of security and usability of single-factor and two-factor authentication in automated telephone banking', Computers & Security 30(4), 208-220.         [  Links  ]

Hosseini, S.S. & Mohammadi, S., 2012, 'Review banking on biometric in the world's banks and introducing a biometric model for Iran's banking system', Journal of Basic and Applied Scientific Research 2(9), 9152-9160.         [  Links  ]

Jain, A.K., Flynn, P. & Ross, A., 2007, Handbook of biometrics , pp. 1-556, Springer Science & Business Media, New York.

Jain, A.K. & Kumar, A., 2010, 'Biometrics of next generation: An overview', Second Generation Biometrics 12(1), 2-3.         [  Links  ]

Jain, A.K., Ross, A. & Pankanti, S., 2006, 'Biometrics: A tool for information security', IEEE Transactions on Information Forensics and Security 1(2), 125-143.         [  Links  ]

Jaiswal, A.M. & Bartere, M., 2014, 'Enhancing ATM security using fingerprint and GSM technology', International Journal of Computer Science and Mobile Computing (IJCSMC) 3(4), 28-32.         [  Links  ]

Jin, C., Kim, H. & Elliott, S., 2007, 'Liveness detection of fingerprint based on band-selective Fourier spectrum', in International Conference on Information Security and Cryptology, Springer, Berlin, Heidelberg, pp. 168-179.

Kelman, J., 2016, The history of banking: A comprehensive reference source & guide , pp. 1-384, CreateSpace Independent Publishing Platform, California,

Kim, S., 2007, 'Governance of information security: New paradigm of security management', in Computational intelligence in information assurance and security , pp. 235-254, Springer, Berlin.

Koppenhaver, K.M., 2007, Forensic document examination: Principles and practice , pp. 1-389, Springer Science & Business Media, New Jersey.

Kristensen, L.B.K. & Solvoll, M., 2019, 'Digital payments for a digital generation', Nordic Journal of Media Studies 1(1), 125-136.         [  Links  ]

Lee, T., 2016, Biometrics and disability rights: legal compliance in biometric identification programs , U. Ill. JL Tech. & Pol'y, p. 209.

Neumeier, A., Wolf, T., & Oesterle, S. 2017, 'The Manifold Fruits of Digitalization - Determining the Literal Value Behind', in St. Gallen, J.M. Leimeister & W. Brenner (eds.), Proceedings der 13 , Internationalen Tagung Wirtschaftsinformatik (WI 2017), St. Gallen, pp. 484-498.

Pacut, A. & Czajka, A., 2006, 'Aliveness detection for iris biometrics', in Proceedings 40th annual 2006 international carnahan conference on security technology, IEEE, pp. 122-129.

Pallant, J., 2020, SPSS survival manual: A step by step guide to data analysis using IBM SPSS , Routledge, London.

Park, R.C., 2008, 'Signature identification in the light of science and experience', Hastings LJ 59, 1101.         [  Links  ]

Rahman, M.R. and Safeena, P.K., 2016, 'Customer Needs and Customer Satisfaction', In Ramees Rahman (eds.), book: Theeranaipunya - A Capacity Building Training Programme Equipping the Fisher women Youth for the Future , Central Marine Fisheries Research Institute, pp. 259-262, Kochi, India.

Rattani, A. & Derakhshani, R., 2018, 'A survey of mobile face biometrics', Computers & Electrical Engineering 72, 39-52.         [  Links  ]

Rhodes, K.A., 2003, Information security: Challenges in using biometrics , General Accounting OfficeTechnical Report # GAO-03-1137T.

Saunders, M., Lewis, P. & Thornhill, A. 2019, Research methods for business students , 8th edn., Pearson Education Limited, Harlow.

Sukamolson, S., 2000, Conducting and developing a multimedia computer-assisted instruction program for teaching Foundation English IIII , Chulalongkorn University, Language Institute.

Sun, Y., Zhang, M., Sun, Z. & Tan, T., 2017, 'Demographic analysis from biometric data: Achievements, challenges, and new frontiers', IEEE transactions on pattern analysis and machine intelligence 40(2), 332-351.         [  Links  ]

Syler, R. & Baker, E., 2016, Building a framework for the influence of digital content on student course engagement .

Tan, X., Li, Y., Liu, J. & Jiang, L., 2010, 'Face liveness detection from a single image with sparse low rank bilinear discriminative model', in European Conference on Computer Vision, Springer, Berlin, Heidelberg, pp. 504-517.

Ten Have, H. & Gordijn, B. (eds.), 2014, Handbook of global bioethics , vol. 4, Springer, New York, NY.

Toth, B., 2005, 'Biometrics. Biometric Liveness Detection', Information Security Bulletin 10, 291-297.         [  Links  ]

World Health Organization, n.d., Coronavirus disease (COVID-19) pandemic , viewed 16 September 2021, from https://www.who.int/emergencies/diseases/novel-coronavirus-2019 .

Wayman, J.L., Jain, A.K., Maltoni, D. & Maio, D. (eds.), 2005, Biometric systems: Technology, design and performance evaluation , Springer Science & Business Media, London, United Kingdom.

Zhu, Y., Tan, T. & Wang, Y., 2000, 'Biometric personal identification based on iris patterns', in Proceedings 15th International conference on pattern recognition, ICPR-2000, vol. 2, pp. 801-804, IEEE.

Received: 13 May 2021 Accepted: 28 Sept. 2021 Published: 10 Dec. 2021

An official website of the United States government

The .gov means it’s official. Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

The site is secure. The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

• Publications
• Account settings

Preview improvements coming to the PMC website in October 2024. Learn More or Try it out now .

• Advanced Search
• Journal List
• Springer Nature - PMC COVID-19 Collection

Biometric applications in education

Marcela hernandez-de-menendez.

1 Tecnológico de Monterrey, Av. E Garza Sada 2501, 64849 Monterrey, NL Mexico

Ruben Morales-Menendez

Carlos a. escobar.

2 General Motors, Global Research and Development, Warren, MI USA

Jorge Arinez

Educational institutions are acquiring novel technologies to help make their processes more efficient and services more attractive for both students and faculty. Biometric technology is one such example that has been implemented in educational institutions with excellent results. In addition to identifying students, access control, and personal data management, it has critical applications to improve the academic domain's teaching/learning processes. Identity management system, class attendance, e-evaluation, security, student motivations, and learning analytics are areas in which biometric technology is most heavily employed. A literature review is performed to present an overview of biometric technology applications for educational purposes, challenges that must overcome to implement biometric technology, and potentially foreshadowing trends effectively. The future seems promising for biometric technology; the biometric technology market is expected to reach a value of USD 94 billion by 2025 at a compound annual growth rate of 36%. New characteristics are under development for commercial applications, such as vascular pattern recognition, ear shape recognition, facial thermography, odor sensing, gait recognition, heartbeat authentication, brain waves, and human body bioacoustics. The biggest challenge this technology must overcome is security and privacy issues, which must be addressed to fully develop the technology to its full potential. It is desirable that this literature review can provide researchers with a sound vision of the potential that biometric technology will have in education.

Introduction

Educational institutions are transforming themselves to be more flexible and adaptable to respond to actual social demands. They are acquiring novel technologies to help them make processes more efficient and services more attractive for students and faculty (and society). Competency-based teaching is oriented to progressive and gradual learning of knowledge, skills, attitudes, and values, which requires real experiences in the educational process.

Generational changes have rendered it necessary for universities to understand and adapt to student's requirements and expectations. Generation Z (GenZ), born in 1995, grew up during the technological revolution (internet, social networks, etc.). Thus, they are easily linked to the virtual world [ 14 ]. GenZ has some common characteristics: creativity, self-taught, multi-screen, and multi-tasking. Therefore, they represent a challenge for traditional education:

• 47% of GenZ spends more than 3 h a day on a video platform.
• 59% uses YouTube to learn.
• 39% prefer to attend a class with a teacher [ 52 ].

Their pragmatism distinguishes GenZ, adaptability to work conditions, and integration with other generations (Baby Boomers, GenX, and Millennials). This generation brings new challenges for education, new uses of technology (and new technology), new ways of teaching and evaluating, and new digital platforms. These new educational tools and approaches have only been accelerated in response to the Covid-19 pandemic.

Educational institutions have implemented various technological tools, including virtual and augment reality, the internet of things, artificial intelligence, 3D printing, telepresence, blockchain, and biometrics [ 28 ]. However, the last one is gaining the attention of the educational community as biometric identification systems are becoming popular.

Biometric “is the science of analyzing physical or behavioral characteristics specific to each individual to authenticate their identity.” In a few words, it measures the human body. This technology can measure physiological (e.g., fingerprints, vein patterns, iris, retina, the shape of the hand and shape of the hand) and behavioral traits (e.g., voice recognition, gait, gestures, the sound of steps, and signature). A combination of characteristics can also be made as a multimodal biometric, which improves measurement confidence. Such combinations could be face and fingerprint, face and iris, etc. The main benefit of biometric technology is that it collects unique human characteristics for each person [ 64 ].

Biometric systems have characteristics that allow for their use in different domains. Several government initiatives (e-passports, e-driver's licenses, border management, and national identifications) are being implemented using advanced biometrics. The banking sector has improved security by using biometric technologies (fingerprint scanners, facial/voice recognition, etc.) to track its customers. Banks also use biometrics to authenticate and protect customer's financial data; it is a long-term market change. With the Internet of Things (IoT), collecting and storing patient data in hospitals has become easier/safer; however, biometric technology can significantly improve. Biometrics can improve essential aspects such as patient's identity and support with real-time information to offer adequate medical services. Facial recognition, iris, and finger scanners are examples of biometric technologies used to access or control a patient's data.

Regarding the security and privacy of patient data, biometrics helps in monitoring and documentation. The productivity of medical personnel (nurses, doctors, etc.) can also be monitored using biometrics. The hotel industry is adopting this technology to achieve a strategic differential in this highly competitive sector. This technology (fingerprints or facial/voice recognition) allows hotels to efficiently automate guest's identification and access and make their stay more comfortable and safe. Using biometric technology, the entry in the migration processes in airports can be accelerated while maintaining security. Many systems use fingerprint scanners, a very efficient and safe tool. Many retailers rely on biometric technology (facial recognition) to prevent theft or promote customer-related items based on their views and interests in a product (Sharman, n.d.). There are platforms such as elBio that are built to teach students about biometric technology. This system introduces the field through a tutorial, and real interactions with verification and identification processes are performed [ 59 ]. Hand geometry systems are helpful to allow access to university systems [ 51 ].

Biometrics is not a new technology. This science dates back to ancient Egypt, where pharaohs relied on height measurements to verify their identity. They also authenticated the decrees they issued by adding their fingerprint to a document and their signature [ 4 ]. Figure  1 indicates the criteria that must be met for a physiological or behavioral attribute to be used as a biometric trait [ 33 ].

Criteria of a biometric trait

A classification of the characteristics measured with biometric systems is presented in Table ​ Table1 1 [ 68 ]. In addition, feature sets are included between brakes [].

Classification of biometric characteristics [feature set]

The bold words are use to highlight the biometric characteristics

Biometric technology is also used for recognizing individual human identities, including DNA, retinal, iris, voice, facial images, fingerprints, and handprints. It scans one of the above characteristics and compares them with stored data. The most common features used are fingerprints and iris. Fingerprints are likely a standard method because fingerprint scanners are relatively inexpensive [ 40 ]. Fingerprint scanners are applied in everyday activities, such as access control and vehicle driver license registration. Research in iris biometrics has accelerated since 2001, with its peak occurring in 2006.

Features such as the face, voice, and retina are also employed. Face biometrics is fast, inexpensive, and non-intrusive. It is also a robust technology that is not heavily influenced by the environment. Researchers have also been working on voice systems to understand the personal encoding of natural languages. In addition, voice biometrics can work with standard telephones, making it a versatile option [ 77 ].

Behavioral biometrics provide several advantages over traditional biometric technologies (iris, fingerprint, handprints, etc.). They can be collected non-obtrusively or even without the knowledge of the user. The collection of behavioral data often does not require any specialized hardware and is very cost-effective. While most behavioral biometrics are not unique enough to provide reliable human identification, they have been shown to give sufficiently high accuracy identity verification. Unfortunately, this type of biometric is not as popular as traditional biometric systems. The reason could be that behavioral characteristics are not exact enough to identify people. However, they have many advantages, such as analyzing people in their routine activities and collecting information that includes a time dimension. The behavior is quantified, and results are used to verify the identity of the person. Some behavioral biometric examples include facial features, email behavior, game strategy, lip movement, mouse dynamics, painting style, programming style, signature/handwriting, storage activity, and systems calls [ 75 ]. Biometrics can also be used for verification or identification processes. In the former, a person that claims identity is verified or rejected. An unknown sample is matched with the stored data [ 5 ].

A basic biometric system is a pattern recognition system that matches the acquired image's salient or discriminatory features with stored image's features. It comprises five main modules, contained in Fig.  2 [ 19 , 68 ].

Composition of a biometric system

A biometric system works in verification or identification mode. In the verification mode (or positive recognition), a user submits the biometric trait and particular identity to the confirmation system. The recognition system validates or voids the user’s claim by comparing (one-to-one) the proposed biometric feature and stored biometric feature associated with the identity. In identification mode (or negative recognition), the system tries to recognize the user by comparing the submitted biometric feature to all the stored biometric features in the database by making (one-to-many) comparisons without a specific identity claim.

The recognition systems based on the measurement of a single biometric modality cannot guarantee 100% accuracy, as shown in Table ​ Table2 2 [ 68 ]. This is due to several factors: (1) noisy data, (2) class variation, (3) distinctiveness, (4) non-universality, and (5) false identity. One solution to this challenge is to use multiple and independent biometric modalities that offer more robustness in precision. A multimodal biometric system may involve multi-sensors, multi-algorithms, multi-instances, multi-sample, multimodal systems. This approach could be serial or cascade mode and with different fusion levels: sensor, feature, matching score, or decision.

Standard accuracy for different biometric modalities

Multimodal processes are done through different devices/sensors that can be used to collect biometric data; these include: (a) cameras for facial or iris recognition, (b) scanning devices for fingerprint recognition, (c) smartphones and laptops for fingerprint recognition, (d) telephones and microphones for voice recognition, (e) special tablets and a digital pen for signature recognition, and (f) keyboard device for keystroke dynamics [ 15 , 16 ].

The biometric market is expected to reach a value of USD 94 billion by 2025, at a compound annual growth rate of 36%, during the next 5 years. This is due to a paradigm shift in commercial discourse, seeking new comprehensive solutions, more privacy, and fewer security threats. Vendors of biometric solutions include: JayPeetek Inc., SecuGen ® Biometric Solutions, onClick® Corp, etc. which offer biometric mice [ 40 ], Gladstone Education offers identity management systems; Squidcard provides a cashless payment system using fingerprint; Voice Commerce offers voice authentication services [ 23 ], and Newcastle-based ievo Ltd is a producer of advanced biometric fingerprint readers who is very demanded by schools and also nurseries [ 18 ]. The benefits of using biometric systems vary, including increased security, increased convenience, reduction of fraud, and delivery of enhanced services [ 77 ].

A literature review is performed to present an overview of biometric technology applications for educational purposes. The paper outline is as follows: Sect.  2 describes the applications biometrics offers in the academic field. Section  3 presents the challenges that must be overcome to implement the technology under consideration effectively. Section  4 presents the trends that are foreseeing. Finally, Sect.  5 concludes the paper.

Applications in education

Biometrics technology is gaining momentum. In 2001 the MIT Technology Review considered biometrics a world-changing emerging technology [ 59 ]. Biometric technologies are disrupting several industries and sectors. General applications include its use for recreational activities, such as in Disneyland. It can also be used for replacing password systems. Innovative devices have also been developed, such as a mouse that recognizes the fingerprint of its owner. ATMs also use this technology [ 40 ].

There is also another exciting field of biometrics, in which the emotional and cognitive state of people are detected. This can be used to monitor student's behavior/emotions and change the educational process appropriately. For example, researchers argue that boredom negatively influences learning, whereas engagement improves learning outcomes; biometric sensors have been used to measure electrodermal activity, skin temperature, and heart rate, all good predictors of emotions [ 73 ]. Biometrics allows academic institutions to save time, money and also improve educational and non-educational activities. They also offer convenience, safety, and security. Various applications are identified: school access, control of attendance, food service, access to library and media center, bus transportation, control staff time, among others (Fry and Dunphy, n.d.). In addition to identifying students, access control, and personal data management, it has critical applications to improve teaching/learning processes in the educational domain, Fig.  3 .

Biometrics in the educational domains

Identity management system

Biometric person recognition systems share many issues and challenges with other pattern recognition applications like video surveillance, speech technologies, human–computer interaction, data analytics applications, behavioral modeling, or recommender systems. Identity Management Systems ( IMS ) are platforms where a permission device gives access to a specific service. These are used in education to provide access to a given product or service or for electronic registration. Passwords are very unsafe and sometimes created to be easy to remember. Biometrics are used in this area for security reasons, as it can grant access to a given system to only authorized persons, identified by their physical or behavioral characteristics [ 59 ]. Fingerprint cards are already used in schools for students that acquire free meals in coffee shops [ 23 ]. Indeed, the use of fingerprints is a common practice from elementary school to universities/research centers.

Nita and Mihailescu [ 48 ] proposed a secure e-learning system based on biometric authentication and homomorphic encryption exploiting cloud computing. Additionally, this proposal predicts if he/she would pass a final exam based on past data of the user’s behavior (using biometrics data).

Class attendance

With biometrics, the attendance of students to a given class can be accelerated. This is advantageous since time devoted to taking attendance is reduced. Also, a more accurate registration process can be performed, diminishing errors [ 9 ]. This technology also enables identifying causes and patterns of absence, and the behavioral characteristics of students can be correlated with class achievement. Analysis of absence between year groups and groups of individuals can also be performed. Universities can also use this technology to track students [ 60 ].

The University of Sunderland London Campus is already using this technology to report class attendance. They use a portable device that has a fingerprint sensor. When students enter the class, they put their fingers on the device to easily register their presence. In India, Delhi University uses this system to track professors’ attendance to class [ 10 ]. In addition, researchers used a biometric fingerprint device to improve active class participation in those classes that consist of a pure lecture. It was combined with a rewarding activity resulting in improved student engagement and class results [ 32 ]. Finally, this technology can also be used in online activities or education to manage time effectively [ 29 ].

e-Evaluation

The submission of e-exams is a relatively new use of biometrics (i.e., since 2017). However, research in this area has been headed by international organizations such as the European Union with its project Adaptive trust-based E-system Assessment for Learning. In these projects, 17 European organizations use keystroke and facial recognition technologies to identify university student's identities and reduce cheating [ 25 ].

Fingerprint recognition can be used for students to take online exams. In this case, learners must verify their identity, and only after that, the exam can be shown on the screen. This can be done by identifying them through facial, iris, or voice recognition. Either the characteristic used, a student sample must be first taken to perform the matching process [ 15 , 16 ].

Biometric systems (finger scans) to verify the identity of IELTS test takers have been implemented globally. As a result, IELTS is available in over 900 locations in 130 countries, making the British Council and IDP IELTS [ 10 ].

Biometrics is a useful technology to identify students and ensure no outsiders either in class or on the university’s campus. On the other hand, this technology can be used in combination with surveillance cameras to detect strangers. In addition, a blocking protocol can be activated in an emergency on campus to ensure that no one enters or leaves. Using identification chips (based on radio frequency) and combining them with intelligent data, students can be located to guarantee their safety. Biometric systems can also limit access to computers, emails, websites, and other restricted educational tools (assessments) [ 29 ]. Finally, students’ presence on campus can be tracked by knowing at any time where they are, when they arrived and left, and where they went [ 18 ].

Higher Education is becoming one of the most popular targets for cyberattacks because universities have relatively open networks. For example, universities have several wireless networks that connect their areas using multiple bandwidths; these multiple networks lead to an output that contains student data (payment information, social security number, personal addresses, etc.). Additionally, universities must comply with various laws to protect student data; these law's guidelines may restrict the institution's IT infrastructure or leave it vulnerable to hackers (Bio-KeyTM, n.d.).

The computer industry is changing rapidly, and people are pushing toward a new technological system buying the latest editions of phones and computers. However, inexperienced users coming into technology rapidly, many of them could unintentionally expose information without realizing it. An inexperienced user can potentially be subject to scams, spoofing, and phishing because the university network allows hackers to enter and exit a system without being detected swiftly. Educational institutions establish an open network architecture with multiple access points; if someone misplaced their cellular phone, a hacker could potentially log into the system and access the entire mainframe.

If intellectual property (patents, documented permission) is stolen, it could cost the institution a large amount of money. Faculty and student personal identification are available; it includes healthcare, credit card/payment, etc. Also, students and faculty have highly sensitive data (bank accounts, personal addresses, etc.). There are many government regulations that Higher Education institutions must follow. Still, in doing so, they expose themselves to a possible attack that they cannot stop due to the regulations. Cyber attacks in Higher Education date back to 2002, Table ​ Table3 3 [ 41 ]. The hackers’ goal remains personal data, social security numbers, financial information, opening up a new credit card, collecting tax refund, etc.

History of cyber attacks in higher education

Biometrics enable body-based security—a technology that authenticates identity based on physical characteristics such as fingerprints, irises, facial structure, voice, and even gestures. Fingerprint authentication (the most commonly used biometric technology) is based on a unique set of identification. Instead of using a passcode, only one person can log into a system using this biometrics technology. Students can start using their fingerprints as their credentials for several reasons: (a) the authentication rarely fails, (b) fingerprints do not rely on memory, (c) rapid system recognition of the fingerprint. By establishing fingerprint authentication, biometrics can protect network architecture and access to other areas. Biometrics is going to be the most powerful technology against cyber-attacks in Higher Education.

Understand students’ motivation and academic progress

In addition to knowing the course's educational content, a good teacher must know their students very well and identify their cognitive status to guide the teaching–learning process properly. For example, if the teacher determines the student's commitment or motivation, he can use different educational strategies to optimize the teaching process. However, when the educational process is done remotely, and for many students, automated system's support is required. This is where biometric technology opens up an excellent opportunity to develop strategies that help detect student's cognitive states.

Due to their influence on learning, emotional states play a crucial role in education in general. Boredom has been shown to influence learning, while engagement can positively improve learning outcomes. Frustration and confusion can positively affect learning if the student can resolve these states. Estimating prediction in real-time of student's affective states is a research topic of great interest due to its benefits through different intervention strategies [ 24 ]. The collection of appropriate biometric data and the analysis of physiological and behavioral patterns during a learning experience can help introduce proper interventions to improve the learning experience as the main hypotheses in this domain.

Biometrics provides an objective measure of the physiological reactivity of users that is used to infer affective states. Electrodermal activity, skin temperature, and heart rate showed high performance as predictors of emotions [ 34 , 35 , 55 ]. Wampfler et al. [ 73 ] predict a student's affective states (while solving math exercises) using arbitrary writing and drawing assignments (based on stylus data).

A low-cost mobile setup to detect student's affective states (non-intrusive and minimum issues related to privacy) is proposed. The system considers bio-sensor data from skin conductance, heart measures, and skin temperature with handwriting data recorded by a stylus to predict student's affective state in a valence-arousal space of emotions proposed [ 54 ]. Valence describes how much emotion is perceived as positive/negative, and arousal represents the emotion's intensity. The circumplex model has two dimensions representing affective states in terms of valence/arousal. The circumplex model has the leading eight affective states (Arousal-0o, Excitement-45o, Pleasure-90o, Contentment-135o, Sleepiness-180o, Depression-135o, Misery-270o, and Distress-315o). The pleasantness-unpleasantness and arousal-sleep dimensions account for the significant proportion of variance; the dimensions of the effects are bipolar; any effect could be defined as combining pleasure and arousal components. Recorded stylus and bio-sensor data are preprocessed, and the relevant features are extracted to train a classification model (using the Random Forest algorithm) for the specific affective regions. Early results are very promising and practical; however, more experiments and validations are required to have overall effects in other knowledge and educational settings.

Dafoulas et al. [ 13 ] used a range of sensors measuring critical data from individual learners, including heartbeat, emotion detection (anger, disgust, fear, happiness, sadness, and surprise), sweat levels, voice fluctuations, and duration/pattern of contribution via voice recognition. Employing biometrics for supporting assessment, facilitating, and enhancing learning experiences in collaborative learning.

Smart biosensors and cameras (infrared) can identify and track students, diagnose their behavioral state (body language and eye contact) and their peer's actions. This can aid in making opportunely changes in the teaching–learning processes and improve students’ results. Also, online student engagement diagnosis will help teachers use the needed teaching strategies and technologies to optimize student's learning.

Many scientific projects have been developed to investigate and estimate student's cognitive states during the teaching/learning processes, based on data and evidence using biometric technology. With this information, the educational process can be optimized. Preliminary results are reasonable; however, research must continue to obtain general, practical, and valid conclusions in different domains. Based on biometric technology, it is possible to assess a student's academic progress and customize strategies to help him achieves his goals [ 29 ].

Biometrics in learning analytics

Learning Analytics is defined as measuring, collecting, analyzing, and reporting data about students and their learning contexts to understand and optimize the learning process and the learning environment [ 58 ]. Learning Analytics is an area of technology-enhanced learning, Big data, cloud technologies, virtual reality, brain-computer interface are some of the technologies that powered Learning Analytics .

LA benefits are: (1) prediction of student performance, (2) personalized student experience, (3) student confinement increases, (4) improves e-learning systems, (5) enhances cost-efficiency. To achieve these Learning Analytics benefits, it is necessary to collect data from various sources. Many data sources vary among many fields, including written or online surveys, interviews, students’ opinions, improvements suggestions, web tools, and more sensitive data: biometric data. The focus is on biometric data due to the recent technologies and devices that enable collecting and analyzing such data [ 15 , 16 ].

Social Network Analysis, GISMO (student-monitoring tool), CourseVis (learning management system), Contextualised Attention Metadata, LOCO-Analyst, Social Networks Adapting Pedagogical Practice ( SNAPP ), Honeycomb, Gephi, sense.us, Signals, and GRAPPLE Visualisation Infrastructure Service ( GVIS ) are some of the Learning Analytics tools that have been developed over time [ 20 ].

Secure access to information and data privacy of learners’ data shall be provided as one of the essential issues that biometric-based systems can quickly solve. Brain-Computer Interface ( BCI ) is a direct communication channel between the human brain and a computer. BCI is a Human–Computer Interface branch oriented towards research human cognitive, sensorial, or motor functions [ 38 ]. EEG -based BCI can help focus student attention and memory retention. It can also measure affective states and adapt the challenge difficulty to the learner’s emotions or even measure the engagement level. This area can improve learning and opens up a large area of research to help people with disabilities.

Over time, tracing Learning Analytics development highlights a gradual shift away from a technological focus towards an educational focus. Factors driving the growth of Learning Analytics :

• Big data : Significant amounts of learner activity take place, and records are distributed across a variety of different sites with different standards, owners, and levels of access.
• Online learning : Learning online offers many benefits, but it is also associated with problems.

The growth of biometric systems has exponentially developed in education and other application areas; however, some opportunities and challenges must be considered to ensure good results.

Users reluctant to use the technology

As biometrics is relatively new, users might be reluctant to use it. It could pose various problems such as wide variance when measuring characteristics, affecting the system's performance. Cultural and social issues must be taken into consideration when designing a biometric system. If not, any initiative can fail. People could be reluctant and fearful of using these systems due since they collect personal information. Also, people may be afraid of acquiring a disease if using, e.g., a fingerprint apparatus (because of the Covid19 pandemic). Some persons might be fearful of how their photographs taken from recognition systems will be managed. Biometric systems that track employee's movement seem beneficial for the employer but intrusive for the worker [ 51 ].

People that can develop solutions and use the technology

For this technology to advance, it is necessary to educate future engineers in this technology well. This is important for developing commercial products that are attractive, useful, and at the same time, solve social problems. This could be achieved by incorporating knowledge regarding the technology in the curriculum of Information Technology universities programs. Several universities are already doing their part. For example, West Virginia University offers the first undergraduate program in biometrics; people who acquire a Bachelor of Science degree in Biometric Systems.

On the other hand, the University of Hertfordshire in the UK has already created a Master’s Degree course in Biometric and Cybersecurity. However, as this is a new field of study, it isn't easy to transmit knowledge effectively. Therefore, some researchers suggest the use of simulation tools.

Confiability

Biometric recognition is based on statistics; i.e., it does not perform a yes or no decision but provides a match score. This can cause the system to present errors given access to an incorrect person [ 59 ]. Environmental issues can also cause errors in the system. Temperature, humidity, and illumination conditions are some of the factors that can influence a biometric system to fail. In physiological recognition, there can be errors during the process, e.g., a student's voice can be altered due to sickness or a noisy environment during the recognition. Performance errors that can be mentioned include computing good quality images, the composition of the population, and the robustness of recognition algorithms [ 68 ].

Privacy issues

Biometric systems have the potential to collect a vast amount of information about people. Privacy is a concern that students and parents have regarding the use of biometric systems in schools. For example, finger and palm print biometric can track individual's dietary habits, which can be intrusive. In 2002, schools in the UK began to scan fingerprints of children without asking for parental consent. These are some of the events that make the academic community have fear mainly about the uses that the data collected will have [ 9 ]. However, there are biometric systems that do not necessarily store images of the characteristics being used. For example, in fingerprint recognition, a number is given to a student's fingertip when the first pass in the biometric device. The number is again generated for subsequent reads and compared with the numbers stored, identifying the student if there is a match [ 60 ]. In e-learning, where a person can learn at every moment, it is crucial to ensure that the personal data collected is safe and provides a personalized experience [ 15 , 16 ]. Therefore, biometric systems should incorporate privacy-protective solutions in their architecture. These should allow a system to work appropriately and give the users confidence and keep personal information safe [ 77 ].

Improvement of biometric technology

Biometric sensors should provide high-quality data that meet standards in mobile and challenging environments. Also, these should work by collecting data from faraway places, i.e., at a distance. Biometric systems should be increasingly accurate regardless of the environment in which they are doing [ 77 ]. Matching algorithms should also be improved, so they work in the presence of imperfect segmentation, noisy features, and signal variance. On the other hand, the technology should be robust enough to face attacks that include falsified biometric traits [ 51 ]. These systems are vulnerable due to their components, such as capture devices, communication channels, and databases. It is essential to improve these issues to work efficiently enough [ 62 ].

The new reality after CoViD19

Humanity before the Covid19 lockdown now seems to have passed a lifetime, and the psychological impact of what we are currently experiencing is likely to be enormous, even incalculable proportions. Facial recognition was promoted as one of the significant changes brought about by the coronavirus pandemic. To counter-balance identification problems due to partial face concealment, biometric Machine Learning and Artificial Intelligence algorithms are being pushed to the next evolutionary step [ 11 ].

CovidTech is a new technology and data movement due to the coronavirus pandemic. CovidTech constitutes the broadest and most profound period of agile technology and data development involving personal data processing.

There are two critical issues at CovidTech : epidemiology and exit strategies from the lockdown. Facial recognition is a crucial part of CovidTech . For example, buses in China have facial recognition systems that are combined with bus thermometers. Russia redesigned its facial recognition technology. Apps are being developed in Poland and India to allow people to upload selfies to contribute to the facial recognition system. Contact tracing is a proven epidemiological technique that has worked successfully in other health emergencies. These are only some examples of the CovidTech solutions that have potential in Higher Education Institutions , but there are certainly some contact-based Biometrics that no have room now. However, there are legal issues of use and scope that have not yet been resolved (Room, 2020).

Some societal and ethical issues

Technology improves the limits of our abilities but challenges our moral boundaries. Biometrics technology is part of our day; for example, through brain implants, social networks like Facebook, etc., using big data and emotion recognition, some systems learn to behave like humans [ 69 ].

Biometric systems allow the use of unique physical characteristics for verification or identification purposes. But, they invade our socio-cultural world, generating some social and ethical issues. Today, most public and political interests focus on privacy (especially personal data protection) and digital security. An analysis of the scientific literature on biometric systems revealed several recurring themes: privacy, security, autonomy, justice, human dignity, control of technology, and balance of powers. The various ethical and social problems manifest themselves in different ways. We will briefly comment on some examples to raise awareness of this issue (Royakkers et al., 2018).

Concerning privacy, biometric technology requires a minimum amount of information to determine if someone has the right to enter a building. On the other hand, because biometrics can identify sensitive data, controlling what happens to that information can be difficult, especially now that the technology has reached the stage of being applied in many more devices and situations. In the example of access to a building, the identity is not revealed; the verification can also be done by comparing someone's biometric characteristics with the information already stored about that person. Thus, biometrics is an excellent way to demonstrate legitimacy while maintaining privacy.

One application of biometrics is facial recognition and identification used in police investigations; this is regulated by law. This highly sensitive information must be stored securely, as biometric data can contain health (risk) and ethnicity. You don't want an insurance company to get hold of the information. An iris scan can determine diabetes or high blood pressure. Fingerprint irregularities can indicate leukemia or breast cancer.

The next generation of biometrics not only gives an idea of "who are you" but also answers the question “how do you feel” [ 45 ]. Emotion recognition technology provides information about people's mental state by examining people's automatic non-verbal comments without knowing it. For example, specific walking, grimacing, and other facial expressions can reveal something about a person and their behavior [ 17 ]. Unfortunately, a biometric system can misclassify and stigmatize someone as a terrorist, criminal, or unreliable individual. This can lead to revoking the presumption of innocence, this stigma likely stays with that person [ 61 ].

Biometric educational trends

Fingerprints, faces, and iris are well-known and consolidated biometric technologies; newer biometrics emerging for different applications include higher education. Although some technologies began to develop many years ago, they have not achieved the proper application level. A brief review is presented with some applications, Fig.  4 .

Biometrics educational trends

Vein/vascular pattern recognition

Vascular pattern recognition (vein pattern authentication) uses near-infrared light to reflect images of a person's blood vessels. The vascular pattern of humans is unique and does not change over the years. The potential for using this technology lies in obtaining trans body images and the possible applications of optical computed tomography scanning [ 56 ]. This technology uses subcutaneous blood vessels on the back of the hands or fingers due to their easy access since the near-infrared rays generated by a bank of LEDs can penetrate the skin on the back of the hand. Due to the difference in absorbance of blood vessels and other tissues, reflected near-infrared rays produce a vascular pattern extracted by various image processing techniques. Different feature-rich data (vessel branch points, vessel thickness, and branch angles) are extracted and stored as a template. Some disadvantages of this vein / vascular technology could be that (a) the quality of the captured image can be affected by body temperature, ambient temperature, humidity, uneven heat distribution, heat radiation, the proximity of the vein to the body surface, camera and focus calibration, and (b) this technology is invasive, which can be a significant limitation, especially in times of pandemic.

Compared to other biometric scanners, palm vein recognition technology has reduced false acceptance/rejection rates; improves the speed and security of endpoint contactless authentication [ 50 ], mainly due to the continuous development of the sensors used. A biometric finger vein scanner using infrared light and a CMOS image sensor in a specially designed system to reduce optical background noise in vein images are commercial solutions [ 8 ]. In addition, NASA developed a new subcutaneous structure imager that can locate veins in humans, Compared to the state of the art vein imaging; it has several characteristics [ 72 ].

Ear shape recognition

The use of ear imaging to identify people occurred more than 100 years ago. There are three methods of imaging the ear for identification: (a) taking a photograph, (b) taking marks, and (c) taking thermographic images of the ear. The essential parts of the ear for identification purposes correspond to the outer ear and the earlobe, but the structure and shape of the entire ear are also often necessary. Using the Image Ray Transform algorithm [ 74 ], a clear image of the outer ear can be systematically generated, this image is translated into a series of numbers for an identification process with a 99.6% success rate with several advantages such as (a) the ears are not affected by facial expressions (or the background), (b) the ears are consistent the age of the person, (c) ear imaging is non-invasive (can even be performed at long distance). However, some disadvantages include the transformation of the ears (with the use of jewelry or other accessories), the hair, the headphones, the lenses that cover the ears, the low light conditions, and the different angles of the ears images of ears.

Some commercial applications, such as the biometric system based on recognizing the ear canal's geometry to authenticate smartphone users with hearing aids, demonstrated efficiency of ~ 95% [ 7 ]. When a sound is played in someone's ear, the sound is propagated and reflected and absorbed through the ear canal, all of which produce a unique signature that can be recorded with the microphone [ 21 ]. Other commercial systems indicate that they can distinguish between people based on their acoustic characteristics with an accuracy greater than 99% [ 47 ].

Facial thermography

Facial thermograms are unique to people and could devise methods and systems for identifying them. Thermograms show the amount of infrared energy emitted, transmitted, and reflected by objects. This infrared energy is converted into temperature, which offers an image with this temperature distribution [ 53 ]. Thus, thermography can function as a facial recognition system in which an infrared camera is used to capture the images. In addition, facial thermogram technology is more accurate and robust in different lighting and environmental conditions than video images, using biosensor data to uniquely and automatically identify people.

Thermograms are a non-intrusive human identification technology in which facial recognition using multispectral imaging modalities, such as IR imaging sensors, has become an area of growing interest. Thermal imaging can avoid some common facial recognition challenges based on video images, such as (a) face and skin detection, location and segmentation are more comfortable to perform, (b) within-class variation. Furthermore, it is less since they are not affected by external distortions, (c) invariance to changes in lighting and facial expressions, (d) works in total darkness, and (e) detects costumes (even though the person has makeup).

Additionally, electronic thermography is used more frequently as a non-ionizing and non-invasive alternative for medicine diagnosis [ 36 ]. The vascular heat emissions present on a person's face can provide physiological indicators of the state of health or, where appropriate, the underlying disease.

Odor sensing

Identifying people through odors is not a new idea; Bloodhound dogs have been trained for years in this task, as body odor is a handy identifier. The primary odor contains stable components over time and does not change with food or the person's environment, making it a successful biometric. A person's secondary odor includes characteristics that are present due to dietary or environmental factors. Finally, the tertiary odor contains elements present due to external factors such as soaps and perfumes.

Real sensors have not yet reached the precision of a dog's smell. Thanks to nano and biotechnology, there are still fundamental advances (thanks to nano and biotechnology) with high sensitivity to detect volatile elements in people's body odor [ 37 ]. Even body odor can vary due to illness, food, mood swings, etc. These recognizable patterns in the body odor of each person have an identification error of 15%. This biometric technology is not intrusive, it works even with deodorants and perfumes since it is impossible to reproduce human's smell. Electronic nose research groups have developed some prototypes of noses to smell different odors and aroma types [ 63 ], some have been successfully tested to search for people in risky situations such as earthquakes or other natural disasters. People can also be identified through their unique breath prints; This method is fast and non-invasive [ 43 ].

Gait recognition

Gait recognition is a behavioral biometric technology that identifies people based on their unique gait patterns. Minor variations in gait style can be used as a biometric identifier to identify individuals. Gait recognition correlates Spatio-temporal parameters such as stride length, stride width, gait speed, and cycle time with kinematic parameters such as joint rotation and the joint's average angles, hip, knee and ankle, and the angles of the thigh, trunk, and foot; The length of the stride and the height of the person are also considered. Some research projects show 95% efficiency in identifying people [ 76 ].

There are important commercial applications that developed a gait recognition technique based on a smartphone's information using a dynamic temporal distortion classification algorithm and an artificial neural network as an evaluation system to authenticate people [ 46 ]. Significant research investments have been generated to bring this image-based gait analysis to the market [ 44 ]. However, it is not the only case, several gait recognition systems have been developed, considering it an excellent biometric [ 6 ].

Heartbeat authentication

Heartbeat can not only detect humans, but it can also identify them. This is because the heartbeat follows an irregular pattern that never repeats and is unique to each person [ 3 ]. With an electrocardiograph (ECG), the unique mathematical features underlying this pattern can be extracted, and a secret key generated the system can use that to encrypt the information. Small changes in the initial conditions lead to very different results [ 12 ]. The shape of the heartbeat pattern is affected by several factors (size, shape, and heart position in the body).

The advantages of biometric information are: (a) it is difficult to clone a biometric marker or an ECG, (b) an ECG cannot be recorded without the permission of a person, and (c) it is non-invasive [ 66 ]. On the other hand, some disadvantages are (1) difficulty in capturing heartbeat patterns, (2) transformation with age or exercise response.

NASA has released a new patent for a heartbeat-based biometric system for commercial licenses to enable solution developers and integrators [ 72 ]. Besides, Huang et al. [ 31 ] strategically reuse ECG signals to improve data encryption, security level, and privacy.

Brain waves/activity

Using brain waves as an identifier is relatively new biometrics; it could serve as a security system and verify a person's identity with less than 1% [ 1 ]. However, the noise associated with all the brain signal measurements has made data analysis difficult. Focusing on the waves in the brain area related to reading and word recognition produces a clearer signal that can be measured faster [ 30 ].

These brain signals are generated when people access their semantic memories, which record specific word's meanings. This method could become a more personal and secure authentication alternative [ 39 ]. Additionally, scanned images of brain activity using functional magnetic resonance imaging can act as a signature pattern to accurately identify people. Some techniques measure neural activity through blood flow in the brain while people are awake and mentally active [ 65 ]. One advantage is that it is almost impossible to clone. The downsides are that the technology is intrusive and not easy to use because it is specialized equipment.

Human body bioacoustics

A modulated micro-vibration that travels through our body could capture a person's unique spectral trait [ 49 ]. Sound waves passing through the human body can identify a person with 97% accuracy, analyze properties such as the individual's skin, joint tension, and bone density [ 57 ]. The spectral patterns of bioacoustics do not undergo significant changes over time. Researchers used a transducer to generate vibrations and sound waves that pass through a person's body. The finger is a part of the body that has been used to test this technique. Once sound passes through skin, bone, and other tissues, a sensor picks up the unique bioacoustic signature.

The biometric system can be very successful in identifying people; for example, different individual fingers can be identified. This means that a person must use the same finger used for authentication [ 27 ]. Auditory acoustics can also be used for authentication. The ear has unique characteristics that change from person to person [ 2 ].

Perceptual user interface

It is possible to use gestures to perform human–machine interaction with gesture recognition, a mathematical interpretation of human movements through a computational device. Currently, computing devices and smartphones have gesture recognition systems, games, and virtual reality systems, more interactive and immersive (i.e., Microsoft's Kinect). These systems can also be used to authenticate a user [ 63 ].

Cheiloscopy is a forensic investigation technique that deals with the identification of humans from lip traces. Lip prints are unique and also permanent for every human being. Lip prints between family members (and between twins) generally revealed different patterns with few similar grooves suggesting inheritance in lip prints [ 70 ]. Cheiloscopy has mainly served forensics as an aid for personal identification; This technology has an enormous development [ 63 ].

New platforms

Biometric technology (as well as traditional systems) has been used mainly when the authentication or verification of the person occurs. However, new platforms are growing due to new demands and developing services. Mobile devices are growing, evolving, incorporating the latest technology platforms, and, with this, biometric systems are transforming and expanding. For example, Samsung's Galaxy Tab has incorporated iris recognition technology to access the device. In addition, some portable devices, such as bracelets, measure the user's heartbeat to authenticate purchases made with Mastercard credit cards on the site [ 22 ].

Conclusions

Biometric technology is relatively new and has changed the way identification and authentication processes are performed. It comprises innovative advances that measure the physical and behavioral characteristics of people. Biometrics is now used in various fields, including government, banking, healthcare, and education. The latter has reaped the benefits of biometric options to good effect. In addition, biometrics can be used to perform non-academic and academic activities at universities. Non-academic activities include the use of fingerprints to enter the campus or university dormitories, pay for a cafeteria or library service. Educational activities include monitoring student behavior or emotions to change teaching strategy on time, class attendance, taking electronic tests, analyzing motivations and progress in a course, and conducting learning analysis. However, some challenges need to be solved effectively, including user's reluctance to use technology. People may be afraid of how their personal information will be used.

As this is a new field of study, it could be challenging to provide students with the knowledge to develop such systems. In addition, privacy concerns are the primary concern, as devices can collect all kinds of personal information. Likewise, there is a need to improve biometric technology to make it more robust and develop more innovative solutions that solve society's main problems or add value to the services offered, in this case, in the educational field.

An important aspect to consider is the choice of the appropriate biometric solution for educational institution's needs. Some factors to consider are (Fry and Dunphy, nd): (a) choose a biometric identification system that can be used in multiple areas, that is, students are identified only once and have access to various services at the university (access to campus, cafeteria, classroom) attendance, library, etc.). (b) the biometric system must be scalable, that is, it must work well with a handful of students and increase their number, (c) the system must be compatible with the software that is already installed on campus, (d) before starting the system, make sure it works well; all traits of potential users need to be scanned, (e) consider accuracy and performance, (f) compare different providers, so that selection is based on an informed process, (g) communicate how the system works to all who will use it.

The biometric market is expected to reach $ 94 billion in 2025, at a compound annual growth rate of 36%, over the next 5 years. New features are being developed, such as vascular pattern recognition, ear shape recognition, facial thermography, odor detection, gait recognition, authentication of heartbeat, brain waves, and bioacoustics of the human body. The development of typical artificial intelligence algorithms combined with the technological growth of the Internet of Things (and its impact on Big Data) will be great catalysts for biometric systems to develop widely and impact many areas within them, including education. However, the biggest challenge for this technology to overcome is security and privacy concerns; they must be addressed to harness this technology's full potential.

Finally, biometrics is a technology that can enhance students’ abilities in the context of Industry 4.0. Various interactive technologies fulfill this task. Such technologies are (a) 3D printing which enhances students understanding; (b) Augment reality which is an interactive technology that captures students attention; (c) Virtual Reality enhances students ability to connect theory and practice; (d) Cloud computing can be used to have access to the learning material from anywhere; (e) Holograms offer interactive spaces to learn [ 26 ]; (f) Artificial Intelligence permits students to have a better understanding of a given subject; (g) Robots capture students attention [ 67 ]; (h) Internet of Things allow students to be active learners [ 42 ].

Acknowledgements

The authors would like to acknowledge the financial and technical support of Writing Lab, TecLabs, Tecnológico de Monterrey , México, in the production of this work.

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Contributor Information

Marcela Hernandez-de-Menendez, Email: xm.cet@zednanrehalecram .

Ruben Morales-Menendez, Email: xm.cet@mmr .

Carlos A. Escobar, Email: [email protected] .

Jorge Arinez, Email: [email protected] .

Biometric Technology and Ethics: Beyond Security Applications

• Review Paper
• Published: 08 March 2019
• Volume 167 , pages 433–450, ( 2020 )

Cite this article

• Andrea North-Samardzic 1  

6735 Accesses

22 Citations

6 Altmetric

Explore all metrics

Biometric technology was once the purview of security, with face recognition and fingerprint scans used for identification and law enforcement. This is no longer the case; biometrics is increasingly used for commercial and civil applications. Due to the widespread diffusion of biometrics, it is important to address the ethical issues inherent to the development and deployment of the technology. This article explores the burgeoning research on biometrics for non-security purposes and the ethical implications for organizations. This will be achieved by reviewing the literature on biometrics and business ethics and drawing from disciplines such as computer ethics to inform a more robust discussion of key themes. Although there are many ethical concerns, privacy is the key issue, with associated themes. These include definitions of privacy, the privacy paradox, informed consent, regulatory frameworks and guidelines, and discrimination. Despite the proliferation of biometric technology, there is little empirical research on applied biometrics and business ethics. As such, there are several avenues for research to improve understanding of the ethical implications of using this technology.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price includes VAT (Russian Federation)

Instant access to the full article PDF.

Rent this article via DeepDyve

Institutional subscriptions

Similar content being viewed by others

Qualitative Research: Ethical Considerations

Digital technologies: tensions in privacy and data

Sara Quach, Park Thaichon, … Robert W. Palmatier

The Trustworthiness of Content Analysis

This review was updated in October 2018.

Albrecht, J. P. (2016). How the GDPR will change the world. European Data Protection Law Review, 2 , 287–289.

Google Scholar  

Alterman, A. (2003). “A piece of yourself”: Ethical issues in biometric identification. Ethics and Information Technology, 5 (3), 139–150.

Ball, K. (2005). Organization, surveillance and the body: Towards a politics of resistance. Organization, 12 (1), 89–108.

Bhattacharyya, D., Ranjan, R., Alisherov, F., & Choi, M. (2009). Biometric authentication: A review. International Journal of u-and e-Service, Science and Technology, 2 (3), 13–28.

Breland, A. (2017). How white engineers built racist code—and why it’s dangerous for black people. https://www.theguardian.com/technology/2017/dec/04/racist-facial-recognition-white-coders-black-people-police . Accessed 1 Nov 2018.

Brown, W. S. (1996). Technology, workplace privacy and personhood. Journal of Business Ethics, 15 (11), 1237–1248.

Brusoni, S., & Vaccaro, A. (2017). Ethics, technology and organizational innovation. Journal of Business Ethics, 143 (2), 223–226.

Buchholz, R. A., & Rosenthal, S. B. (2002). Technology and business: Rethinking the moral dilemma. Journal of Business Ethics, 41 (1–2), 45–50.

Campisi, P. (2013). Security and privacy in biometrics (Vol. 24). New York: Springer.

Carpenter, D., McLeod, A., Hicks, C., & Maasberg, M. (2018). Privacy and biometrics: An empirical examination of employee concerns. Information Systems Frontiers, 20 (1), 91–110.

Cavoukian, A., Chibba, M., & Stoianov, A. (2012). Advances in biometric encryption: Taking privacy by design from academic research to deployment. Review of Policy Research, 29 (1), 37–61.

Corcoran, P., & Costache, C. (2016). Smartphones, biometrics, and a brave new world. IEEE Technology and Society Magazine, 35 (3), 59–66.

Custers, B., Dechesne, F., Sears, A. M., Tani, T., & van der Hof, S. (2018). A comparison of data protection legislation and policies across the EU. Computer Law and Security Review, 34 (2), 234–243.

D’Mello, S. K., Craig, S. D., & Graesser, A. C. (2009). Multimethod assessment of affective experience and expression during deep learning. International Journal of Learning Technology, 4 (3–4), 165–187.

D’Mello, S. K., & Graesser, A. (2010). Multimodal semi-automated affect detection from conversational cues, gross body language, and facial features. User Modeling and User-Adapted Interaction, 20 (2), 147–187.

Davis, J., & Nathan, L. P. (2015). Value sensitive design: Applications, adaptations, and critiques. In Handbook of ethics, values, and technological design (pp. 11–40). New York: Springer.

de Vries, R. E., & van Gelder, J.-L. (2015). Explaining workplace delinquency: The role of Honesty–Humility, ethical culture, and employee surveillance. Personality and Individual Differences, 86 , 112–116.

Dhanani, L. Y., Beus, J. M., & Joseph, D. L. (2018). Workplace discrimination: A meta-analytic extension, critique, and future research agenda. Personnel Psychology, 71 (2), 147–179.

Dierksmeier, C., & Seele, P. (2018). Cryptocurrencies and business ethics. Journal of Business Ethics, 152 (1), 1–14.

Dixon, P. (2008). Ethical issues implicit in library authentication and access management: Risks and best practices. Journal of Library Administration, 47 (3–4), 141–162.

Economist, T. (2017). Advances in AI are used to spot signs of sexuality. https://www.economist.com/news/science-and-technology/21728614-machines-read-faces-are-coming-advances-ai-are-used-spot-signs . Accessed 1 Nov 2018.

European Commission. (2018a). What does the General Data Protection Regulation (GDPR) govern? https://ec.europa.eu/info/law/law-topic/data-protection/reform/what-does-general-data-protection-regulation-gdpr-govern_en . Accessed 28 Dec.

European Commission. (2018b). What data can we process and under what conditions? https://ec.europa.eu/info/law/law-topic/data-protection/reform/rules-business-and-organisations/principles-gdpr/what-data-can-we-process-and-under-which-conditions_en . Accessed 29 Dec.

Evans, R., McNamee, M., & Owen, G. (2017). Ethics, nanobiosensors and elite sport: The need for a new governance framework. Science and Engineering Ethics, 23 (6), 1487–1505.

Eyers, J. (2017). Westpac testing AI to monitor staff and customers. Australian Financial Review. November 14. https://www.afr.com/technology/westpac-testing-ai-to-monitor-staff-and-customers-20171114-gzks7h . Accessed 29 Dec 2018.

Fairweather, N. B. (1999). Surveillance in employment: The case of teleworking. Journal of Business Ethics, 22 (1), 39–49.

Grafsgaard, J., Wiggins, J. B., Boyer, K. E., Wiebe, E. N., & Lester, J. (2013). Automatically recognizing facial expression: Predicting engagement and frustration. In Proceedings of the 6th International Conference on Educational Data Mining (pp. 43–50).

Greenwood, M., & Van Buren, I. I. I., H. J (2010). Trust and stakeholder theory: Trustworthiness in the organisation–stakeholder relationship. Journal of Business Ethics, 95 (3), 425–438.

Gregersen, A., Langkjær, B., Heiselberg, L., & Wieland, J. L. (2017). Following the viewers: Investigating television drama engagement through skin conductance measurements. Poetics, 64 , 1–13.

Gurdus, E. (2017). UnitedHealthcare and Fitbit to pay users up to $1,500 to use devices, Fitbit co-founder says. https://www.cnbc.com/2017/01/05/unitedhealthcare-and-fitbit-to-pay-users-up-to-1500-to-use-devices.html . Accessed 4 Nov 2018.

Hassib, M., Schneegass, S., Eiglsperger, P., Henze, N., Schmidt, A., & Alt, F., EngageMeter: A system for implicit audience engagement sensing using electroencephalography. In Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems, 2017 (pp. 5114–5119): ACM.

Henschke, A. (2017). Ethics in an age of surveillance: Personal information and virtual identities . Cambridge: Cambridge University Press.

Herschel, R., & Miori, V. M. (2017). Ethics and big data. Technology in Society, 49 , 31–36.

Holland, P. J., Cooper, B., & Hecker, R. (2015). Electronic monitoring and surveillance in the workplace: The effects on trust in management, and the moderating role of occupational type. Personnel Review, 44 (1), 161–175.

Hoofnagle, C. J., King, Jennifer, Li, Su, & Turow, J. (2010). How different are young adults from older adults when it comes to information privacy attitudes and policies?. Available at https://doi.org/10.2139/ssrn.1589864 .

Hou, R. (2017). Neoliberal governance or digitalized autocracy? The rising market for online opinion surveillance in China. Surveillance and Society, 15 (3/4), 418–424.

Introna, L., & Nissenbaum, H. (2010). Facial recognition technology a survey of policy and implementation issues. Lancaster University Management School Working Paper (Vol. 2010/030): Lancaster University.

Jain, A. K., Hong, L., & Pankanti, S. (2000). Biometric identification. Communications of the ACM, 43 (2), 90–98.

Jain, A. K., & Kumar, A. (2012). Biometric recognition: an overview. In Second generation biometrics: The ethical, legal and social context (pp. 49–79). New York: Springer.

Jain, A. K., Ross, A. A., & Nandakumar, K. (2011). Introduction. In Introduction to biometrics (pp. 1–49). New York: Springer.

Karkazis, K., & Fishman, J. R. (2017). Tracking US professional athletes: The ethics of biometric technologies. The American Journal of Bioethics, 17 (1), 45–60.

Kirchberg, V., & Tröndle, M. (2012). Experiencing exhibitions: A review of studies on visitor experiences in museums. Curator, 55 (4), 435–452.

Kirchberg, V., & Tröndle, M. (2015). The museum experience: Mapping the experience of fine art. Curator, 58 (2), 169–193.

Klare, B. F., Burge, M. J., Klontz, J. C., Bruegge, R. W. V., & Jain, A. K. (2012). Face recognition performance: Role of demographic information. IEEE Transactions on Information Forensics and Security, 7 (6), 1789–1801.

Kokolakis, S. (2017). Privacy attitudes and privacy behaviour: A review of current research on the privacy paradox phenomenon. Computers and Security, 64 , 122–134.

Li, Y., Kobsa, A., Knijnenburg, B. P., & Nguyen, M. C. (2017). Cross-cultural privacy prediction. Proceedings on Privacy Enhancing Technologies , 2017(2), 113–132.

Loch, K. D., Conger, S., & Oz, E. (1998). Ownership, privacy and monitoring in the workplace: A debate on technology and ethics. Journal of Business Ethics, 17 (6), 653–663.

Lodge, J. (2006). Ethical EU eJustice: Elusive or illusionary? Journal of Information, Communication and Ethics in Society, 4 (3), 131–144.

Lodge, J. (2012). The dark side of the moon: Accountability, ethics and new biometrics. In Second generation biometrics: The ethical, legal and social context (pp. 305–328). New York: Springer.

Lupton, D. (2013). Quantifying the body: Monitoring and measuring health in the age of mHealth technologies. Critical Public Health, 23 (4), 393–403.

Lupton, D. (2016a). The diverse domains of quantified selves: Self-tracking modes and dataveillance. Economy and Society, 45 (1), 101–122.

Lupton, D. (2016b). Personal data practices in the age of lively data. Digital sociologies, 335–350.

Lupton, D. (2016c). You are your data: Self-tracking practices and concepts of data. In Lifelogging (pp. 61–79): Springer.

Lupton, D. (2017). Feeling your data: Touch and making sense of personal digital data. New Media and Society, 19 (10), 1599–1614.

Malsch, I. (2013). The just war theory and the ethical governance of research. Science and Engineering Ethics, 19 (2), 461–486.

Maltseva, K., & Lutz, C. (2018). A quantum of self: A study of self-quantification and self-disclosure. Computers in Human Behavior, 81 , 102–114.

Martella, C., Gedik, E., Cabrera-Quiros, L., Englebienne, G., & Hung, H. (2015). How was it?: Exploiting smartphone sensing to measure implicit audience responses to live performances. In Proceedings of the 23rd ACM International conference on Multimedia (pp. 201–210). Brisbane Australia.

Martella, C., Miraglia, A., Frost, J., Cattani, M., & van Steen, M. (2017). Visualizing, clustering, and predicting the behavior of museum visitors. Pervasive and Mobile Computing, 38 , 430–443.

Martin, A. J., Wellen, J. M., & Grimmer, M. R. (2016). An eye on your work: How empowerment affects the relationship between electronic surveillance and counterproductive work behaviours. The International Journal of Human Resource Management, 27 (21), 2635–2651.

Martin, K. (2016). Understanding privacy online: Development of a social contract approach to privacy. Journal of Business Ethics, 137 (3), 551–569.

Martin, K. (2018). Ethical implications and accountability of algorithms. Journal of Business Ethics , 1–16.

Martin, K., & Freeman, R. E. (2003). Some problems with employee monitoring. Journal of Business Ethics, 43 (4), 353–361.

Martin, K. E., & Freeman, R. E. (2004). The separation of technology and ethics in business ethics. Journal of Business Ethics, 53 (4), 353–364.

McDaniel, B., D’Mello, S., King, B., Chipman, P., Tapp, K., & Graesser, A. (2007). Facial features for affective state detection in learning environments. In D.S. McNamara and J.G. Trafton (Eds.) Proceedings of the 29th Annual Cognitive Science Society , (pp. 467–472), Austin.

McStay, A. (2014). Privacy and philosophy: New media and affective protocol . New York, Peter Lang.

McStay, A. (2016). Empathic media and advertising: Industry, policy, legal and citizen perspectives (the case for intimacy). Big Data and Society, 3 (2), 1–11.

McStay, A. (2018). Emotional AI: The rise of empathic media . Bangor: Sage.

Milligan, C. S. (1999). Facial recognition technology, video surveillance, and privacy. Southern California Interdisciplinary Law Journal, 9 , 295.

Miltgen, C. L., & Peyrat-Guillard, D. (2014). Cultural and generational influences on privacy concerns: A qualitative study in seven European countries. European Journal of Information Systems, 23 (2), 103–125.

Mingers, J., & Walsham, G. (2010). Toward ethical information systems: The contribution of discourse ethics. MIS Quarterly, 34 (4), 833.

Moore, P., & Piwek, L. (2017). Regulating wellbeing in the brave new quantified workplace. Employee Relations, 39 (3), 308–316.

Murry, W. D., Wimbush, J. C., & Dalton, D. R. (2001). Genetic screening in the workplace: Legislative and ethical implications. Journal of Business Ethics, 29 (4), 365–378.

Naker, S., & Greenbaum, D. (2017). Now you see me: Now you still do: Facial recognition technology and the growing lack of privacy. Boston University Journal of Science & Technology Law, 23 , 88.

Nissenbaum, H. (2004). Privacy as contextual integrity. Washington Law Review, 79 , 119–158.

Nissenbaum, H. (2009). Privacy in context: Technology, policy, and the integrity of social life . Stanford: Stanford University Press.

Nissenbaum, H. (2011). A contextual approach to privacy online. Daedalus, 140 (4), 32–48.

NIST (2018). National Institute of Standards and Technology Face Recognition Vendor Test. https://www.nist.gov/programs-projects/face-recognition-vendor-test-frvt . Accessed 4 Nov.

Norval, A., & Prasopoulou, E. (2017). Public faces? A critical exploration of the diffusion of face recognition technologies in online social networks. New Media and Society, 19 (4), 637–654.

Nunan, D., & Di Domenico, M. (2017). Big data: A normal accident waiting to happen? Journal of Business Ethics, 145 (3), 481–491.

Odoherty, K. C., Christofides, E., Yen, J., Bentzen, H. B., Burke, W., Koenig, N. H. A., et al. (2016). If you build it, they will come: Unintended future uses of organised health data collections. BMC Medical Ethics, 17 (54), 1–16.

Park, Y. J. (2013). Digital literacy and privacy behavior online. Communication Research, 40 (2), 215–236.

Park, Y. J., & Skoric, M. (2017). Personalized ad in your Google Glass? Wearable technology, hands-off data collection, and new policy imperative. Journal of Business Ethics, 142 (1), 71–82.

Pentina, I., Zhang, L., Bata, H., & Chen, Y. (2016). Exploring privacy paradox in information-sensitive mobile app adoption: A cross-cultural comparison. Computers in Human Behavior, 65 , 409–419.

Prabhakar, S., Pankanti, S., & Jain, A. K. (2003). Biometric recognition: Security and privacy concerns. IEEE Security and Privacy, 99 (2), 33–42.

Royakkers, L., Timmer, J., Kool, L., & Rinie van, E. (2018). Societal and ethical issues of digitization. Ethics and Information Technology, 20 (2), 127–142.

Schuelke-Leech, B. A. (2018). A model for understanding the orders of magnitude of disruptive technologies. Technological Forecasting and Social Change, 129 , 261–274.

Schumacher, G. (2012). Behavioural biometrics: Emerging trends and ethical risks. In Second Generation Biometrics: The Ethical, Legal and Social Context (pp. 215–227). New York: Springer.

Sheehy, B. (2015). Defining CSR: Problems and solutions. Journal of Business Ethics, 131 (3), 625–648.

Shi, X., & Wu, X. (2017). An overview of human genetic privacy. Annals of the New York Academy of Sciences, 1387 (1), 61–72.

Smith, G. J. D., San Roque, M., Westcott, H., & Marks, P. (2013). Surveillance texts and textualism: Truthtelling and trustmaking in an uncertain world. Surveillance and Society, 11 (3), 215–221.

Soleymani, M., Asghari-Esfeden, S., Pantic, M., & Fu, Y. Continuous emotion detection using EEG signals and facial expressions. In Multimedia and Expo (ICME), 2014 IEEE International Conference on, 2014 (pp. 1–6): IEEE**.

Sprokkereef, A., and de Hert, P. (2012). Biometrics, privacy and agency. In Second generation biometrics: The ethical, legal and social context (pp. 81–101). New York: Springer.

Sud, M., & VanSandt, C. (2015). Identity rights: A structural void in inclusive growth. Journal of Business Ethics, 132 (3), 589–601.

Sutrop, M., & Laas-Mikko, K. (2012). From identity verification to behavior prediction: Ethical implications of second generation biometrics. Review of Policy Research, 29 (1), 21–36.

Taddicken, M. (2014). The ‘privacy paradox’in the social web: The impact of privacy concerns, individual characteristics, and the perceived social relevance on different forms of self-disclosure. Journal of Computer-Mediated Communication, 19 (2), 248–273.

Trocchia, P. J., & Ainscough, T. L. (2006). Characterizing consumer concerns about identification technology. International Journal of Retail and Distribution Management, 34 (8), 609–620.

Ulman, Y. I., Cakar, T., & Yildiz, G. (2015). Ethical issues in neuromarketing: “I Consume, Therefore I am!”. Science and Engineering Ethics, 21 (5), 1271–1284.

Unar, J., Seng, W. C., & Abbasi, A. (2014). A review of biometric technology along with trends and prospects. Pattern Recognition, 47 (8), 2673–2688.

Van der Ploeg, I. (2003). Biometrics and privacy: A note on the politics of theorizing technology. Information, Communication and Society, 6 (1), 85–104.

Wang, C., & Cesar, P. Do we react in the same manner?: Comparing GSR patterns across scenarios. In Proceedings of the 8th Nordic Conference on Human-Computer Interaction: Fun, Fast, Foundational , 2014 (pp. 501–510): ACM.

Wang, C., & Cesar, P. The play is a hit: But how can you tell? In Proceedings of the 2017 ACM SIGCHI Conference on Creativity and Cognition , 2017 (pp. 336–347): ACM.

Wang, C., Geelhoed, E. N., Stenton, P. P., & Cesar, P. Sensing a live audience. In Proceedings of the 32nd annual ACM conference on Human factors in computing systems , 2014 (pp. 1909–1912): ACM.

Wang, C., Wong, J., Zhu, X., Röggla, T., Jansen, J., & Cesar, P. Quantifying audience experience in the wild: Heuristics for developing and deploying a biosensor infrastructure in theaters. In Quality of Multimedia Experience (QoMEX), 2016 Eighth International Conference on IEEE , 2016 (pp. 1–6).

Webb, A. M., Wang, C., Kerne, A., & Cesar, P. Distributed liveness: Understanding how new technologies transform performance experiences. In Proceedings of the 19th ACM Conference on Computer-Supported Cooperative Work and Social Computing , 2016 (pp. 432–437): ACM.

West, J. P., & Bowman, J. S. (2016). Electronic surveillance at work: An ethical analysis. Administration and Society, 48 (5), 628–651.

Whitehill, J., Serpell, Z., Lin, Y.-C., Foster, A., & Movellan, J. R. (2014). The faces of engagement: Automatic recognition of student engagementfrom facial expressions. IEEE Transactions on Affective Computing, 5 (1), 86–98.

Winter, J. S. (2014). Surveillance in ubiquitous network societies: Normative conflicts related to the consumer in-store supermarket experience in the context of the Internet of Things. Ethics and Information Technology, 16 (1), 27–41.

Winter, S. J., Stylianou, A. C., & Giacalone, R. A. (2004). Individual differences in the acceptability of unethical information technology practices: The case of Machiavellianism and ethical ideology. Journal of Business Ethics, 54 (3), 273–301.

Zarsky, T. Z. (2017). Incompatible: The GDPR in the age of big data. Seton Hall Law Review, 47 (4), 995–1020.

Zhao, W., Chellappa, R., Phillips, P. J., & Rosenfeld, A. (2003). Face recognition: A literature survey. ACM Computing Surveys, 35 (4), 399–458.

Zwitter, A. (2014). Big data ethics. Big Data and Society, 1 (2), 1–6.

Wickins, J. (2007). The ethics of biometrics: the risk of social exclusion from the widespread use of electronic identification. Science and Engineering Ethics, 13 (1), 45–54.

Finn, R. L., Wright, D., & Friedewald, M. (2013). Seven types of privacy. In European data protection: coming of age (pp. 3–32). Dordrecht: Springer.

Jain, A. K., Ross, A., & Prabhakar, S. (2004). An introduction to biometric recognition. IEEE Transactions on circuits and systems for video technology, 14 (1), 1–29.

Download references

Acknowledgements

Feedback from the anonymous reviewers informed this updated version; I greatly appreciate their efforts and useful suggestions in improving the manuscript. I would also like to acknowledge the research assistance of Ishan Senarathna. I would also like to thank Nicholas Patterson and Matthew Warren for their feedback and guidance in the early stages.

Funding for this research was provided by the Deakin Business School, Centre for Sustainable and Responsible Organisations (CSaRO).

Author information

Authors and affiliations.

Department of Management, Deakin Business School, Deakin University, 70 Elgar Road, Burwood, VIC, 3125, Australia

Andrea North-Samardzic

You can also search for this author in PubMed   Google Scholar

Corresponding author

Correspondence to Andrea North-Samardzic .

Ethics declarations

Conflict of interest.

I declare that I have no conflict of interest.

Additional information

Publisher’s note.

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

An earlier version of this paper was accepted for the 77th Academy of Management Meeting. 

Rights and permissions

Reprints and permissions

About this article

North-Samardzic, A. Biometric Technology and Ethics: Beyond Security Applications. J Bus Ethics 167 , 433–450 (2020). https://doi.org/10.1007/s10551-019-04143-6

Download citation

Received : 14 July 2018

Accepted : 04 March 2019

Published : 08 March 2019

Issue Date : December 2020

DOI : https://doi.org/10.1007/s10551-019-04143-6

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Biometric technology
• Find a journal
• Publish with us
• Track your research

Academia.edu no longer supports Internet Explorer.

To browse Academia.edu and the wider internet faster and more securely, please take a few seconds to  upgrade your browser .

Enter the email address you signed up with and we'll email you a reset link.

• We're Hiring!
• Help Center

Exploiting Literature review of fingerprint technology in Biometrics

Related Papers

Kolapo A Ige ( Professor) , Prof. Sir Bashiru Aremu

Biometrics can provide a greater degree of security than traditional authentication methods, meaning that resources are accessible only to authorized users and are kept protected from unauthorized users in theory a password is memorized by a single person, it's hard guess it's never written down, and it's never shared. They can offer much greater convenience than systems based on remembering multiple passwords or on keeping possession of an authentication token for PC application in which a user must access multiple resources, biometrics can greatly simple.

Prof. Sir Bashiru Aremu

Prof. Sir Bashiru Aremu , Promise Mebine

RELATED PAPERS

Pattern Recognition

Xudong Jiang , Setsuko Katoh

shiva prasad

Prasad Banoth

Aythami Morales

IEEE Signal Processing Magazine

J. Ortega-garcia

Computer Vision and Image Understanding

Recent Advances in Signal Processing

Gonzalo Bailador-del-pozo

Information Sciences

J. Ortega-garcia , Aythami Morales

Chhaya Methani

Pattern Recognition Letters

Aparrnaa Raghuraman

IJAET Journal

Jordanian Journal of Computers and Information Technology JJCIT , Ezdihar Bifari

Proceedings of the IEEE

Rebecca Heyer

Expert Systems with Applications

Shahrel Suandi

Proceedings 1999 International Conference on Image Processing (Cat. 99CH36348)

IJERA Journal

IEEE Transactions on Information Forensics and Security

IEEE Transactions on Pattern …

Sushmita Chauhan

IEEE Transactions on Signal Processing

Xudong Jiang

Rogerio Enriquez-Caldera

Md Jan Nordin

Andrew Beng Jin Teoh

jayavani kumar

Serge Belongie

neha dahiya

Rex Journal

Rafael Marcos Luque Baena , Ezequiel López-Rubio

International Journal of Biometrics

Mita Nasipuri

Saroj Panigrahy

In proc. of Third COST 275 Workshop (COST 275)

Florian Evéquoz , Catherine Pugin , Jean Hennebert , Dijana Petrovska-delacrétaz

American Journal of Applied Sciences

Associate Professor Dr. Fatma Susilawati Mohamad

•   We're Hiring!
•   Help Center
• Find new research papers in:
• Health Sciences
• Earth Sciences
• Cognitive Science
• Mathematics
• Computer Science
• Academia ©2024