hypothesis on fingerprints

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How fingerprints form was a mystery — until now.

A theory proposed by mathematician Alan Turing in the 1950s helps explain the process

Three of the most common fingerprint shapes — arch, loop and whorl (traced in purple) — can be explained in part by a theory proposed by British mathematician Alan Turing.

J. Glover et al / Cell 2023

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By McKenzie Prillaman

February 9, 2023 at 11:00 am

Scientists have finally figured out how those arches, loops and whorls formed on your fingertips.

While in the womb, fingerprint-defining ridges expand outward in waves starting from three different points on each fingertip. The raised skin arises in a striped pattern thanks to interactions between three molecules that follow what’s known as a Turing pattern, researchers report February 9 in Cell . How those ridges spread from their starting sites — and merge — determines the overarching fingerprint shape.

Fingerprints are unique and last for a lifetime. They’ve been used to identify individuals since the 1800s. Several theories have been put forth to explain how fingerprints form, including spontaneous skin folding, molecular signaling and the idea that ridge pattern may follow blood vessel arrangements.

Scientists knew that the ridges that characterize fingerprints begin to form as downward growths into the skin, like trenches. Over the few weeks that follow, the quickly multiplying cells in the trenches start growing upward, resulting in thickened bands of skin.

Since budding fingerprint ridges and developing hair follicles have similar downward structures, researchers in the new study compared cells from the two locations. The team found that both sites share some types of signaling molecules — messengers that transfer information between cells — including three known as WNT, EDAR and BMP. Further experiments revealed that WNT tells cells to multiply, forming ridges in the skin, and to produce EDAR, which in turn further boosts WNT activity. BMP thwarts these actions.

To examine how these signaling molecules might interact to form patterns, the team adjusted the molecules’ levels in mice. Mice don’t have fingerprints, but their toes have striped ridges in the skin comparable to human prints. “We turn a dial — or molecule — up and down, and we see the way the pattern changes,” says developmental biologist Denis Headon of the University of Edinburgh.

Increasing EDAR resulted in thicker, more spaced-out ridges, while decreasing it led to spots rather than stripes. The opposite occurred with BMP, since it hinders EDAR production.

That switch between stripes and spots is a signature change seen in systems governed by Turing reaction-diffusion, Headon says. This mathematical theory, proposed in the 1950s by British mathematician Alan Turing, describes how chemicals interact and spread to create patterns seen in nature ( SN: 7/2/10 ). Though, when tested, it explains only some patterns ( SN: 1/21/14 ).

Mouse digits, however, are too tiny to give rise to the elaborate shapes seen in human fingerprints. So, the researchers used computer models to simulate a Turing pattern spreading from the three previously known ridge initiation sites on the fingertip: the center of the finger pad, under the nail and at the joint’s crease nearest the fingertip.

By altering the relative timing, location and angle of these starting points, the team could create each of the three most common fingerprint patterns — arches, loops and whorls — and even rarer ones. Arches, for instance, can form when finger pad ridges get a slow start, allowing ridges originating from the crease and under the nail to occupy more space.

“It’s a very well-done study,” says developmental and stem cell biologist Sarah Millar, director of the Black Family Stem Cell Institute at the Icahn School of Medicine at Mount Sinai in New York City.

Controlled competition between molecules also determines hair follicle distribution, says Millar, who was not involved in the work. The new study, she says, “shows that the formation of fingerprints follows along some basic themes that have already been worked out for other types of patterns that we see in the skin.”

Millar notes that people with gene mutations that affect WNT and EDAR have skin abnormalities. “The idea that those molecules might be involved in fingerprint formation was floating around,” she says.

Overall, Headon says, the team aims to aid formation of skin structures, like sweat glands, when they’re not developing properly in the womb, and maybe even after birth.

“What we want to do, in broader terms, is understand how the skin matures.”

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A biochemical hypothesis on the formation of fingerprints using a turing patterns approach

Affiliation.

• 1 Mechanical and Mechatronics Engineering Department, Universidad Nacional de Colombia, Engineering Modeling and Numerical Methods Group (GNUM), Bogotá, Colombia. [email protected]
• PMID: 21711537
• PMCID: PMC3141687
• DOI: 10.1186/1742-4682-8-24

Background: Fingerprints represent a particular characteristic for each individual. Characteristic patterns are also formed on the palms of the hands and soles of the feet. Their origin and development is still unknown but it is believed to have a strong genetic component, although it is not the only thing determining its formation. Each fingerprint is a papillary drawing composed by papillae and rete ridges (crests). This paper proposes a phenomenological model describing fingerprint pattern formation using reaction diffusion equations with Turing space parameters.

Results: Several numerical examples were solved regarding simplified finger geometries to study pattern formation. The finite element method was used for numerical solution, in conjunction with the Newton-Raphson method to approximate nonlinear partial differential equations.

Conclusions: The numerical examples showed that the model could represent the formation of different types of fingerprint characteristics in each individual.

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• Research Support, Non-U.S. Gov't
• Biochemical Phenomena*
• Computer Simulation
• Dermatoglyphics*
• Models, Biological*

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The surprising genes behind a fingerprint’s unique swirls

The same genes that build an animal’s limbs also encode the intricate patterns in fingerprints. Credit: Douglas Sacha/Getty

The arches, loops and whorls that make each person’s fingerprints unique are created by some of the same genes that drive limb development 1 .

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Introduction to Fingerprints

• First Online: 29 October 2023

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• Neeti Kapoor 6 ,
• Poonam Moon 7 ,
• Pooja Pardeshi 6 &
• Ashish Badiye 6  

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Fingerprints are an individual’s unique characteristic that remains identical throughout an individual’s lifetime. No two individuals can have the same fingerprints, not even the identical twins. Therefore, fingerprints are the most widely adopted method for the biometric purpose. Also, fingerprints, both visible and invisible, are one of the most common pieces of evidence encountered at crime scenes. Researchers have developed different physical, chemical and analytical methods for developing latent fingerprints. The present chapter includes a basic introduction to fingerprints, including history, fingerprint laws, classification, types, collection and preservation methods. Various development methods such as physical, chemical, optical and sophisticated instrumental analytical methods for latent fingerprints are also included.

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Latent Fingerprinting: A Review

Advances in the development and component recognition of latent fingerprints

An Overview and Forecast of Biometric Recognition Technology Used in Forensic Science

A simplified guide to Fingerprint Analysis (2013). https://www.forensicsciencesimplified.org/prints/how.html

Badiye A, Kapoor N (2015) Efficacy of Robin® powder blue for latent fingerprint development on various surfaces. Egypt J Forensic Sci 5(4):166–173. https://doi.org/10.1016/j.ejfs.2015.01.001

Article   Google Scholar  

Bansal HD, Badiye AD, Kapoor NS (2014) Distribution of fingerprint patterns in an Indian population. Malaysian J Forensic Sci 5(2):18–21. http://forensics.org.my/pdf/fssmVol.5No.2/Article04.pdf

Google Scholar  

Braasch K, de la Hunty M, Deppe J, Spindler X, Cantu AA, Maynard P, Lennard C, Roux C (2013) Nile red: alternative to physical developer for the detection of latent fingermarks on wet porous surfaces? Forensic Sci Int 230(1–3):74–80. https://doi.org/10.1016/j.forsciint.2013.03.041

Article   CAS   PubMed   Google Scholar  

Bridges BC (1942) Practical fingerprinting. Funk & Wagnalis Company

Bumbrah GS (2017) Cyanoacrylate fuming method for detection of latent fingermarks: a review. Egypt J Forensic Sci 7(1). https://doi.org/10.1186/s41935-017-0009-7

Bumbrah GS, Sharma RM, Jasuja OP (2016) Emerging latent fingerprint technologies: a review. Res Rep Forensic Med Sci 6:39–50

Bumbrah GS, Sodhi GS, Kaur J (2019) Oil red O (ORO) reagent for detection of latent fingermarks: a review. Egypt J Forensic Sci 9(1):3–9. https://doi.org/10.1186/s41935-018-0107-1

Champod C, Lennard CJ, Margot P, Stoilovic M (2004) Fingerprints and other ridge skin impressions. In: Fingerprints and other ridge skin impressions. CRC Press, Boca Raton. https://doi.org/10.1201/9780203485040

Chapter   Google Scholar  

Daluz HM (2015) Fundamentals of fingerprint analysis. CPC press, Boca Raton

Daluz HM (2019) Fundamentals of fingerprint analysis (second edition). CRC Press, Boca Raton

Frick AA, Fritz P, Lewis SW (2015) Chemical methods for the detection of latent fingermarks. In: Forensic chemistry: fundamentals and applications. Wiley, New York, pp 354–399. https://doi.org/10.1002/9781118897768.ch9

Girod A, Ramotowski R, Weyermann C (2012) Composition of fingermark residue: a qualitative and quantitative review. Forensic Sci Int 223(1–3):10–24. https://doi.org/10.1016/j.forsciint.2012.05.018

Hawthorne MR (2009) Fingerprint: analysis and understanding. CRC Press, Boca Raton

Jasuja OP, Singh G (2009) Development of latent fingermarks on thermal paper: preliminary investigation into use of iodine fuming. Forensic Sci Int 192(1–3):11–16. https://doi.org/10.1016/j.forsciint.2009.08.005

Article   CAS   Google Scholar  

Jasuja OP, Singh GD, Sodhi GS (2008) Small particle reagents: development of fluorescent variants. Sci Justice 48(3):141–145. https://doi.org/10.1016/j.scijus.2008.04.002

Jasuja OP, Toofany MA, Singh G, Sodhi GS (2009) Dynamics of latent fingerprints: the effect of physical factors on quality of ninhydrin developed prints—a preliminary study. Sci Justice 49(1):8–11. https://doi.org/10.1016/j.scijus.2008.08.001

Article   PubMed   Google Scholar  

Kapoor N (2020) An analysis of single digit fingerprints for determination of hand using various types of fingerprint patterns. http://hdl.handle.net/10603/330631

Kapoor N, Badiye A (2015a) Sex differences in the thumbprint ridge density in a central Indian population. Egypt J Forensic Sci 5(1):23–29. https://doi.org/10.1016/j.ejfs.2014.05.001

Kapoor N, Badiye A (2015b) Digital dermatoglyphics: a study on Muslim population from India. Egypt J Forensic Sci 5(3):90–95

Kapoor N, Ahmed S, Shukla RKRK, Badiye A (2019) Development of submerged and successive latent fingerprints: a comparative study. Egypt J Forensic Sci 9(1):44. https://doi.org/10.1186/s41935-019-0147-1

Kapoor N, Badiye A, Mishra SD (2020a) Fingerprint analysis for the determination of hand origin (right/left) using the axis slant in whorl patterns. Forensic Sci Res 7(2):285–289. https://doi.org/10.1080/20961790.2020.1794362

Article   PubMed   PubMed Central   Google Scholar  

Kapoor N, Mishra SD, Badiye A (2020b) Single-digit fingerprint analysis for hand determination: a study of twinned loops. Med Sci Law 60(3):182–187

Lee HC, Gaensselen RE (2001) Advances in fingerprint technology (second edition). CRC Press, Boca Raton

Li H, Cao J, Niu J, Huang Y, Mao L, Chen J (2011) Study of non-invasive detection of latent fingerprints using UV laser. In: International symposium on photoelectronic detection and imaging 2011: laser sensing and imaging; and biological and medical applications of photonics sensing and imaging, 8192, 81921V. https://doi.org/10.1117/12.900235

Loll A, Police P (2013) Automated fingerprint identification systems ( AFIS ). In: Encyclopedia of forensic sciences, 2nd edn. Elsevier, New York. https://doi.org/10.1016/B978-0-12-382165-2.00261-0

Menzel ER (1980) Fingerprint Detection With Lasers | U.S. Department of Justice Office of Justice Programs. NCJ Number 78311

Menzel ER (1985) Comparison of argon-ion, copper-vapor, and frequency-doubled neodymium: yttrium aluminum garnet (ND:YAG) lasers for latent fingerprint development. J Forensic Sci 30(2):11817J. https://doi.org/10.1520/jfs11817j

Nabar BS (2013) Forensic science in crime investigation, 3rd edn. Asia Law House, Hyderabad

Nabar BS (2019) Forensic science in crime investigation. Asia Law House, Hyderabad. ISBN: 978-9388437691

Nath S (1991) Fingerprint identification in crime detection. Reliance Publishing House, New Delhi

Nath S (2006) Personal identification through fingerprints, 1st ed. Shree Publishers and Distributors, New Delhi. ISBN: 9788183291170

Ramotowskithird RS (2012) Lee and gaensslen’s advances in fingerprint technology. In: Lee and Gaensslen’s Advances in fingerprint technology. Routledge, Oxfordshire. https://doi.org/10.1201/b12882

Saferstein R (1983) Criminalistics: an introduction to forensic science, 1st edn. McGraw-Hill College, New York

Salares VR, Eves CR, Carey PR (1979) On the detection of fingerprints by laser excited luminescence. Forensic Sci Int 14(3):229. https://doi.org/10.1016/0379-0738(79)90142-7

Sharma BR (2014) Forensic science in criminal investigation & trial, 5th edn. Universal Law Publishing, New Delhi

Sodhi GS, Kaur J (2017) Multimetal deposition method for detection of latent fingerprints: a review. Egypt J Forensic Sci 7(1):1–7. https://doi.org/10.1186/S41935-017-0017-7/FIGURES/9

Sodhi GS, Kaur J (2019) Application of metals and metal salts in latent fingerprint detection: a review. Int J Forensic Sci 2(2):71–77

Stauffer E, Becue A, Singh KV, Thampi KR, Champod C, Margot P (2007) Single-metal deposition (SMD) as a latent fingermark enhancement technique: an alternative to multimetal deposition (MMD). Forensic Sci Int 168(1):e5. https://doi.org/10.1016/J.FORSCIINT.2006.12.009

Swanson CL (2013) Pattern evidence/history fingerprint sciences. In: Encyclopedia of forensic sciences, 2nd edn. Elsevier, New York. https://doi.org/10.1016/B978-0-12-382165-2.00272-5

Yamashita B, French M, Bleay S, Cantu A, Inlow V, Ramotowski R, Sears V, Wakefield M (2014) Latent print development. In: Latent fingerprint examination: elements, human factors and recommendations. Nova Science Publishers, Hauppauge, pp 225–320. https://doi.org/10.4324/9780429454530-12

Yassin AA, Jin H, Ibrahim A, Zou D (2012) “Anonymous Password Authentication Scheme by Using Digital Signature and Fingerprint in Cloud Computing,” 2012 Second International Conference on Cloud and Green Computing. Xiangtan, China, pp 282–289. https://doi.org/10.1109/CGC.2012.91

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Fingerprints: The Key to Our Individuality

By Charlton Sullivan 蘇柏安

Introduction

Before we delve into the complexities of blood tests and DNA analyses to identify who we are, society has already made a simple yet straightforward method that we always see in movies and when we are crossing the border: our fingerprints which are also known as “friction ridge skin”. Just like our faces, fingerprints are key to our individuality and identity. But have you ever wondered why each of us has unique fingerprints? The answer lies deep in the interaction between our genes, especially those that control limb development, and the environment. This results in the formation of unique dermatoglyphic patterns which can be classified into three categories: arch, loop, and whorl (Figure 1).

Figure 1  The three categories of fingerprint: arch, loop and whorl.

Fingerprint Formation

There are multiple theories supporting fingerprint development but dermatologists believe the folding hypothesis is the most promising one [1]. Skin tissue consists of three tightly connected vertical layers: epidermis, basal layer and dermis. The different rates of cellular growth in the top epidermis and the bottom dermis create a tension across the fast-growing basal layer, resulting in the folding of the basal layer at individual sites to relieve the stress (Figure 2) [2, 3]. Cell proliferation continues at those sites while the folds combine and merge into clusters to form linear ridges in a rather random fashion, creating the unique pattern of wrinkles in our fingerprints (Figure 3) [2].

Figure 2  Schematic diagram of the folding hypothesis [3].

Figure 3  Ridge formation through the combination of the centers of cell proliferation [2].

Volar pads are well known to play a role in determining fingerprint pattern. They are transient tissue swellings present on certain areas of our palms, including each fingertip, during embryonic development (embryogenesis) (Figure 4). Coinciding with the process of ridge formation, these structures start shrinking from the 10th week [1]. The shrinkage introduces extra mechanical stresses across the skin, affecting the directions of ridge formation [1]. Scientists generally agreed that the height and size of volar pads can influence the pattern of fingerprints [2, 4]. Whorl-type patterns are usually formed on high volar pads whereas low volar pads produce the arch-type pattern. Intermediate-height volar pads create the loop-type pattern.

Figure 4  Volar pads on the palm during embryonic development are highlighted in light gray.

Then, how do genetic factors come into play? It was illustrated that the geometry of volar pads can be controlled by genes [1]. For example,  EVI1 , a limb development gene responsible for the outgrowth of distal limbs and digits, was found to express under the volar pads. It was hypothesized to influence fingerprint pattern by controlling the shape and size of the volar pads through its function to promote cell proliferation, as in the distal ends of the developing limbs [4]. This also provides insight into the correlation between fingerprint pattern and limb-related phenotypes [4]. Researchers discovered that individuals with whorl patterns on both pinky fingers often have a longer pinky finger compared to those with no whorl pattern on pinky fingers; the frequency of whorl patterns on the fingers of both hands (except the thumbs) is also associated with longer pinky fingers [4].

Exception: The Family with No fingerprints

We have often taken fingerprint technology for personal identification for granted. Fingerprints are a huge part of our identity in modern society, with applications in mobile phones and immigration. Nevertheless, Apu Sarker’s family in Bangladesh has no fingerprints due to a rare genetic mutation in the  SMARCAD1  gene, causing Adermatoglyphia or “Delayed Immigration Disease” [5]. Luckily, it does not cause any serious illnesses, but the family encountered difficulties in their everyday lives because fingerprints became mandatory when obtaining driving licenses, sim cards, and passports. As a result, they could not obtain a driving license nor purchase a sim card for their mobile phones. In Apu’s ID card when he was still 10, he was labeled with “NO FINGERPRINT” as the government officials had no idea on how to issue a card without the means for personal identification. With the advent of modern technologies, such as iris scan and facial recognition, let us all hope those who have such genetic conditions would not unintentionally be discriminated against in the near future.

Interesting Fact: Do Monozygotic Twins Share the Same Fingerprints?

Have you ever wondered whether identical twins share identical fingerprints? Although they are very similar in appearance and contain the same DNA sequence, they have slightly different fingerprints that are significant enough to be captured by the modern recognition software [6]. Aside from the randomness in the fingerprint formation process, small differences in umbilical cord length, womb position, blood pressure, nutritional intake, and the rate of finger growth during the 13th to 19th week can still influence fingerprint formation as a result [6]! This highlights the importance of how environmental factors apart from genetics also play an essential role in determining our fingerprints.

Our fingerprints are an important physical trait that can define who we are as individuals. The unique pattern formed during embryogenesis persists throughout our lives from the 19th week of gestation [3] and remains the same even after we die (until decomposition occurs). Its formation is dependent on the interaction between genetic and variable environmental factors, which ultimately give rise to our unique fingerprints: the key to our identity and individuality.

References 參考資料：

[1] Kücken, M. (2007). Models for fingerprint pattern formation.  Forensic Science International, 171 (2-3), 85–96.  https://doi.org/10.1016/j.forsciint.2007.02.025

[2] Wertheim, K. (2011). Embryology and Morphology of Friction Ridge Skin. In A. McRoberts (Ed.),  Fingerprint Sourcebook  (pp. 3-1–3-26). National Institute of Justice.  https://www.ojp.gov/pdffiles1/nij/225323.pdf

[3] Garzón-Alvarado, D. A., & Ramírez Martinez, A. M. (2011). A biochemical hypothesis on the formation of fingerprints using a turing patterns approach.  Theoretical Biology and Medical Modelling, 8 , 24.  https://doi.org/10.1186/1742-4682-8-24

[4] Li, J., Glover, J. D., Zhang, H., Peng, M., Tan, J., Mallick, C. B., Hou, D., Yang, Y., Wu, S., Liu, Y., Peng, Q., Zheng, S. C., Crosse, E. I., Medvinsky, A., Anderson, R. A., Brown, H., Yuan, Z., Zhou, S., Xu, Y., . . . Wang, S. (2022). Limb development genes underlie variation in human fingerprint patterns.  Cell, 185 (1), 95–112.  https://doi.org/10.1016/j.cell.2021.12.008

[5] Sabbir, M. (2020, December 26). The family with no fingerprints.  BBC News.   https://www.bbc.com/news/world-asia-55301200

[6] Asher, C. (2021, September 18). Why do identical twins have different fingerprints?  BBC Science Focus Magazine.   https://www.sciencefocus.com/the-human-body/why-do-identical-twins-have-different-fingerprints-2/

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Collective intelligence in fingerprint analysis

• Jason M. Tangen 1 ,
• Kirsty M. Kent 1 &
• Rachel A. Searston 2  

Cognitive Research: Principles and Implications volume  5 , Article number:  23 ( 2020 ) Cite this article

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When a fingerprint is located at a crime scene, a human examiner is counted upon to manually compare this print to those stored in a database. Several experiments have now shown that these professional analysts are highly accurate, but not infallible, much like other fields that involve high-stakes decision-making. One method to offset mistakes in these safety-critical domains is to distribute these important decisions to groups of raters who independently assess the same information. This redundancy in the system allows it to continue operating effectively even in the face of rare and random errors. Here, we extend this “wisdom of crowds” approach to fingerprint analysis by comparing the performance of individuals to crowds of professional analysts. We replicate the previous findings that individual experts greatly outperform individual novices, particularly in their false-positive rate, but they do make mistakes. When we pool the decisions of small groups of experts by selecting the decision of the majority, however, their false-positive rate decreases by up to 8% and their false-negative rate decreases by up to 12%. Pooling the decisions of novices results in a similar drop in false negatives, but increases their false-positive rate by up to 11%. Aggregating people’s judgements by selecting the majority decision performs better than selecting the decision of the most confident or the most experienced rater. Our results show that combining independent judgements from small groups of fingerprint analysts can improve their performance and prevent these mistakes from entering courts.

Public Significance Statement

Several reports by peak scientific and regulatory bodies have been roundly critical of the dearth of evidence supporting traditional forensic methods and practices such as fingerprint analysis. In response to these criticisms, a number of experiments have now been conducted, demonstrating that professional fingerprint analysts are impressively accurate compared to novices when distinguishing between crime-scene prints from the same and different sources—but they still make mistakes. These mistakes are unavoidable, even in other high stakes, safety-critical domains such as medicine, aviation, or nuclear power. The aim, then, is to build safeguards into these systems that mitigate the impact of these mistakes in practice. In this experiment, we examine one such countermeasure, which exploits the collective intelligence of groups of professional fingerprint analysts. Our results show that pooling the decisions of small, independent groups of examiners can substantially boost the overall performance of these crowds and reduce the influence of errors. Integrating collective intelligence processes into existing forensic identification and verification systems could play a significant role—alongside effective training methods and evidence-based practices—in developing reliable and resilient systems to ensure the rule of law is justly applied.

When a fingerprint is recovered from a crime scene, a computer algorithm is used to compare the print to tens of millions of prints stored in a database. The algorithm then returns a list of potential candidates ranked from the most to the least similar. It is up to a human examiner to work through this list, comparing the overall pattern and flow of the prints as well as the fine details in each, such as ridge endings, bifurcations, contours, islands, dots, breaks, creases, pores, and enclosures. If the examiner has identified a sufficient number of corresponding features to be confident that the two prints came from the same person, then this final “same source” decision is logged into the computer system, which is then typically “verified” by a second examiner who—depending on their jurisdiction—may or may not be blind to the initial examiner’s decision (Thompson, Black, Jain, & Kadane, 2017 ).

Despite the fact that fingerprint examiners have been shown to perform exceptionally well (Searston & Tangen, 2017a ; Searston & Tangen, 2017b ; Tangen, Thompson, & McCarthy, 2011 ; Thompson & Tangen, 2014 ; Ulery, Hicklin, Buscaglia, & Roberts, 2011 ), errors have occurred in the past (Cole, 2005 ). A correct identification can mean the difference between exonerating a criminal or convicting an innocent person. It is clear that forensic analysts are working hard to capture criminals and uphold civil liberties; they have a very high workload, relentlessly coding evidence, supporting detectives, searching and maintaining databases, writing reports, and testifying as expert witnesses. It is also clear that—despite everyone’s best efforts—mistakes happen and will continue to happen, even when people’s lives are at stake. In medical diagnostics for example, roughly 200,000 patients die from preventable medical errors each year (James, 2013 ), and 5% of autopsies reveal lethal diagnostic errors that could have been averted (Shojania, Burton, McDonald, & Goldman, 2003 ). One of the main conclusions of an authoritative report on these errors by the National Institute of Medicine was that these mistakes do not result from individual recklessness or the actions of a particular group. Instead, it is important to design resilient systems that identify and enhance the positive capacities of people (Dekker, 2014 ). Rather than focusing on “bad apples” at the frontline, the report recommended the development of safeguards for people’s fallibility—to “make it harder for people to do something wrong and easier for them to do it right” (Institute of Medicine, 2000 , p. 2).

One such safeguard that has been highly successful across several domains has been to exploit the “collective intelligence” of groups who collaborate to solve problems well. The “wisdom of crowds” phenomenon dates back to Aristotle (see Landemore, 2012 ), it was later investigated by Galton ( 1907 ) and others more formally in the early 20th century (e.g., Gordon, 1924 ). The rise of “crowds” has since been promoted in a series of popular books (e.g., Surowiecki, 2004 ; Rheingold, 2007 ; McAfee & Brynjolfsson, 2017 ) and even in a short-lived crime television drama (Humphrey, 2017 ), and for good reason, since combining judgements from many individuals can be surprisingly accurate in prediction markets, forecasting sporting outcomes, box office success, and geopolitical and climate-related events (Escoffier & McKelvey, 2015 ; Hueffer, Fonseca, Leiserowitz, & Taylor, 2013 ; Tetlock, Mellers, Rohrbaugh, & Chen, 2014 ; Wolfers & Zitzewitz, 2004 ). The benefits of aggregation have also been identified across a range of safety-critical domains including the diagnosis of skin lesions (Kurvers, Krause, Argenziano, Zalaudek, & Wolf, 2015 ), the interpretation of mammograms (Wolf, Krause, Carney, Bogart, & Kurvers, 2015 ), diagnosis in emergency medicine (Kämmer, Hautz, Herzog, Kunina-Habenicht, & Kurvers, 2017 ), and matching unfamiliar faces (Balsdon, Summersby, Kemp, & White, 2018 ).

A useful analogy for thinking about system failures, such as medical mishaps or nuclear meltdowns, is the Swiss cheese model of errors by Reason ( 2000 ), which likens human systems to multiple slices of Swiss cheese layered on top of each other. Each defensive layer (e.g., alarms, physical barriers, automatic shutdowns) could prevent a breach from occurring, but has unintended flaws, or holes, which can all align and cause harm by allowing the hazard to pass through. One can think about the wisdom of crowds in a similar way: each rater is depicted as a slice of Swiss cheese; the fewer and smaller the holes, the more expertise one has, the less chance there is of making an error. As more people are layered on, if their decisions are independent and they approach the problem from different perspectives, then the holes will be misaligned, preventing the error from passing through. On the other hand, if the raters all have the same blind spots—where the “holes” align—then errors may slip through.

In this experiment, we extend this “wisdom of crowds” approach to fingerprint analysis by comparing the performance of individuals and crowds of professional fingerprint analysts. We test whether crowds of novice participants are as collectively wise as experts, and also evaluate the collective intelligence of the groups by comparing three different rules for aggregating people’s responses:

Follow-the-majority . Adopt the judgement with the most support in the group.

Follow-the-most-confident . Adopt the judgement with the highest confidence rating.

Follow-the-most-senior . Adopt the judgment of the most experienced examiner.

Majority and confidence rules have been used successfully in high-stakes domains such as breast and skin cancer detection (Kurvers et al., 2015 ), while the seniority rule is less common (Kämmer et al., 2017 ). Pooling the independent judgments of small groups of diagnosticians substantially increases performance relative to average individual performance, often better than the highest performing member. The best rule often depends on the size of the group, but in general, if the decisions being pooled are unbiased, diverse, and derived independently, then the collective output will typically outperform even the best member of the group (Surowiecki, 2004 ). All three of these decision rules are often used in practice across a range of applied contexts, but they can lead to very different outcomes. But what about fingerprint analysis? Is it more sensible to follow the majority, the most confident, or the most senior examiner?

The methods and materials for this experiment are available and described at length on the Open Science Framework, including our experiment code, video instructions, trial sequences, de-identified data, and analysis scripts ( http://tiny.cc/jbkxcz ).

Thirty-six professional fingerprint examiners from the Australian Federal Police, Queensland Police Service, Victoria Police, and New South Wales Police (13 females and 23 males, mean age = 46 years, SD = 8, mean experience = 16.4 years, SD = 8.6) volunteered their time. Thirty-six novice participants (25 females and 11 males, mean age = 21.6 years, SD = 3.6, with no formal experience with fingerprints) consisting of undergraduate psychology students who participated for course credit and members of the broader communities at The University of Queensland and The University of Adelaide also volunteered their time. A novice control group is important for establishing expertise (Thompson, Tangen, & McCarthy, 2013 ), and allows us to examine whether more domain knowledge makes for a wiser crowd—which may not always be the case (Herzog & Hertwig, 2011 ).

The “crime scene” prints and their matches were collected and developed at The University of Queensland from undergraduate students who left their prints on various surfaces (e.g., wood, plastic, metal, and glass), so unlike genuine crime-scene prints, they had a known true origin (Cole, 2005 ). Simulated prints were dusted by a research assistant (who was trained by a qualified fingerprint expert), photographed, cropped, and isolated in the frame. A qualified expert reported that each simulated print contained sufficient information to make an identification if there was a clear comparison exemplar.

Each of the 36 fingerprint examiners was presented with the same set of 24 fingerprint pairs from the same finger (targets) and 24 highly similar pairs from different fingers (distractors) in a different random order. Each pair consisted of a crime-scene “latent” fingerprint and a fully rolled “arrest” fingerprint, and participants were asked to provide a rating on a 12-point scale ranging from 1 (Sure Different) to 12 (Sure Same). On target trials, when the prints were from the same person, ratings from 7 to 12 count as a “true positive”; on distractor trials, when the prints were from different people, ratings from 7 to 12 count as a “false positive.” The distractors were created by running each latent fingerprint through the National Australian Fingerprint Identification System—which consists of roughly 67 million fingerprints—to return the most similar exemplars from the database (see Tangen et al., 2011 , for a similar methodology). On the first 44 of 48 trials (22 targets, 22 distractors), participants were given 20 s to examine the prints. On the final four trials (two targets, two distractors), they had an unlimited amount of time to make a decision. These four untimed trials were cycled across each of the fingerprint pairs across the 36 participants so that each fingerprint pair was examined by three different participants. After running these 36 fingerprint examiners through the experiment, we presented an identical set of 36 trial sequences with the same fingerprint pairs in the same order to 36 novice participants.

Individual Performance

The individual performance of the 36 novices (yellow) and 36 experts (purple) is illustrated in Fig.  1 . The true-positive rate (on the left) represents each person’s performance when the prints came from the same finger. “False negatives” on these target trials are the sort of mistakes that could potentially lead to false exonerations in practice. The false-positive rate (on the right) represents each person’s performance when the prints came from different fingers. “False positives” on these distractor trials are the sort of mistakes that could potentially lead to false convictions in practice. These results closely replicate previous findings (e.g., Tangen et al., 2011 ; Thompson, Tangen, & McCarthy, 2014 ) in which experts outperformed novices on distractor trials, and performed the same or slightly better than novices on target trials. This benefit of expertise is evident in Fig.  1 a during the 44 trials (22 targets, 22 distractors) in which participants were given 20 s to make a decision, and in Fig.  1 b during the four trials (two targets, two distractors) in which participants had no time limit on making a decision. In the 20-s condition with 44 trials, experts made true-positive decisions 71% (SD = 45%) of the time and false-positive decisions 8.5% (SD = 28%) of the time. Novices, by comparison, made true-positive decisions 71% (SD = 45%) of the time and false-positive decisions 50% (SD = 50%) of the time. In the untimed condition with four trials, experts made true-positive decisions 85% (SD = 36%) of the time and false positives 2.8% (SD = 17%) of the time. Novices, on the other hand, made true-positive decisions 76% (SD = 43%) of the time and false positives 60% (SD = 49%) of the time.

True- and false-positive rate for individual novices and experts after 20 s of analysis ( a ) or without a time limit ( b ). Each jittered data point represents the mean proportion of true or false positives for each individual participant. The red shape represents the mean and vertical bars ±1 standard deviation

A 2 (Group: novices vs. experts) × 2 (Rate Type: true vs. false positives) mixed ANOVA confirmed these impressions with significant main effects of Group, F (1, 70) = 61.34, p  < .001, η g 2  = .33, and Rate Type, F (1, 70) = 342.11, p  < .001, η g 2  = .68, along with a significant interaction, F (1, 70) = 84.25, p  < .001, η g 2  = .34 in the 20-s condition. The same pattern was evident in the unlimited condition: significant main effects of Group, F (1, 70) = 27.09, p  < .001, η g 2  = .16, and Rate Type, F (1, 35) = 110.62, p  < .001, η g 2  = .44, as well as a significant interaction, F (1, 70) = 48.47, p  < .001, η g 2  = .26.

Collective Performance

The most popular, transparent, and easiest method of aggregating people’s decisions is the majority rule (Hastie & Kameda, 2005 ). It is based on the commonsense notion that “many heads are better than one,” and is commonly used when making decisions in elections and committees: choose the option that gets more than half of the votes. In the experiment described above, each of the 48 pairs of fingerprints was either judged to be from “same” or “different” fingers by 36 professional fingerprint analysts and 36 novices. For each pair of prints, we took a random sample of three analysts, and tallied the decisions made by this trio using the majority rule. We then took another random group of three analysts, tallied their decisions, and repeated this process 2000 times and for groups of 3, 5, 7, and so on for each odd group size up to 35. The result was 2000 majority decisions for each of the 48 fingerprint pairs (24 targets and 24 distractors) across the 17 different group sizes. We repeated this process for novices as well.

The results of the simulation are illustrated in Fig.  2 . The individual true- and false-positive rates from Fig.  1 are represented as “Group Size, Number of Raters: 1” on the left side of each panel of Fig.  2 , respectively. As we aggregate the 20-s decisions of 3, 5, 7... experts moving along the x -axis of Fig.  2 a and c, the true-positive rate begins to increase and false-positive rate begins to decrease until they begin to level off at nine raters. For novices, however, their true-positive rate improves as more raters are included, but their false-positive rate remains at roughly 50% with a group of 35. When people are given an unlimited amount of time to decide—as illustrated in Fig.  2 b and d—the benefit of expertise is even more pronounced. The true-positive rate increases from 85% for individuals to 96% for groups of three experts, but increases from 76% for individual novices to 79% for novice trios. The false-positive rate is 2.8% for individual experts, and 0% for groups of three experts. The false-positive rate for novices is 60% for individuals, and 79% for groups of three novices.

Mean true-positive rates for groups of novices and experts after 20 s of analysis ( a ) or without a time limit ( b ), and mean false positive rates for groups of novices and experts after 20 s of analysis ( c ) or without a time limit ( d )

Pooling the independent judgements of a group of professional fingerprint analysts using a majority rule reduced their false-negative rate by up to 12% and their false-positive rate by up to 8%. Groups of novices, on the other hand, also received a boost in their true-positive rate of up to 19% with the majority rule, but their false-positive rate remained at roughly 50%.

Another way to represent these results is to combine people’s true- and false-positive rates into a single measure of discriminability, which calculates how well they can distinguish between prints from the same finger and prints from different fingers. We use a non-parametric model of discriminability that averages the minimum and maximum proper receiver operating characteristic curves through a point ( A ) for each individual expert and novice participant; an A value of .5 is chance and 1 is perfect discriminability (Zhang & Mueller, 2005 ). As illustrated by the dark purple data points in Fig.  3 , expert discrimination improves when taking the majority decision of small groups of examiners, leveling off at groups of nine, which is mirrored by a similar improvement by novices in dark yellow—just at a much lower level of performance.

Mean discriminability scores ( A ) for experts (purple) and novices (yellow) after 20 s of analysis ( a ) or without a time limit ( b ). The different shades of each color represent the three aggregation rules: (1) follow-the-majority; (2) follow-the-most-confident; and (3) follow-the-most-senior

The discriminability scores for the majority rule in Fig.  3 are presented alongside two other aggregation rules: (1) follow-the-most-confident and (2) follow-the-most-senior, which both improved the collective diagnostic performance of medical students (Kämmer et al., 2017 ).

We measured people’s absolute confidence on each trial by first collapsing across same and different on our 12-point scale, which ranged from 1 (Sure Different) to 12 (Sure Same), so each rating ranged from 1 (Unsure) to 6 (Sure). We then adopted the judgment with the highest confidence rating. For example, a random group of five people might provide ratings of 7, 10, 9, 8, and 1, which equates to confidence ratings of 1, 4, 3, 2, and 6. Even though four of the five provide a “Same” judgement, the extreme “Different” rating of 1 is the most extreme, so this highly confident examiner’s decision would be adopted. If people were equally confident about the two options, one was selected at random.

At the beginning of the experiment, each participant was asked to indicate how many years of formal experience they have with fingerprints. Given the follow-the-most-senior rule, we adopted the judgment of the most “senior” examiner in the crowd (i.e., the person with the greatest number of years examining prints). For example, a random group of five examiners might have 7, 10, 9, 18, and 25 years of experience. Even though the four less-experienced examiners each provide a “Same” judgement, since the most experienced examiner with 25 years of experience said, “Different,” this decision would be adopted. If examiners have the same level of experience, the response by one examiner was selected at random. Since none of the novice control participants had any experience with fingerprints, this rule was not applied to their ratings.

The output of these three aggregation rules is depicted in Fig.  3 . All three rules boosted collective performance compared to individual judgements—particularly in the unlimited time condition. For novices, the majority rule produced the largest increase when given 20 s to decide, and the confidence rule produced the largest gains in the unlimited condition. But even the output of the best aggregation rule applied to novice ratings paled in comparison to experts. The majority rule produced the largest collective performance boost for experts followed by the confidence rule followed by the seniority rule—both in the 20 s and unlimited time conditions.

Managing errors when lives and livelihoods are at stake requires resilient systems with safeguards that can tolerate mistakes and withstand their damaging effects (Reason, 2000 ). The wisdom of crowds may provide one such countermeasure to mitigate their impact, which motivated us to explore the role of collective intelligence in fingerprint analysis. Our results showed that individual experts performed exceedingly well, but they still made errors. Yet when we combined their decisions using a simple majority rule, these mistakes disappeared almost entirely. Pooling the decisions from small crowds of professional fingerprint analysts makes this wise group even wiser. Pooling the decisions from small crowds of novices, however, improved their true-positive rate, but at the cost of many more false positives. We tested the effect of two other aggregation methods. The first is to adopt the decision of the most confident person in the crowd and the second is to adopt the decision of the most experienced person in the crowd. Both of these pooling methods produced a slight improvement for experts compared to individual judgements depending on the condition, but the majority rule—which is the most common, transparent, and easiest method to adopt—delivered the most considerable boost in performance.

Our results add to the growing body of evidence that combining independent judgements can greatly improve the quality of decision-making in high-stakes domains. What makes this collective intelligence approach particularly appealing in these contexts is the robustness or “fault tolerance” that is built into the aggregation process. Instead of a single examiner bearing the weight of this important decision, the burden is distributed equally across several individuals. This redundancy provides some assurance that the system will not collapse with a single mistake. Such a system would be straightforward to implement; it embodies a team-based approach to decision-making, and would bring greater peace of mind to analysts, managers, and their organizations. Of course, it is also possible that examiners could feel less responsibility for their collective decisions compared to acting alone, so they may be less conservative or careful than usual if they assume other examiners will catch their mistakes (El Zein, Bahrami, & Hertwig, 2019 ). Despite the promise of a collective intelligence system, courts would need to figure out how to accommodate cases where decision-making is distributed (Kemp, White, & Edmond, in press ). Time and resourcing limitations could also be a consideration in adopting a distributed system, but each expert may not need to replicate the entire analysis that is currently performed by an individual examiner (Ballantyne, Edmond, & Found, 2017 ). Indeed, this experiment was conducted in a tightly controlled setting and should be replicated under typical conditions using actual casework materials, software tools, and timeframes. Assuming that our results generalize to everyday practice, pooling the decisions of crowds of expert analysts may provide an effective safeguard against miscarriages of justice.

Availability of data and materials

The data and code for each individual novice and expert participant used to produce our results and plots are available, with the exception of participants’ years of experience, which have been rank-ordered to preserve the identities of our participants.

Change history

29 september 2023.

A Correction to this paper has been published: https://doi.org/10.1186/s41235-023-00514-w

Ballantyne, K. N., Edmond, G., & Found, B. (2017). Peer review in forensic science. Forensic Science International , 277 , 66–76. https://doi.org/10.1016/j.forsciint.2017.05.020 .

Article   PubMed   Google Scholar  

Balsdon, T., Summersby, S., Kemp, R. I., & White, D. (2018). Improving face identification with specialist teams. Cognitive Research: Principles and Implications , 3 (1), 25. https://doi.org/10.1186/s41235-018-0114-7 .

Cole, S. A. (2005). More than zero: Accounting for error in latent fingerprint identification. The Journal of Criminal Law & Criminology , 95 (3), 985–1078 http://www.jstor.org/stable/3491332 .

Google Scholar  

Dekker, S. (2014). The Field Guide to Understanding ‘Human Error’ . London: Routledge. https://doi.org/10.1201/9781317031833 .

Book   Google Scholar  

El Zein, M., Bahrami, B., & Hertwig, R. (2019). Shared responsibility in collective decisions. Nature Human Behaviour , 3 (6), 554–559. https://doi.org/10.1038/s41562-019-0596-4 .

Article   PubMed   PubMed Central   Google Scholar  

Escoffier, N., & McKelvey, B. (2015). The wisdom of crowds in the movie industry: Towards new solutions to reduce uncertainties. International Journal of Arts Management , 17 (2), 52–73 http://www.jstor.org/stable/24587073 .

Galton, F. (1907). Vox populi. Nature , 75 (7), 450–451. https://doi.org/10.1038/075450a0 .

Article   Google Scholar  

Gordon, K. (1924). Group judgments in the field of lifted weights. Journal of Experimental Psychology , 7 (5), 398–400. https://doi.org/10.1037/h0074666 .

Hastie, R., & Kameda, T. (2005). The robust beauty of majority rules in group decisions. Psychological Review , 112 (2), 494–508. https://doi.org/10.1037/0033-295X.112.2.494 .

Herzog, S. M., & Hertwig, R. (2011). The wisdom of ignorant crowds: Predicting sport outcomes by mere recognition. Judgment and Decision making , 6 (1), 58–72.

Hueffer, K., Fonseca, M. A., Leiserowitz, A., & Taylor, K. M. (2013). The wisdom of crowds: Predicting a weather and climate-related event. Judgment and Decision making , 8 (2), 91–105.

Humphrey, T. (2017). Wisdom of the Crowd . Los Angeles: Columbia Broadcasting System.

Institute of Medicine (2000). To Err Is Human: Building a Safer Health System . Washington, DC: The National Academies Press. https://doi.org/10.17226/9728 .

James, J. T. (2013). A new, evidence-based estimate of patient harms associated with hospital care. Journal of Patient Safety , 9 (3), 122–128. https://doi.org/10.1097/PTS.0b013e3182948a69 .

Kämmer, J. E., Hautz, W. E., Herzog, S. M., Kunina-Habenicht, O., & Kurvers, R. H. (2017). The potential of collective intelligence in emergency medicine: Pooling medical students’ independent decisions improves diagnostic performance. Medical Decision Making , 37 (6), 715–724 https://doi.org/10.1177/0272989X17696998 .

Kemp, R. I., White, D., & Edmond, G. (in press). A proposed solution to the problem of identifying people from CCTV and other images. In M. Toglia, A. Smith, & J. M. Lampinen (Eds.), Methods, measures, and theories in forensic facial-recognition tasks . London: Taylor and Francis.

Kurvers, R. H., Krause, J., Argenziano, G., Zalaudek, I., & Wolf, M. (2015). Detection accuracy of collective intelligence assessments for skin cancer diagnosis. JAMA Dermatology , 151 (12), 1346–1353 https://doi.org/10.1001/jamadermatol.2015.3149 .

Landemore, H. E. (2012). Why the many are smarter than the few and why it matters. Journal of Public Deliberation , 8 (1), 7 https://www.publicdeliberation.net/jpd/vol8/iss1/art7 .

McAfee, A., & Brynjolfsson, E. (2017). Machine, platform, crowd: Harnessing our digital future . New York: WW Norton & Company.

Reason, J. (2000). Human error: models and management. BMJ , 320 (7237), 768–770 https://doi.org/10.1136/bmj.320.7237.768 .

Rheingold, H. (2007). Smart mobs: The next social revolution . New York: Basic books.

Searston, R. A., & Tangen, J. M. (2017a). The emergence of perceptual expertise with fingerprints over time. Journal of Applied Research in Memory and Cognition , 6 (4), 442–451 https://doi.org/10.1016/j.jarmac.2017.08.006 .

Searston, R. A., & Tangen, J. M. (2017b). The style of a stranger: Identification expertise generalizes to coarser level categories. Psychonomic Bulletin & Review , 24 (4), 1324–1329 https://doi.org/10.3758/s13423-016-1211-6 .

Shojania, K. G., Burton, E. C., McDonald, K. M., & Goldman, L. (2003). Changes in rates of autopsy-detected diagnostic errors over time: a systematic review. JAMA , 289 (21), 2849–2856 https://doi.org/10.1001/jama.289.21.2849 .

Surowiecki, J. (2004). The Wisdom of Crowds . New York: Random House.

Tangen, J. M., Thompson, M. B., & McCarthy, D. J. (2011). Identifying fingerprint expertise. Psychological Science , 22 (8), 995–997 https://doi.org/10.1177/0956797611414729 .

Tetlock, P. E., Mellers, B. A., Rohrbaugh, N., & Chen, E. (2014). Forecasting tournaments: Tools for increasing transparency and improving the quality of debate. Current Directions in Psychological Science , 23 (4), 290–295 https://doi.org/10.1177/0963721414534257 .

Thompson, M. B., & Tangen, J. M. (2014). The nature of expertise in fingerprint matching: Experts can do a lot with a little. PLoS One , 9 (12), e114759 https://doi.org/10.1371/journal.pone.0114759 .

Thompson, M. B., Tangen, J. M., & McCarthy, D. J. (2013). Expertise in fingerprint identification. Journal of Forensic Sciences , 58 (6), 1519–1530 https://doi.org/10.1111/1556-4029.12203 .

Thompson, M. B., Tangen, J. M., & McCarthy, D. J. (2014). Human matching performance of genuine crime scene latent fingerprints. Law and Human Behavior , 38 (1), 84–93 https://doi.org/10.1037/lhb0000051 .

Thompson, W., Black, J., Jain, A., & Kadane, J. (2017). Forensic science assessments: A quality and gap analysis – Latent fingerprint examination . Washington, DC: American Association for the Advancement of Science.

Ulery, B. T., Hicklin, R. A., Buscaglia, J., & Roberts, M. A. (2011). Accuracy and reliability of forensic latent fingerprint decisions. Proceedings of the National Academy of Sciences , 108 (19), 7733–7738 https://doi.org/10.1073/pnas.1018707108 .

Wolf, M., Krause, J., Carney, P. A., Bogart, A., & Kurvers, R. H. (2015). Collective intelligence meets medical decision-making: The collective outperforms the best radiologist. PLoS One , 10 (8), e0134269 https://doi.org/10.1371/journal.pone.0134269 .

Wolfers, J., & Zitzewitz, E. (2004). Prediction markets. Journal of Economic Perspectives , 18 (2), 107–126 https://doi.org/10.1257/0895330041371321 .

Zhang, J., & Mueller, S. T. (2005). A note on ROC analysis and non-parametric estimate of sensitivity. Psychometrika , 70 (1), 203–212 https://doi.org/10.1007/s11336-003-1119-8 .

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Acknowledgements

We thank the Australian fingerprint examiners who participated in our research for giving their time and expertise so generously. We thank Amy Cramb and Molly Arendt for their assistance in collecting the data.

This research was supported by grant No. LP170100086 from the Australian Research Council to Tangen and Searston.

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In line with the CRediT taxonomy, JMT contributed to all aspects of the project: conceptualization, data curation, formal analysis, funding acquisition, investigation, methodology, project administration, resources, software, supervision, validation, visualization, writing, and editing. KMK contributed to the conceptualization, investigation, methodology, project administration, validation, writing, and editing. RAS contributed to the data curation, formal analysis, funding acquisition, investigation, project administration, resources, software, supervision, validation, visualization, writing, and editing. The authors read and approved the final manuscript.

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Tangen, J.M., Kent, K.M. & Searston, R.A. Collective intelligence in fingerprint analysis. Cogn. Research 5 , 23 (2020). https://doi.org/10.1186/s41235-020-00223-8

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DOI : https://doi.org/10.1186/s41235-020-00223-8

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• Collective intelligence
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Succession Science: Are Fingerprint Patterns Inherited?

A Father's Day inheritance investigation from Science Buddies

By Science Buddies

Key concepts Genetics Inheritance Biology Development DNA   Introduction Have you ever seen a child who looked just like his or her father when the latter was younger? We can often tell that two people are related because they have several similar physical traits, such as facial features or hair color. This is because children receive half of their DNA (genetic blueprints) from each parent. But what about something small, such as fingerprints—are they an inherited trait? Fingerprints are used to identify people because each person's fingerprints are unique, but people can have similar fingerprint patterns. This Father's Day you could do this activity with your family to investigate whether fingerprint patterns are random or influenced by genetics. You'll be able to see if your fingerprint pattern is just one more trait that you can thank your father (and mother) for giving you.   Background You started getting your own unique fingerprints even before you were born! During weeks 10 through 24 of development ridges form on the epidermis (outermost skin layer) of a fetus’s fingertips. The patterns that these ridges make on each finger and thumb are known as fingerprints, which are static and do not change with age—so an individual will have the same fingerprints from infancy to adulthood. The patterns change size, but not shape, as the person grows. (To get a better idea of how that works you can model the change in size by inking your fingerprint onto a balloon and then blowing up the balloon.) Because each person has unique fingerprints that do not change over time, these prints can be used for identification. For example, police use fingerprints to determine whether a particular individual has been at a crime scene.   Although the exact number, shape and spacing of the ridges changes from person to person, fingerprints can be sorted into three general categories based on their pattern type: loop, arch and whorl.   Materials

At least one pair of parents and their genetic son(s) or daughter(s) (The more members of the nuclear family that are available, the better the results will be.)

Magnifying glass (optional)

  Preparation

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Look at some drawings or pictures of the three basic fingerprint pattern types: loop, arch and whorl. In a loop pattern the ridges enter from either side, curve up and then exit usually from the same side they entered. In a whorl pattern the ridges are usually circular. In an arch pattern the ridges enter from one side, make a small rise in the center and exit generally on the opposite side.

Become familiar with what the different types look like so you can readily identify them. Note that there is some variation on these basic types, such as the "tented arch," which looks like a more sharply curved version of the typical arch.

  Procedure

Gather family members together so that you can look at their fingerprints as a group.

One at a time, look at each person's right index finger where their fingerprint is. By looking at the pattern on the finger, characterize the pattern as a whorl, arch or loop. You could use a magnifying glass to look at their finger more closely. What type of fingerprint pattern do they have?

Look at the fingerprint pattern of other family members, one at a time, and characterize each as one of the three basic patterns. What type of fingerprint patterns do other family members have? Do you see any trends?

Overall, does it look like fingerprint patterns are inherited? In other words, did siblings usually have the same fingerprint pattern and did people have fingerprint patterns in common with their parents?

Extra: Try this activity again but this time collect fingerprint pattern information from a lot more people to draw a better conclusion about whether fingerprint patterns are inherited. Try to get at least 15 related pairs of people (such as siblings) and at least 15 unrelated pairs of people. Compare the percentage of times that the pairs had matching fingerprint patterns. Are the percentages the same for related and unrelated pairs? Which is higher? What does this tell you about whether fingerprint patterns are genetic?

Extra: Try this activity with more people (related and/or unrelated). Are some fingerprint patterns more common than others?

Extra: Toes also have ridge patterns. Do "toe prints" follow the same rules as fingerprints?

 [break] Observations and results Did you see some examples of fingerprint patterns being inherited?   There is an inheritance component to fingerprint patterns but the genetics of how they are inherited are complicated. (Multiple genes are involved.) Fingerprints are also affected by a person's environment while developing in the womb. Because of this, you may have seen some examples of fingerprint patterns likely being inherited (such as a son and/or daughter having the same pattern type as their father). But this may not have always been the case for individuals you know to be closely related. To more clearly see how fingerprint patterns are inherited, you would need to use a much larger sample size, such as described in the first "Extra" step. Because each person's fingerprints are unique, and not even identical twins—who share the same DNA—have identical fingerprints, this also shows that fingerprints are not completely controlled by genetics.   More to explore Are one's fingerprints similar to those of his or her parents in any discernable way? , from Scientific American The inheritance of fingerprint patterns , from The American Journal of Human Genetics Fun, Science Activities for You and Your Family , from Science Buddies Are Fingerprint Patterns Inherited? , from Science Buddies

This activity brought to you in partnership with Science Buddies

• News & Opinion

Finally, Scientists Uncover the Genetic Basis of Fingerprints

Much like with a zebra’s stripes or a leopard’s spots, turing patterns explain how the distinctive patterns of human fingerprints form, a study finds..

James Gaines is a freelance science journalist in Seattle, Washington. He got his start at City University in London, where he received a master's degree in science journalism. Since then,...

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ABOVE: Fingerprint patterns are laid down in a set of waves, starting and spreading from distinct anatomical sites. The image illustrates three separate patterning waves (green, pink, and blue) converging to form a loop pattern. JAMES GLOVER

H ow the unique arrays of swirls, arches, and loops on the tips of our fingers form is a longstanding scientific enigma. Now, a paper published February 9 in Cell has solved the mystery, revealing not only the process by which fingerprints are formed, but also the genes responsible. And, it turns out, our distinctive prints stem from the same phenomenon that gives zebras their stripes and leopards their spots.

For several years, University of Edinburgh developmental biologists Denis Headon , James Glover , and their colleagues had been investigating how skin develops and matures, with a special interest in fingerprints. Fingerprints form before birth and may have evolved because they improve our ability to grip onto or feel the texture of objects. Still, scientists have long been stumped when it comes to the actual mechanisms by which these distinctive patterns develop. Understanding this process could help improve therapies for human congenital conditions like ectodermal dysplasias , Headon says, or even lead to better ways of regenerating skin.

It had been previously suggested fingerprints may arise from some form of pre-existing template, the way finger skin cells communicate, or even just simple wrinkling of the skin . To figure out which, if any, of these ideas is correct, Headon and his team used a variety of methods, including examining mouse and human tissue under a microscope, looking at the gene expression of single cells using single nucleus RNA sequencing, growing groups of cells on culture plates, and computer modeling.

“One thing that stood out to me was just the breadth of different approaches that they use in the paper,” Jeff Rasmussen, a developmental biologist at the University of Washington who was not involved in the work, tells The Scientist . “All of those different types of methodology really help them build [a] cohesive story.”

That story begins in the outer layer of body tissue, called the epithelium. Headon’s team ultimately found that fingerprints start out looking very similar to hair follicles: Both begin as small discs of cells on the epithelium, and in both cases, the cells turn on genes for a suite of proteins including EDAR and WNT—which are respectively related to how epithelial cells and cells in general migrate, differentiate, and mature. However, hair follicles go on to recruit cells from layers below the epithelium, forming a deep tube where hair will eventually grow. Slight differences in gene expression prevent this recruitment step from happening in fingerprints.

Those same differences in gene expression also seem to set up a Turing pattern, named for the English mathematician Alan Turing who first hypothesized its existence. Back in 1952, Turing suggested that natural biological patterns like stripes or spots could form in the presence of two molecules: a slow-moving activator and a fast-moving inhibitor. The activator would do three things: 1) tell cells to do something, such as make colored pigment; 2) tell cells to make more activator; and 3) tell cells to start making its inhibitor. Meanwhile, the inhibitor tells the cell to slow down activator production (and thus, ultimately, to make less of itself). This means that the activator and inhibitor are always made in overall proportion to each other, and the whole system can propagate from even a single initiation point.

The process may be admittedly a bit hard to picture, but after a while, this pattern of activation and inhibition can result in a series of stripes or spots depending on how the effect ripples outward from the starting point. In the new study, the researchers found that the WNT and EDAR proteins act as activators that create ridges in the forming skin, while proteins called BMPs (which are produced in response to WNT) act as inhibitors.

Today, we know that Turing patterns are responsible for things like zebra stripes and leopard spots , perhaps even the arrangement of fingers on our hands . Analogous phenomena in physics can even explain patterns like the stripes in sand dunes. In their paper, Headon and colleagues saw the phenomenon play out in the fingertips of mice with mutations to the gene for EDAR, when the stripey ridges of their fingerprints turned into spotty bumps—something difficult to explain with anything other than a Turing pattern, according to study author James Glover.

The pattern seems to originally start in three areas in humans: up near the nail, towards the center of the fingertip, and down near the crease from the first knuckle. As the Turing pattern matures, the fingerprint ridges then spread out as a series of waves from these initiation sites, eventually meeting in the middle and forming the unique fingerprint pattern each of us is born with.

“It’s the way that this patterning system is switched on in different locations and oriented in different locations . . . that’s what determines fingerprint type,” says Headon.

Some of the team’s colleagues— Benjamin Walker , Adam Townsend , and Andrew Krause —created an online simulator called VisualPDE where folks can experiment with Turing patterns and initiation sites. VisualPDE’s simulation is not unique to fingerprints but can illustrate how small changes can create unique patterns.

Rasmussen says he’d be interested in seeing if scientists could reprogram the process, creating hair follicles or prints where there had been none before. That’s the hope, Headon says: that somewhere down the road this work could lead to therapies for congenital conditions or medical regeneration.

But there’s also value in learning more about the many ways Turing’s patterns show up and connect life on Earth, and the subtle ways they can lead to such a wide variety of forms, says Glover. “From this, you can . . . gain a broader understanding of how patterns form in biology,” he explains. “These systems do vary between organs and species. So chipping away at looking at the different mechanisms, different systems will be really useful going forward.”

• Open access
• Published: 28 June 2011

A biochemical hypothesis on the formation of fingerprints using a turing patterns approach

• Diego A Garzón-Alvarado 1 &
• Angelica M Ramírez Martinez 2  

Theoretical Biology and Medical Modelling volume  8 , Article number:  24 ( 2011 ) Cite this article

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Fingerprints represent a particular characteristic for each individual. Characteristic patterns are also formed on the palms of the hands and soles of the feet. Their origin and development is still unknown but it is believed to have a strong genetic component, although it is not the only thing determining its formation. Each fingerprint is a papillary drawing composed by papillae and rete ridges (crests). This paper proposes a phenomenological model describing fingerprint pattern formation using reaction diffusion equations with Turing space parameters.

Several numerical examples were solved regarding simplified finger geometries to study pattern formation. The finite element method was used for numerical solution, in conjunction with the Newton-Raphson method to approximate nonlinear partial differential equations.

Conclusions

The numerical examples showed that the model could represent the formation of different types of fingerprint characteristics in each individual.

Fingerprints represent a particular characteristic for each individual [ 1 – 10 ]. These enable individuals to be identified through the embossed patterns formed on fingertips. Characteristic patterns are also formed on the palms of the hands and soles of the feet [ 1 ]. Their origin and development is still unknown but it is believed to have a strong genetic component, although it is not the only thing determining its formation. Each fingerprint is a papillary drawing composed by papillae and rete ridges (crests) [ 1 – 6 ]. These crests are epidermal ridges having unique characteristics [ 1 ].

Characteristic fingerprint patterns begin their formation by the sixth month of gestation [ 1 – 6 ]. Such formation is unchangeable until an individual's death. No two fingerprints are identical; they thus become an excellent identification tool [ 1 , 2 ]. Various theories have been proposed concerning fingerprint formation; among the most accepted are those that consider differential forces on the skin (mechanical theory) [ 1 , 6 , 7 ] and those having a genetic component [ 1 , 6 , 10 ]. From a mechanical point of view, it has been considered that fingerprints are produced by the interaction of nonlinear elastic forces between the dermis and epidermis [ 7 ]. This theory considers that the growth of the fingers in the embryo (dermis) is different than growth in the epidermis, resulting in folds in the skin surface [ 7 ]. Figure 1 shows a mechanical explanation for the formation of the folds that give rise to fingerprints.

Fingerprint formation Taken from [ 7 ]. An explanation for the formation of grooves forming a fingerprint. The first figure on the left (top) shows the epidermis and dermis. Right: rapid growth of the basal layer. Below (right) compressive loads are generated. Left: generation of wrinkles due to mechanical loads.

Fingers are separated from each other in the fetus during embryonic formation during the sixth week, generating certain asymmetries in each finger's geometry [ 10 ]. The fingertips begin to be defined from the seventh week onwards [ 1 , 10 ]. The first waves forming the fingerprint begin to take shape from the tenth week; these are patterns which keep growing and deform until the whole fingertip has been completed [ 10 ]. Fingerprint formation finishes at about week 19 [ 10 ]. From this time on, the fingerprints stop changing for the rest of an individual's lifetime. Figure 2 shows the stages of fingerprint formation.

Stages of Fingerprint formation Taken from [ 9 ]. Fingerprint formation. a) primary formation, b) the first loop is generated, c) development. d) complete formation, e) side view, f) wear.

Alternately to the proposal made by Kucken [ 7 ], this paper presents a hypothesis about fingerprint formation from a biochemical effect. The proposed model uses a reaction-diffusion-convection (RDC) system. Following a similar approach to that used in [ 11 , 12 ], a glycolysis reaction model has been used to simulate the appearance of patterns on fingertips. A solution method on three dimensional surfaces using total Lagrangian formulation is provided for resolving the reaction diffusion (RD) equations. Equations whose parameters are in the Turing space have been used for pattern formation; therefore, the patterns found are Turing patterns which are stable in time and unstable in space. Such stability is similar to that found in fingerprint formation. The model explained in [ 11 ] was used for fold growth where the formation of the folds depends on the concentration of a biochemical substance present on the surface of the skin.

Reaction-diffusion (RD) system

Following a biochemical approach, it was assumed that a RD system could control fingerprint pattern formation. For this purpose, an RD system was defined for two species, given by (1):

where u 1 and u 2 were the concentrations of chemical species present in reaction terms f and g, d was the dimensionless diffusion coefficient and γ was a constant in a dimensionless system [ 12 ].

RD systems have been extensively studied to determine their behavior in different scenarios regarding parameters [ 12 , 13 ], geometrics [ 13 , 14 ] and for different biological applications [ 15 – 17 ]. One area that has led to developing extensive work on RD equations has been the formation of patterns which are stable in time and unstable in space [ 18 , 19 ]. In particular, Turing [ 20 ], in his book, "The chemical basis of morphogenesis," developed the necessary conditions for spatial pattern formation. The conditions for pattern formation determined Turing space given by the following restrictions (2):

Equations ( 1 ) and constraints (2) led to developing the dynamic system branch of research [ 11 , 18 ]: Turing instability. Turing pattern theory has helped explain the formation of complex biological patterns such as the spots found on the skin of some animals [ 15 , 16 ] and morphogenesis problems [ 10 ]. It has also been experimentally proven that the behavior of some RD systems produce traveling wave and stable spatial patterns [ 21 – 23 ].

The equations used for predicting pattern formation in this paper were those for glycolysis [ 24 ], given by:

Epidermis strain

The ideas suggested in [ 10 , 25 , 26 ] were used to strain the fingertip surface regarding the substances (morphogens) present in the domain; i.e. surface S , was strained according to its normal N and the amount of molecular concentration (u 2 ) at each material point, therefore:

where K was a constant determining growth rate.

Including the term for surface growth (equation ( 4 )) modifies equation ( 1 ), which presented a new term taking into account the convection and dilation of the domain given by:

The finite element method [ 27 ] was used to solve the RDC system described above in (5) and the Newton-Raphson method [ 28 ] to solve the non-linear system of partial differential equations arising from the formulation. The seed coat surface pattern growth field was imposed by solving equation ( 4 ), giving the new configuration (current) and velocity field to be included in the RD problem.

The solution of the RD equations by using the finite element method is shown below.

Solution for RDC system

Formulating the RD system, including convective transport, could be written as (6) [ 24 ]:

where u 1 and u 2 were the RD system's chemical variables. This equation could also be written in terms of total derivative (7) [ 24 ]:

where it should be noted that

According to the description in [ 29 ], then the RDC system in the initial configuration, or reference Ω 0 (with coordinates in X(x) ), was given by the following equation, written in terms of material coordinates:

Therefore, equation ( 8 ) gave the general weak form for (9) [ 27 ].

where U was either of the two studied species (U 1 or U 2 ), W was the weighting, J was the Jacobian (and equaled the determinant for strained gradient F ) and C -1 was the inverse of the Cauchy-Green tensor on the right [ 27 , 28 ].

In the case of total Lagrangian formulation, the calculation was always done in the initial reference configuration. Therefore, the solution for system (8) and (9) began with the discretization of the variables U 1 and U 2 by (10) [ 27 ]:

where nnod was the number of nodes, U 1 and U 2 were the vectors containing U 1 and U 2 values at nodal points and superscript h indicated the variable discretization in finite elements. The Newton-Raphson method residue vectors were obtained by choosing weighting functions equal to shape functions (Galerkin standard) given by [ 27 ] (11):

where J was strained gradient determinant, C -1 was the inverse of the Cauchy-Green tensor on the right p, s = 1, ..., nnod and I, J = 1, .., dim , where dim was the dimension in which the problem was resolved. Therefore, using equations ( 11 ) and ( 12 ), the Newton-Raphson method could be implemented to solve the RD system using its material description. It should be noted that (11) and (12) were integrated in the initial configuration [ 29 ].

Applying the velocity fields

Equation ( 4 ) was used to calculate the movement of the mesh and the velocity at which the domain was strained, integrated by Euler's method, given by [ 28 ]:

where S t+dt and S t were the surface configuration in state t and t+dt. Therefore, velocity was given by (14):

where the velocity term had direction and magnitude depending on the material point of surface S.

Aspects of computational implementation

The formulation described above was used for implementing the RD model using the finite element method. It should be noted that although the surface was orientated in a 3D space, the numerical calculations were done in 2D. The normal for each element (Z') was thus found and the prime axes (X'Y') forming a parallel plane to the element plane were located. The geometry was enmeshed by using first order triangular elements with three nodes. Therefore, the calculation was simplified from a 3D system to a system which solved two-dimensional RD models at every instant of time. The relationship between the X'Y'Z 'and XYZ axes could be obtained by a transformation matrix T [ 29 ].

A program in FORTRAN was used for solving the system of equations resulting from the finite element method with the Newton-Raphson method and the following examples were solved on a Laptop having 4096 MB of RAM and 800 MHz processor speed. In all cases, the dimensionless problem was solved with random conditions around the steady state [ 12 , 24 ] for the RD system.

The mesh used is shown in Figure 3 . This mesh was made on a 1 cm long, 0.5 cm radius ellipsoid. The number of triangular elements was 5,735 and the number of nodes 2,951. The time step used in the simulation was dt = 2 (dimensionless). The total simulation time was t = 100.

Mesh used in the simulation . Mesh used in developing the problem. In this figure the mesh has 5735 triangular elements and 2951 nodes.

The dimensionless parameters of the RD system of glycolysis were given by d = 0.08, δ = 1.2 and κ = 0.06 for Figure 4a , 4b and 4c ) d = 0.06, δ = 1.2 and κ = 0.06. Therefore the steady state was given at the point of equilibrium ( u 1 ,u 2 ) 0 = (0.8,1.2), so that the initial conditions were random around steady state [ 12 , 14 ]. K = 0.05 in equations ( 4 ) and ( 13 ) was used for all glycolysis simulations.

Results of the simulation of Fingerprint a) photo of the fingerprints, b) results for parameters d = 0 .08, δ = 1.2 and κ = 0.06. c) results for parameters d = 0.06, δ = 1.2 and κ = 0.06.

Figure 4b )- 4c ) shows surface pattern evolution. The formation of labyrinths and blind spots in the grooves approximating the shape of the fingerprint patterns can be observed (Figure 4a ). The pattern obtained was given by bands of high concentration of a chemical species, for which the domain had grown in the normal direction to the surface and hence generated its own fingerprint grooves.

Figure 5 shows temporal evolution during the formation of folds and furrows on the fingertip. In 5a) shows that there was no formation whatsoever of folds. In b), small bumps began to form, in the entire domain, which continued to grow and form the grooves, as shown in Figure 5f ).

Stages of Fingerprint formation simulation . Different instants of time in the evolution of the folds and grooves forming the fingerprint. a) t = 0, b) t = 20, c) t = 40, d) t = 60, e) t = 80 f) t = 100. Time is dimensionless.

Discussion and conclusions

This paper has presented a phenomenological model based on RD equations to predict the formation of rough patterns on the tips of the fingers, known as fingerprints. The application of the RD models with Turing space parameters is an area of constant work and controversy in biology [ 31 , 32 ] and has attracted recent interest due to the work of Sick et al ., [ 32 ] confirming the validity of RD equations in a model of the appearance of the hair follicle. From this point of view, the work developed in this article has illustrated RD equation validity for representing complex biological patterns, such as patterns formed in fingerprints.

This paper proposes the existence of a reactive system (activator-inhibitor) on the skin surface giving an explanation for the patterns found. The high stability of the emergence of the patterns can also be explained, i.e. the repetition of the patterns was due to a specialized biochemical system allowing the formation of wrinkles in the fingerprints and skin pigmentation.

The formulation of a system of RD equations acting under domain strain was programmed to test this hypothesis. Continuum mechanics thus led to the general form of the RD equations in two- and three-dimensions on domains presenting strain. The resulting equations were similar to those shown in [ 33 ], where major simplifications were carried out on field dilatation. The RD system was solved by the finite element method, using a Newton-Raphson approach to solve the nonlinear problem. This allowed longer time steps and obtaining solutions closer to reality. The results showed that RD equations have continuously changing patterns.

Additionally, it should be noted that the results obtained with the RD mathematical model was based on assumptions and simplifications that should be discussed for future models.

The model was based on the assumption of a tightly coupled biochemical system (non-linear) between an activator and an inhibitor generating Turing patterns. As far as the authors know, this assumption has not been tested experimentally, so the model is a hypothesis to be tested in future research. It is also feasible, as in other biological models (see [ 7 ]), that there were a large number of chemical factors (morphogens) involved, interacting to form superficial patterns found in the fingers. In the case of patterns with superficial roughness, the biochemical system could also interact with its own mechanical growth factors. Therefore, determining the exact influence of each biochemical and mechanical factor on the formation of surface patterns becomes an experimental challenge that will reveal the morphogenesis of fingerprints.

Kucken M, Newell A: Fingerprint formation. Journal of theoretical biology. 2005, 235 (1): 71-83. 10.1016/j.jtbi.2004.12.020.

Article   PubMed   Google Scholar  

Kucken M, Newell A: A model for fingerprint formation. EPL (Europhysics Letters). 2004, 68: 141-146. 10.1209/epl/i2004-10161-2.

Article   Google Scholar  

Bonnevie K: Studies on papillary patterns of human fingers. Journal of Genetics. 1924, 15 (1): 1-111. 10.1007/BF02983100.

Hale A: Breadth of epidermal ridges in the human fetus and its relation to the growth of the hand and foot. The Anatomical Record. 1949, 105 (4): 763-776. 10.1002/ar.1091050409.

Article   CAS   PubMed   Google Scholar  

Hale A: Morphogenesis of volar skin in the human fetus. American Journal of Anatomy. 1952, 91 (1): 147-181. 10.1002/aja.1000910105.

Hirsch W: Morphological evidence concerning the problem of skin ridge formation. Journal of Intellectual Disability Research. 1973, 17 (1): 58-72.

Article   CAS   Google Scholar  

Kucken M: Models for fingerprint pattern formation. Forensic science international. 2007, 171 (2-3): 85-96. 10.1016/j.forsciint.2007.02.025.

Jain AK, Prabhakar S, Pankanti S: On the similarity of identical twin fingerprints. Pattern Recognition. 2002, 35 (11): 2653-2663. 10.1016/S0031-3203(01)00218-7.

Bonnevie K: Die ersten Entwicklungsstadien der Papillarmuster der menschlichen Fingerballen. Nyt Mag Naturvidenskaberne. 1927, 65: 19-56.

Google Scholar  

Cummins H, Midlo C: Fingerprints, palms and soles: An introduction to dermatoglyphics. 1961, Dover Publications Inc. New York, 778:

Cartwright J: Labyrinthine Turing pattern formation in the cerebral cortex. Journal of theoretical biology. 2002, 217 (1): 97-103. 10.1006/jtbi.2002.3012.

Madzvamuse A: A numerical approach to the study of spatial pattern formation. D Phil Thesis. 2000, United Kingdom: Oxford University

Meinhardt H: Models of biological pattern formation. 1982, Academic Press, New York, 6:

Madzvamuse A: Time-stepping schemes for moving grid finite elements applied to reaction-diffusion systems on fixed and growing domains. Journal of computational physics. 2006, 214 (1): 239-263. 10.1016/j.jcp.2005.09.012.

Madzvamuse A, Sekimura T, Thomas R, Wathen A, Maini P: A moving grid finite element method for the study of spatial pattern formation in Biological problems. Morphogenesis and Pattern Formation in Biological Systems-Experiments and Models, Springer-Verlag, Tokyo. Edited by: T. Sekimura, S. Noji, N. Nueno and P.K. Maini. 2003, 59-65.

Chapter   Google Scholar  

Madzvamuse A, Wathen AJ, Maini PK: A moving grid finite element method applied to a model biological pattern generator* 1. Journal of computational physics. 2003, 190 (2): 478-500. 10.1016/S0021-9991(03)00294-8.

Madzvamuse A, Thomas RDK, Maini PK, Wathen AJ: A numerical approach to the study of spatial pattern formation in the Ligaments of Arcoid Bivalves. Bulletin of mathematical biology. 2002, 64 (3): 501-530. 10.1006/bulm.2002.0283.

Gierer A, Meinhardt H: A theory of biological pattern formation. Biological Kybernetik. 1972, 12 (1): 30-39. 10.1007/BF00289234.

Chaplain MG, Ganesh AJ, Graham I: Spatio-temporal pattern formation on spherical surfaces: Numerical simulation and application to solid tumor growth. J Math Biol. 2001, 42: 387-423. 10.1007/s002850000067.

Turing A: The chemical basis of morphogenesis. Philosophical Transactions of the Royal Society of London Series B, Biological Sciences. 1952, 237 (641): 37-72. 10.1098/rstb.1952.0012.

De Wit A: Spatial patterns and spatiotemporal dynamics in chemical systems. Adv Chem Phys. 1999, 109: 435-513.

CAS   Google Scholar  

Maini PK, Painter KJ, Chau HNP: Spatial pattern formation in chemical and biological systems. Journal of the Chemical Society, Faraday Transactions. 1997, 93 (20): 3601-3610. 10.1039/a702602a.

Kapral R, Showalter K: Chemical waves and patterns. 1995, Kluwer Academic Pub, 10:

Book   Google Scholar  

Garzón D: Simulación de procesos de reacción-difusión: aplicación a la morfogénesis del tejido Óseo. 2007, Universidad de Zaragoza, Ph.D. Thesis

Harrison LG, Wehner S, Holloway D: Complex morphogenesis of surfaces: theory and experiment on coupling of reaction-diffusion patterning to growth. Faraday Discussions. 2002, 120: 277-293.

Holloway D, Harrison L: Pattern selection in plants: coupling chemical dynamics to surface growth in three dimensions. Annals of botany. 2008, 101 (3): 361-

Article   PubMed Central   PubMed   Google Scholar  

T.J.R , Hughes : The finite element method: linear static and dynamic finite element analysis. 2003, New York: Courier Dover Publications

Hoffman J: Numerical methods for engineers and scientists. 1992, McGraw-Hil

Holzapfel GA: Nonlinear solid mechanics: a continuum approach for engineering. 2000, John Wiley & Sons, Ltd

Belytschko T, Liu W, Moran B: Nonlinear finite elements for continua and structures. 2000, John Wiley and Sons, 36:

Crampin EJ, Maini PK: Reaction-diffusion models for biological pattern formation. Methods and Applications of Analysis. 2001, 8 (3): 415-428.

Sick S, Reinker S, Timmer J, Schlake T: WNT and DKK determine hair follicle spacing through a reaction-diffusion mechanism. Science. 2006, 314 (5804): 1447-1450. 10.1126/science.1130088.

Madzvamuse A, Maini PK: Velocity-induced numerical solutions of reaction-diffusion systems on continuously growing domains. Journal of computational physics. 2007, 225 (1): 100-119. 10.1016/j.jcp.2006.11.022.

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Acknowledgements

This work was financially supported by Division de Investigación de Bogotá, of Universidad Nacional de Colombia, under title Modelling in Mechanical and Biomedical Engineering, Phase II.

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Associate Professor, Mechanical and Mechatronics Engineering Department,, Universidad Nacional de Colombia, Engineering Modeling and Numerical Methods Group (GNUM),, Bogotá,, Colombia

Diego A Garzón-Alvarado

Professor, Mechanical Engineering Department,, Fundación Universidad Central,, Bogotá,, Colombia

Angelica M Ramírez Martinez

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Garzón-Alvarado, D.A., Ramírez Martinez, A.M. A biochemical hypothesis on the formation of fingerprints using a turing patterns approach. Theor Biol Med Model 8 , 24 (2011). https://doi.org/10.1186/1742-4682-8-24

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DOI : https://doi.org/10.1186/1742-4682-8-24

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Development of submerged and successive latent fingerprints: a comparative study

• Neeti Kapoor 1 ,
• Shagufa Ahmed 2 ,
• Ritesh K. Shukla 3 &
• Ashish Badiye   ORCID: orcid.org/0000-0001-8097-7249 1  

Egyptian Journal of Forensic Sciences volume  9 , Article number:  44 ( 2019 ) Cite this article

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The use of water to destroy evidences in criminal cases is common. It is uncommon to believe the usefulness of evidences recovered underwater in terms of its forensic significance regarding personal identification especially by the investigating officers, who are responsible to collect and analyse the evidences. In this study, two main factors were considered which may impact the condition of fingerprint evidences: firstly, the time duration for which the evidence remains submerged in water (0.5 h, 24 h, 48 h, 120 h), and secondly, the succession or the number of prints given by the same finger one after the other (5 subsequent prints).

The result of this study revealed the successful development of latent fingerprint using Robin blue and silver magnetic powders on 8 different non-porous surfaces.

The developed prints provide significant individual characteristics; hence, the evidentiary value of the objects found submerged in water should not be undervalued.

Fingerprints are a distinctive feature of individuals and are one of the oldest and most widely accepted forensic evidence used to establish personal identity (Houck and Siegel 2006 ; Gaensslen 2009 ). A report of Federal Bureau of Investigation stated that ‘fingerprint identification is the most affirmative form of personal identification which is based on the inimitable and static arrangement of ridge details present on the fingertip’ (FBI 1990 ).

The most familiar prints encountered at the scene of the crime are the latent prints. These are invisible prints retained on a substrate on account of various secretions from the body. Fingerprints can be deposited on a number of substrates, broadly classified as porous, non-porous, and semi-porous surfaces. Non-porous surfaces are characteristically non-absorbent, and hence, the fingerprint residues that are superficially present are more prone to be disturbed.

Fingerprints can be deposited through different mechanisms, on a variety of substrates, and can be exposed to various environmental conditions. This interaction among fingerprint composition, deposition surface, and environment is explained in the fingerprint triangle of interaction which describes that the technique or process to be selected and used to enhance latent prints is governed by the understanding of and the interplay between these three elements (Sears et al. 2012 ). Because of this concept, there is a vast range of latent fingerprint visualization and enhancement techniques for the differing interactions between the fingerprint composition, substrate, and environment interactions (Dhall and Kapoor 2016 ; Bradshaw et al. 2008 ).

This environmental effect can be enhanced when the print is exposed to destructive environments, either through the nature of the crime scene or through an intentional attempt to destroy evidence by the perpetrator of a crime. The fingerprint evidence subjected to such destructive conditions is generally neglected due to the misconception of impossible recovery (Deans 2006 ).

Criminals consider water and water bodies as perfect disposal sites for weapons of assault and other evidences which may connect them to the crime. These evidences may be retrieved from diverse aquatic environments (Becker 2006 ). Many researchers were successful at recovering latent fingerprints from surfaces exposed to water (Armstrong and Erskine 2011 ; Becker 1995 ; Yuille 2009 ; Beresford et al. 2012 ; Kabklang et al. 2009 ; Daéid et al. 2008 ; Maslanka 2016 ; Jasuja et al. 2015 ; Beaudoin 2004 ; Cuce et al. 2004 ; Frank and Almog 1993 ; Polimeni et al. 2004 ; Olenik 1984 ; Soltyszewski et al. 2007 ; Onstwedder 1989 ; Wood and James 2009 ; Vandiver 1976 ).

Considering the earlier research, it is evident that the value of recovering and enhancing fingerprints found at a crime scene can be invaluable in leading to the identification of a person of interest in relation to a crime. Keeping this view into the mind, in this study, we have made an attempt to develop a method to recover and identify the latent fingerprint present on eight different non-porous surfaces that were submerged into the water for different duration of time as well as five successive prints.

Methodology

The study was conducted in the month of February 2018. The average max. temp. was 31.2  (±1) °C, and the average humidity was about 45%. In this study, eight non-porous substrates namely glass, laminated paper, plastic mug, floor tile, credit card (golden glitter), debit card (red), aluminium foil, and painted iron saw were used as surfaces for fingerprint deposition. The surfaces were thoroughly cleaned with an alcohol swab. The markings ‘1’, ‘2’, ‘3’, ‘4’, and ‘5’ have been done on the surfaces to note the number and position of the five successive prints, using a permanent marker.

Fingerprint deposits

Fingerprints were taken from a single donor so as to maintain consistency in the sampling. Prints were deposited on the surfaces as described by Sears et al. ( 2012 ). Natural prints were deposited onto the substrates with no previous hand-washing or post eccrine (wearing gloves) or sebaceous (rubbing fingers on the nose) grooming of prints. This range of fingerprints allowed to mimic the real-life scenario in which different people shall have secreted varying amount of sweat and even concentration of the sweat composition would also vary in their prints. Furthermore, sweating is also affected by the physiological state of an individual that would lead to variation in the amount of sweat secretions which could affect the nature/form of fingerprint impression found at the crime scene (Kuno 1934 ).

Submergence

In this study, instant dry prints were developed first. Thereafter, the same surface was thoroughly cleaned with an alcohol swab and air dried, and again, fresh prints were taken on that surface. Then, the surfaces were allowed to submerge in the container with water for the stipulated time periods, i.e. 0.5 h, 24 h, 48 h, and 120 h, respectively. After the completion of the set duration, the surface was taken out from the water and allowed to air dry at room temperature for 45 min.

Fingerprint development powders

The prints were then developed by powder dusting method using separate brushes (Badiye and Kapoor 2015 ). The process was repeated for two latent print development powders—Robin® blue powder and commercially available silver magnetic dual powder. To avoid contamination and the unintentional prints deposition, gloves were worn throughout.

The developed prints and the photograph of the prints (taken using a tripod-mounted Nikon D-3100 DSLR camera with 18–55 mm kit lens w/o flash) were analysed for designating a score based on the fingerprint quality assessment scale as described below (Castelló et al. 2013 ; Soltyszewski et al. 2007 ; Devlin 2011 ; Stow and McGurry 2006 ):

Result and discussion

Latent fingerprints were developed after submergence in water from all the eight surfaces used in this study. The quality of prints developed in relation to the two variables studied, i.e. duration of submergence and succession of prints, is depicted in Tables  1 , 2 , 3 , 4 , 5 , 6 , 7 , and 8 . The developed latent fingerprints, submerged as well as successive, on all the selected substrates are shown in the tables in the Additional file 1 .

Overall 400 prints were developed and examined in this study. Half of the total prints (200) were developed using Robin powder blue, of which 174 prints were identifiable with a score of 3–5 and only 26 prints were not properly identifiable with score 2 or 1. However, in case of commercially available silver magnetic dual powder, 25 prints developed on the aluminium foil surface had negligible contrast and hence were discarded. Remaining 175 prints were developed by silver magnetic powder, in which 152 prints were identifiable with a score of 3–5 and only 23 prints were unidentifiable with score 2 and 1 (Fig.  1 ).

Fingerprint assessment score in the succession prints developed using both the powders

The best quality identifiable prints were obtained on glass (Tables  9 and 10 ), laminated paper, and floor tile surfaces with 49, 48, and 48 (out of 50) prints respectively with a score of 5, 4, or 3. In addition, floor tile, debit card (red), and painted iron saw surfaces gave 44, 43, and 39 (out of 50) identifiable prints with a score of 5, 4, or 3, respectively, while 21 (out of 50) prints remained unidentified on credit card (golden glitter) surfaces with a score of 2 or 1. In this order, Robin blue powder provided a better quality of prints on credit card (golden glitter) as compared to silver magnetic powder. The reason behind the low quality of print was the conflicting glitter of silver magnetic powder as well as golden glitter of the credit card which rendered poor contrast with the developing powder. On aluminium foil, none of the prints could be developed/visualized with silver magnetic powder due to poor contrast while only one print developed out of the 25 prints, by Robin powder blue, was poor (Table  11 ).

It is known that the quality of latent fingerprints naturally deteriorates over time (Baniuk 1990 ; Yuille 2009 ; Archer et al. 2005 ; Midkiff 1993 ), and our results are also similar to this. In this order, only 2 prints (out of 75) were not identified, when they were developed after 0.5 h and 24 h. However, when these prints were developed after 48 and 120 h in water using a similar method, the quality of developed prints significantly deteriorated. After 48 and 120 h in water 11 and 32 (out of 75) prints, respectively, were scored 2 or 1. Our results show that prolonged submergence deteriorates the quality of developed prints (Figs.  2 and 3 ).

Development of prints by silver magnetic dual powder

Development of prints by Robin powder blue

Our results are in accordance with Castello et al., who found that up to a submersion time of 3 days, the development results were similar for the glass and plastic surfaces with 4 or 5 grades on scale which indicates clear well-identified prints. From the fifth day, significant differences in print development were observed (Castelló et al. 2013 ). Similarly, some other studies (Soltyszewski et al. 2007 ; Devlin 2011 ; Stow and McGurry 2006 ; Madkour et al. 2017 ; Trapecar 2012a , b ; Rohatgi et al. 2015 ; Trapecar and Pantic 2017 ) were also in consensus with the result that the clarity of the prints decreases with an increase in submersion period. By extending the duration of submersion in water, the number of developed useable fingerprints were reduced, and more finger marks remained undeveloped.

It is a well-believed principle that ‘everything changes with the passage of time’. There are various changes that occur based on several factors along with the time duration (Cadd et al. 2015 ). The lengthier the duration, the greater is the degradation (Girod et al. 2012 ). Additionally, the water-soluble components of the fingerprint residue are more prone to destructive forces such as water, high temperatures, and low humidity (Iten 2012 ; Barnum and Klasey 1997 ). If a print is wetted, then the aqueous components of the print are removed, thereby leaving less available components for the powder to adhere to, but the enhancement is still possible and is reported in the current study and in literature (Dhall and Kapoor 2016 ). On analysing the impact of succession of prints, it was found that the score decreases with subsequent prints. In the first, second, and third prints, a total of 8, 5, and 6 (out of 75) prints respectively were unrecognizable with a score of 1 or 2, while in the fourth and fifth subsequent prints, 11 and 19 (out of 75) were unidentifiable with a score less than 3.

The procedure adapted to fingerprint deposition on the surfaces was uniformly maintained to have consistent quality prints before submersion. After all, the results are dependent on the initial prints too. A systematic process can also result in inconsistent fingerprint deposition . There are certain factors that influence latent fingerprint deposition; some may be controlled while some may not be (Fieldhouse 2011 ). It is unlikely that the chemical and physical composition of two fingerprints will be identical, thus affecting the credibility of conclusions made. Factors associated with the donor (chemical composition) include smoking, illness, medication, age, gender, race, and diet. The factors associated with fingerprint deposition (physical) include force applied during deposition, duration of surface contact, angle of surface contact, substrate, and residue quantity due to washing.

This study is restricted to simulated stagnant water conditions in a laboratory setup, while the natural water bodies that are encountered during investigations of crimes are diverse from laboratory condition. They are subject to a number of internal and external factors including temperature, wind, aquatic flora and fauna, pH, precipitation, and enclosure.

This study aimed to highlight the notion that evidences recovered under water should be tested for prints as required, irrespective of the amount of time spent beneath water. Considering the use of eight different substrates in the current study, for the effect of succession of prints and submergence in water, on latent fingerprints, was based on our hypothesis that these two factors may played a vital role in a degrading the print quality and texture. Our results were at par with the hypothesis confirming that increasing succession of prints as well as increasing duration of time, the surface containing prints that were exposed to water reduces the quality of visualized prints. Both Robin blue powder and Silver magnetic dual powder have proved to be efficacious in this study. Both the powders are easily available, low-cost, and versatile. Powder dusting followed by light brushing method gave notable results. Further research is essential in order to explore the impact of confounding variables on submerged and successive latent print development.

Availability of data and materials

All data generated or analysed during this study are included in this published article (and its supplementary information file/additional file).

Abbreviations

No contrast

Robin powder blue

Silver magnetic dual powder

Archer NE, Charles Y, Elliott JA, Jickells S (2005) Changes in the lipid composition of latent fingerprint residue with time after deposition on a surface. Forensic Sci Int 154(2–3):224–239

Article   CAS   Google Scholar  

Armstrong EJ, Erskine KL (2011) Water-related death investigation: practical methods and forensic applications. CRC Press, Boca Raton

Google Scholar  

Badiye A, Kapoor N (2015) Efficacy of Robin powder blue for latent fingerprint development on various surfaces Egypt. J Foren Sci 5(4):166–173

Baniuk K (1990) Determination of age of fingerprints. Forensic Sci Int 46(1–2):133–137

Article   Google Scholar  

Barnum CA, Klasey DR (1997) Factors affecting the recovery of latent prints on firearms. Print 13(3):6–9

Beaudoin A (2004) New technique for revealing latent fingerprints on wet, porous surfaces: oil red O. J Forensic Identif 54(4):413–421

Becker RF (1995) The underwater crime scene: underwater crime investigative techniques. Thomas, Springfield

Becker RF (2006) Underwater forensic investigation. Pearson Prentice Hall, Upper Saddle River

Beresford AL, Brown RM, Hillman AR, Bond JW (2012) Comparative study of electrochromic enhancement of latent fingerprints with existing development techniques. J Forensic Sci 57(1):93–102

Bradshaw G, Bleay S, Deans J, NicDaeid N (2008) Recovery of fingerprints from arson scenes: part 1-latent fingerprints. J Forensic Identif 58(1):54

Cadd S, Islam M, Manson P, Bleay S (2015) Fingerprint composition and aging: a literature review. Sci Justice 55(4):219–238

Castelló A, Francés F, Verdú F (2013) Solving underwater crimes: development of latent prints made on submerged objects. Sci Justice 53:328–331

Cuce P, Polimeni G, Lazzaro AP, Fulvio GD (2004) Small particle reagents technique can help to point out wet latent fingerprints. Forensic Sci Int Suppl.146:7–8

Daéid NN, Carter S, Laing K (2008) Comparison of vacuum metal deposition and powder suspension for recovery of fingerprints on wetted nonporous surfaces. J Forensic Identif 58:600–613

Deans J (2006) Recovery of fingerprints from fire scenes and associated evidence. Sci Justice 46(3):153–168

Devlin B (2011) Recovery of latent fingerprints after immersion in various aquatic conditions. George Mason University, Degree of Master Thesis, Fairfax

Dhall JK, Kapoor A (2016) Development of latent prints exposed to destructive crime scene conditions using wet powder suspensions. Egypt J Forensic Sci 6(4):396–404

Federal Bureau of Investigation (1990) The science of fingerprinting. Department of Justice, Washington DC

Fieldhouse S (2011) Consistency and reproducibility in fingermark deposition. Forensic Sci Int 207(1–3):96–100

Frank A, Almog J (1993) Modified SPR (small particle reagent) for latent fingerprint development on wet, dark objects. J Forensic Identif 43(3):240–244

Gaensslen RA (2009) Fingerprints. Forensic science: an introduction to scientific and investigative techniques, 3rd edn. CRC Press/Taylor & Francis Group, Boca Raton, pp 355–375

Girod A, Ramotowski R, Weyermann C (2012) Composition of fingermark residue: a qualitative and quantitative review. Forensic Sci Int 223:10–24

Houck MM, Siegel JA (2006). Fundamentals of forensic science. Amsterdam, Boston: Elsevier/Academic Press

Iten A (2012) Optimal temperatures for latent print recovery. Forensic Mag Available from: https://www.forensicmag.com/article/2012/06/optimal-temperatures-latent-print-recovery

Jasuja OP, Kumarb P, Singh G (2015) Development of latent fingermarks on surfaces submerged in water: optimization studies for phase transfer catalyst (PTC) based reagents. Sci Justice 55:335–342

Kabklang P, Riengrojpitak S, Suwansamrith W (2009) Latent fingerprint detection by various formulae of SPR on wet non-porous surfaces. J Sci Res Chula Univ 34(2):59–64

CAS   Google Scholar  

Kuno Y (1934) Physiology of human perspiration. J. and A. Churchill, London

Madkour S, Sheta A, Dine FAE, Elwakeel Y, AbdAllah N (2017) Development of latent fingerprints on non-porous surfaces recovered from fresh and sea water. Egypt J Forensic Sci 7:3

Maslanka DS (2016) Latent fingerprints on a nonporous surface exposed to everyday liquids. J Forensic Identif 66(2):137

Midkiff CR (1993) Lifetime of a latent print: how long? Can you tell? J Forensic Identif 43(4):386

Olenik JH (1984) Super glue®, a modified technique for the development of latent prints. J Forensic Sci 29(3):881–884

Onstwedder J (1989) Small particle reagent: developing latent prints on water-soaked firearms and effect on firearms analysis. J Forensic Sci 134(2):321–327

Polimeni G, Feudale BF, Saravo L, Fulvio GD (2004) A novel approach to identify the presence of fingerprints on wet surfaces. Forensic Sci Int Suppl.146:45–46

Rohatgi R, Sodhi GS, Kapoor AK (2015) Small particle reagent based on crystal violet dye for developing latent fingerprints on non-porous wet surfaces. Egypt J Forensic Sci 5(4):162–165

Sears VG, Bleay SM, Bandey HL, Bowman VJ (2012) A methodology for finger mark research. Sci Justice 52(3):145–160

Soltyszewski I, Moszczynski J, Pepinski W, Jastrzebowska S, Makulec W, Zbiec R et al (2007) Fingerprint detection and DNA typing on objects recovered from water. J Forensic Identif 57(5):681–687

Stow KM, McGurry J (2006) The recovery of finger marks from soot-covered glass fire debris. Sci Justice 46(1):3–14

Trapecar M (2012a) Finger marks on glass and metal surfaces recovered from stagnant water. Egypt J Forensic Sci 2:48–53

Trapecar M (2012b) Fingerprint recovery from wet transparent foil. Egypt J Forensic Sci 2:126–130

Trapecar M, Pantic S (2017) A continuation study on fingerprint recovery from wet metal surfaces. Eur J Forensic Sci 4(1):12–16

Vandiver JV (1976) Fingerprints. Identif News 26(5):3–4

Wood MA, James T (2009) Latent fingerprint persistence and development techniques on wet surfaces. Fingerprint Whorld 35(135):90–100

Yuille MJ (2009) Deterioration of friction ridge impressions on a metallic substrate after submergence in lake water. Identif Canada 32(2):48–62

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Acknowledgements

NK & AB greatfully acknowledge the Director, Government Institute of Forensic Science, Nagpur and Department of Higher Education, Government of Maharashtra for their constant encouragement and support.

No funding was received.

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Department of Forensic Science, Government Institute of Forensic Science, R.T. Road, Civil Lines, Nagpur, Maharashtra, 440001, India

Neeti Kapoor & Ashish Badiye

Government Institute of Forensic Science, Nagpur, Maharashtra, India

Shagufa Ahmed

Biological and Life Sciences, School of Arts and Sciences, Ahmedabad University, Central Campus, Ahmedabad, Gujarat, India

Ritesh K. Shukla

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Contributions

NK and AB conceptualized and designed the study. SA conducted the experimentation. AB and NK interpreted the results. RS and AB were the major contributors in writing the manuscript. All authors read and approved the final manuscript.

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Correspondence to Ashish Badiye .

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Additional file 1:.

Development of submerged & successive prints on various substrates using Robin powder blue (RPB) and silver magnetic dual powder (SMDP). (DOCX 1440 kb)

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Kapoor, N., Ahmed, S., Shukla, R.K. et al. Development of submerged and successive latent fingerprints: a comparative study. Egypt J Forensic Sci 9 , 44 (2019). https://doi.org/10.1186/s41935-019-0147-1

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Published : 26 July 2019

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Science News Explores

Experiment: are fingerprint patterns inherited.

Investigate whether fingerprint patterns are random or influenced by genetics

Fingerprints differ from person to person. This science activity can help you determine whether the patterns are random or inherited.

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• Google Classroom

By Science Buddies

April 17, 2023 at 6:30 am

Objective : Collect, categorize and compare the fingerprints of siblings versus unrelated pairs of individuals to determine if fingerprint patterns are inherited.

Areas of science : Genetics & Genomics

Difficulty : Hard intermediate

Time required : 2–5 days

Prerequisites :

• Basic understanding of genetic inheritance
• Consent forms must be signed for each person participating in this experiment. You should inform people that although fingerprints can be used as forms of identification, you will assign their fingerprints a code and not use their name so that the fingerprints remain anonymous. For children under the age of 18, parents must grant consent.

Material availability : Readily available

Cost : Very low (under $20)

Safety : No issues

Credits : Sandra Slutz, PhD, Science Buddies; edited by Sabine De Brabandere, PhD, Science Buddies

During weeks 10 through 24 of  gestation  (when a fetus is developing inside of its mother’s womb, also called  in utero ), ridges form on the  epidermis , which is the outermost layer of skin, on the fingertips of the fetus. The pattern that these ridges make is known as a fingerprint and looks like the drawing shown in Figure 1 below.

Fingerprints are static and do not change with age, so an individual will have the same fingerprint from infancy to adulthood. The pattern changes size, but not shape, as the person grows. (To get a better idea of how that works, you can model the change in size by inking your fingerprint onto a balloon and then blowing up the balloon.) Since each person has unique fingerprints that do not change over time, they can be used for identification. For example, police use fingerprints to determine whether a particular individual has been at a crime scene. Although the exact number, shape and spacing of the ridges changes from person to person, fingerprints can be sorted into three general categories based on their pattern type: loop, arch and whorl, as shown in Figure 2, below.

The  DNA  that a person  inherits  from their parents determines many personal characteristics and traits, like whether someone is right- or left-handed or the color of their eyes. In this science project, you will examine fingerprints from siblings versus pairs of unrelated individuals to figure out if general  fingerprint  patterns are  genetic  or random. Have you ever looked at two girls and said, “You must be sisters”? We can often tell that two people are siblings because they appear to have several similar physical traits. This is because children receive half their DNA from each parent. All  biological siblings  are the mixture of both parents’ DNA. This results in a greater degree of matching traits between siblings than between unrelated individuals. Therefore, if DNA determines fingerprint patterns, then siblings are more likely to share the same fingerprint category than two unrelated individuals are.

Terms and concepts

• Gestation 
• Fingerprint patterns
• Biological siblings
• Fingerprint formation
• Inheritance
• What does it mean to be biologically related?
• What are fingerprints and how are they formed?
• What procedures do officials, like the police, use to record fingerprints?
• What are the different types or classes of fingerprints?

Materials and equipment

• Paper towel
• Moist towelettes for cleaning hands
• White printer paper, tracing paper or parchment paper
• Clear tape 
• Scissors 
• White paper 
• Sibling pairs (at least 15)
• Unrelated pairs of people (at least 15)
• Optional: Magnifying glass
• Lab notebook

Experimental procedure

1. To start this science project, practice taking reliable, clear fingerprints. First try the technique on yourself, then ask a friend or family member to let you learn by using his or her fingerprints.

• To make an ink pad variation, rub a pencil on a piece of printer paper, parchment paper or tracing paper several times until an area of about 3 by 3 centimeters (1.2 by 1.2 inches) is completely grey, as shown in Figure 3 (the paper on the left).
• Use a moist towelette to clean the person’s right index finger.
• Thoroughly dry the finger with a paper towel.
• Press and slide each side of the right index fingertip one time over the pad. 
• Then roll the grey fingertip onto the sticky side of a piece of clear tape. The result will look like the tape in Figure 3.
• Use another towelette to clean the person’s grey finger.
• Cut off the piece of tape containing the fingerprint and stick it onto a piece of white paper, as shown in Figure 3. 
• Perfect your technique until the fingerprints come out clear each time.
• When your prints start to fade, rub your pencil a couple of times over your pad and try again.

2. Make up a consent form for your science project. Because fingerprints can be used to identify people, you will need their consent to take and use their fingerprints. The Science Buddies resource on  Projects Involving Human Subjects  will give you some additional information on getting consent.

3. Collect fingerprints of pairs of siblings  and  of pairs of unrelated people.

• Make sure they sign a consent form  before  you take the fingerprint.
• Use the cleaning and printing system you developed in step 1 to take one fingerprint of each person’s right index finger.
• Label each fingerprint with a unique code, which will tell you which pair the fingerprint belongs to and whether that is a sibling pair or an unrelated pair. An example of an appropriate code would be to assign each pair a number and each individual a letter. Siblings would be labeled as subjects A and B, while unrelated individuals would be labeled as subjects D and Z. Thus, fingerprints from a sibling pair might carry the codes 10A and 10B while fingerprints from an unrelated pair might be labeled 11D and 11Z.
• Collect fingerprints from at least 15 sibling pairs and 15 unrelated pairs. For unrelated pairs, you can actually reuse your sibling data by pairing them up differently. As an example, you could pair sibling 1A with sibling 2B since these individuals are not related to each other. The more pairs you look at in your science project, the stronger your conclusions will be! For a more in-depth look at how the number of participants affects the reliability of your conclusions, see the Science Buddies resource  Sample Size: How Many Survey Participants Do I Need?

4. Examine each fingerprint and characterize it as a whorl, arch or loop pattern. You can use a magnifying glass if you have one. In your lab notebook, make a data table like Table 1, creating a separate row for each person, and fill it out.

In your lab notebook, make a data table like this one and fill it out using the fingerprint pattern data you collected. Be sure to make a separate row for each person.

5. To analyze your data, calculate the percentage of related pairs whose fingerprint patterns match and the percentage of unrelated pairs whose fingerprint patterns match. Advanced students can calculate the margin of error. The Science Buddies resource Sample Size: How Many Survey Participants Do I Need? can help you with this.

6. Make a visual representation of your data. A pie chart or bar graph will work well for this data. Advanced students can indicate the margin of error on their graph. 

7. Compare the percentage of related pairs whose fingerprint patterns match to the percentage of unrelated pairs whose fingerprint patterns match. 

• Are they the same? Is the difference significant taking the margin of error into account? Which one is higher? 
• What does this tell you about whether fingerprint patterns are genetic?
• Identical twins share (nearly) 100 percent of their DNA. Does your data include any identical twins? Do they have the same fingerprint pattern?
• How do your results change if you compare all 10 fingers rather than just one? Do all 10 fingers from the same person have the same fingerprint? 
• Toes also have ridge patterns. Do “toe prints” follow the same rules as fingerprints? 
• Are some patterns more common than others?
• If you make more quantitative measurements of the fingerprint patterns, can they be used to predict sibling pairs? With what degree of accuracy?
• If fingerprints are unique, why do misidentifications occur in forensics? How easy or hard is it to match a fingerprint with an individual?
• Read about statistics and use a mathematical test (like Fisher’s exact test) to determine if your findings are statistically relevant. To do this, you will need to make sure you understand p values and you will need to think about whether your sample size is large enough. Online calculators, like the one from  GraphPad Software , are good resources for this analysis.

This activity is brought to you in partnership with  Science Buddies . Find  the original activity  on the Science Buddies website.

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DNA fingerprinting in forensics: past, present, future

Lutz roewer.

1 Department of Forensic Genetics, Institute of Legal Medicine and Forensic Sciences, Charité - Universitätsmedizin Berlin, Berlin, Germany

DNA fingerprinting, one of the great discoveries of the late 20th century, has revolutionized forensic investigations. This review briefly recapitulates 30 years of progress in forensic DNA analysis which helps to convict criminals, exonerate the wrongly accused, and identify victims of crime, disasters, and war. Current standard methods based on short tandem repeats (STRs) as well as lineage markers (Y chromosome, mitochondrial DNA) are covered and applications are illustrated by casework examples. Benefits and risks of expanding forensic DNA databases are discussed and we ask what the future holds for forensic DNA fingerprinting.

The past - a new method that changed the forensic world

'“I’ve found it! I’ve found it”, he shouted, running towards us with a test-tube in his hand. “I have found a re-agent which is precipitated by hemoglobin, and by nothing else”,’ says Sherlock Holmes to Watson in Arthur Conan Doyle’s first novel A study in Scarlet from1886 and later: 'Now we have the Sherlock Holmes’ test, and there will no longer be any difficulty […]. Had this test been invented, there are hundreds of men now walking the earth who would long ago have paid the penalty of their crimes’ [ 1 ].

The Eureka shout shook England again and was heard around the world when roughly 100 years later Alec Jeffreys at the University of Leicester, in UK, found extraordinarily variable and heritable patterns from repetitive DNA analyzed with multi-locus probes. Not being Holmes he refrained to call the method after himself but 'DNA fingerprinting’ [ 2 ]. Under this name his invention opened up a new area of science. The technique proved applicable in many biological disciplines, namely in diversity and conservation studies among species, and in clinical and anthropological studies. But the true political and social dimension of genetic fingerprinting became apparent far beyond academic circles when the first applications in civil and criminal cases were published. Forensic genetic fingerprinting can be defined as the comparison of the DNA in a person’s nucleated cells with that identified in biological matter found at the scene of a crime or with the DNA of another person for the purpose of identification or exclusion. The application of these techniques introduces new factual evidence to criminal investigations and court cases. However, the first case (March 1985) was not strictly a forensic case but one of immigration [ 3 ]. The first application of DNA fingerprinting saved a young boy from deportation and the method thus captured the public’s sympathy. In Alec Jeffreys’ words: 'If our first case had been forensic I believe it would have been challenged and the process may well have been damaged in the courts’ [ 4 ]. The forensic implications of genetic fingerprinting were nevertheless obvious, and improvements of the laboratory process led already in 1987 to the very first application in a forensic case. Two teenage girls had been raped and murdered on different occasions in nearby English villages, one in 1983, and the other in 1986. Semen was obtained from each of the two crime scenes. The case was spectacular because it surprisingly excluded a suspected man, Richard Buckland, and matched another man, Colin Pitchfork, who attempted to evade the DNA dragnet by persuading a friend to give a sample on his behalf. Pitchfork confessed to committing the crimes after he was confronted with the evidence that his DNA profile matched the trace DNA from the two crime scenes. For 2 years the Lister Institute of Leicester where Jeffreys was employed was the only laboratory in the world doing this work. But it was around 1987 when companies such as Cellmark, the academic medico-legal institutions around the world, the national police, law enforcement agencies, and so on started to evaluate, improve upon, and employ the new tool. The years after the discovery of DNA fingerprinting were characterized by a mood of cooperation and interdisciplinary research. None of the many young researchers who has been there will ever forget the DNA fingerprint congresses which were held on five continents, in Bern (1990), in Belo Horizonte (1992), in Hyderabad (1994), in Melbourne (1996), and in Pt. Elizabeth (1999), and then shut down with the good feeling that the job was done. Everyone read the Fingerprint News distributed for free by the University of Cambridge since 1989 (Figure  1 ). This affectionate little periodical published non-stylish short articles directly from the bench without impact factors and resumed networking activities in the different fields of applications. The period in the 1990s was the golden research age of DNA fingerprinting succeeded by two decades of engineering, implementation, and high-throughput application. From the Foreword of Alec Jeffreys in Fingerprint News , Issue 1, January 1989: 'Dear Colleagues, […] I hope that Fingerprint News will cover all aspects of hypervariable DNA and its application, including both multi-locus and single-locus systems, new methods for studying DNA polymorphisms, the population genetics of variable loci and the statistical analysis of fingerprint data, as well as providing useful technical tips for getting good DNA profiles […]. May your bands be variable’ [ 5 ].

Cover of one of the first issues of Fingerprint News from 1990.

Jeffreys’ original technology, now obsolete for forensic use, underwent important developments in terms of the basic methodology, that is, from Southern blot to PCR, from radioactive to fluorescent labels, from slab gels to capillary electrophoresis. As the technique became more sensitive, the handling simple and automated and the statistical treatment straightforward, DNA profiling, as the method was renamed, entered the forensic routine laboratories around the world in storm. But, what counts in the Pitchfork case and what still counts today is the process to get DNA identification results accepted in legal proceedings. Spectacular fallacies, from the historical 1989 case of People vs. Castro in New York [ 6 ] to the case against Knox and Sollecito in Italy (2007–2013) where literally DNA fingerprinting was on trial [ 7 ], disclosed severe insufficiencies in the technical protocols and especially in the DNA evidence interpretation and raised nolens volens doubts on the scientific and evidentiary value of forensic DNA fingerprinting. These cases are rare but frequent enough to remind each new generation of forensic analysts, researchers, or private sector employees that DNA evidence is nowadays an important part of factual evidence and needs thus intense scrutiny for all parts of the DNA analysis and interpretation process.

In the following I will briefly describe the development of DNA fingerprinting to a standardized investigative method for court use which has since 1984 led to the conviction of thousands of criminals and to the exoneration of many wrongfully suspected or convicted individuals [ 8 ]. Genetic fingerprinting per se could of course not reduce the criminal rate in any of the many countries in the world, which employ this method. But DNA profiling adds hard scientific value to the evidence and strengthens thus (principally) the credibility of the legal system.

The technological evolution of forensic DNA profiling

In the classical DNA fingerprinting method radio-labeled DNA probes containing minisatellite [ 9 ] or oligonucleotide sequences [ 10 ] are hybridized to DNA that has been digested with a restriction enzyme, separated by agarose electrophoresis and immobilized on a membrane by Southern blotting or - in the case of the oligonucleotide probes - immobilized directly in the dried gel. The radio-labeled probe hybridizes to a set of minisatellites or oligonucleotide stretches in genomic DNA contained in restriction fragments whose size differ because of variation in the numbers of repeat units. After washing away excess probe the exposure to X-ray film (autoradiography) allows these variable fragments to be visualized, and their profiles compared between individuals. Minisatellite probes, called 33.6 and 33.15, were most widely used in the UK, most parts of Europe and the USA, whereas pentameric (CAC)/(GTG) 5 probes were predominantly applied in Germany. These so-called multilocus probes (MLP) detect sets of 15 to 20 variable fragments per individual ranging from 3.5 to 20 kb in size (Figure  2 ). But the multi-locus profiling method had several limitations despite its successful application to crime and kinship cases until the middle of the 1990s. Running conditions or DNA quality issues render the exact matching between bands often difficult. To overcome this, forensic laboratories adhered to binning approaches [ 11 ], where fixed or floating bins were defined relative to the observed DNA fragment size, and adjusted to the resolving power of the detection system. Second, fragment association within one DNA fingerprint profile is not known, leading to statistical errors due to possible linkage between loci. Third, for obtaining optimal profiles the method required substantial amounts of high molecular weight DNA [ 12 ] and thus excludes the majority of crime-scene samples from the analysis. To overcome some of these limitations, single-locus profiling was developed [ 13 ]. Here a single hypervariable locus is detected by a specific single-locus probe (SLP) using high stringency hybridization. Typically, four SLPs were used in a reprobing approach, yielding eight alleles of four independent loci per individual. This method requires only 10 ng of genomic DNA [ 14 ] and has been validated through extensive experiments and forensic casework, and for many years provided a robust and valuable system for individual identification. Nevertheless, all these different restriction fragment length polymorphism (RFLP)-based methods were still limited by the available quality and quantity of the DNA and also hampered by difficulties to reliably compare genetic profiles from different sources, labs, and techniques. What was needed was a DNA code, which could ideally be generated even from a single nucleated cell and from highly degraded DNA, a code, which could be rapidly generated, numerically encrypted, automatically compared, and easily supported in court. Indeed, starting in the early 1990s DNA fingerprinting methods based on RFLP analysis were gradually supplanted by methods based on PCR because of the improved sensitivity, speed, and genotyping precision [ 15 ]. Microsatellites, in the forensic community usually referred to short tandem repeats (STRs), were found to be ideally suited for forensic applications. STR typing is more sensitive than single-locus RFLP methods, less prone to allelic dropout than VNTR (variable number of tandem repeat) systems [ 16 ], and more discriminating than other PCR-based typing methods, such as HLA-DQA1 [ 17 ]. More than 2,000 publications now detail the technology, hundreds of different population groups have been studied, new technologies as, for example, the miniSTRs [ 18 ] have been developed and standard protocols have been validated in laboratories worldwide (for an overview see [ 19 ]). Forensic DNA profiling is currently performed using a panel of multi-allelic STR markers which are structurally analogous to the original minisatellites but with much shorter repeat tracts and thus easier to amplify and multiplex with PCR. Up to 30 STRs can be detected in a single capillary electrophoresis injection generating for each individual a unique genetic code. Basically there are two sets of STR markers complying with the standards requested by criminal databases around the world: the European standard set of 12 STR markers [ 20 ] and the US CODIS standard of 13 markers [ 21 ]. Due to partial overlap, they form together a standard of 18 STR markers in total. The incorporation of these STR markers into commercial kits has improved the application of these markers for all kinds of DNA evidence with reproducible results from as less than three nucleated cells [ 22 ] and extracted even from severely compromised material. The probability that two individuals will have identical markers at each of 13 different STR loci within their DNA exceeds one out of a billion. If a DNA match occurs between an accused individual and a crime scene stain, the correct courtroom expression would be that the probability of a match if the crime-scene sample came from someone other than the suspect (considering the random, not closely-related man) is at most one in a billion [ 14 ]. The uniqueness of each person’s DNA (with the exception of monozygotic twins) and its simple numerical codification led to the establishment of government-controlled criminal investigation DNA databases in the developed nations around the world, the first in 1995 in the UK [ 23 ]. When a match is made from such a DNA database to link a crime scene sample to an offender who has provided a DNA sample to a database that link is often referred to as a cold hit. A cold hit is of value as an investigative lead for the police agency to a specific suspect. China (approximately 16 million profiles, the United States (approximately 10 million profiles), and the UK (approximately 6 million profiles) maintain the largest DNA database in the world. The percentage of databased persons is on the increase in all countries with a national DNA database, but the proportions are not the same by the far: whereas in the UK about 10% of the population is in the national DNA database, the percentage in Germany and the Netherlands is only about 0.9% and 0.8%, respectively [ 24 ].

Multilocus DNA Fingerprint from a large family probed with the oligonucleotide (GTG) 5 ( Courtesy of Peter Nürnberg, Cologne Center for Genomics, Germany ).

Lineage markers in forensic analysis

Lineage markers have special applications in forensic genetics. Y chromosome analysis is very helpful in cases where there is an excess of DNA from a female victim and only a low proportion from a male perpetrator. Typical examples include sexual assault without ejaculation, sexual assault by a vasectomized male, male DNA under the fingernails of a victim, male 'touch’ DNA on the skin, and the clothing or belongings of a female victim. Mitochondrial DNA (mtDNA) is of importance for the analyses of low level nuclear DNA samples, namely from unidentified (typically skeletonized) remains, hair shafts without roots, or very old specimens where only heavily degraded DNA is available [ 25 ]. The unusual non-recombinant mode of inheritance of Y and mtDNA weakens the statistical weight of a match between individual samples but makes the method efficient for the reconstruction of the paternal or maternal relationship, for example in mass disaster investigations [ 26 ] or in historical reconstructions. A classic case is the identification of two missing children of the Romanov family, the last Russian monarchy. MtDNA analysis combined with additional DNA testing of material from the mass grave near Yekaterinburg gave virtually irrefutable evidence that the two individuals recovered from a second grave nearby are the two missing children of the Romanov family: the Tsarevich Alexei and one of his sisters [ 27 ]. Interestingly, a point heteroplasmy, that is, the presence of two slightly different mtDNA haplotypes within an individual, was found in the mtDNA of the Tsar and his relatives, which was in 1991 a contentious finding (Figure  3 ). In the early 1990s when the bones were first analyzed, a point heteroplasmy was believed to be an extremely rare phenomenon and was not readily explainable. Today, the existence of heteroplasmy is understood to be relatively common and large population databases can be searched for its frequency at certain positions. The mtDNA evidence in the Romanov case was underpinned by Y-STR analysis where a 17-locus haplotype from the remains of Tsar Nicholas II matched exactly to the femur of the putative Tsarevich and also to a living Romanov relative. Other studies demonstrated that very distant family branches can be traced back to common ancestors who lived hundreds of years ago [ 28 ]. Currently forensic Y chromosome typing has gained wide acceptance with the introduction of highly sensitive panels of up to 27 STRs including rapidly mutating markers [ 29 ]. Figure  4 demonstrates the impressive gain of the discriminative power with increasing numbers of Y-STRs. The determination of the match probability between Y-STR or mtDNA profiles via the mostly applied counting method [ 30 ] requires large, representative, and quality-assessed databases of haplotypes sampled in appropriate reference populations, because the multiplication of individual allele frequencies is not valid as for independently inherited autosomal STRs [ 31 ]. Other estimators for the haplotype match probability than the count estimator have been proposed and evaluated using empirical data [ 32 ], however, the biostatistical interpretation remains complicated and controversial and research continues. The largest forensic Y chromosome haplotype database is the YHRD ( http://www.yhrd.org ) hosted at the Institute of Legal Medicine and Forensic Sciences in Berlin, Germany, with about 115,000 haplotypes sampled in 850 populations [ 33 ]. The largest forensic mtDNA database is EMPOP ( http://www.empop.org ) hosted at the Institute of Legal Medicine in Innsbruck, Austria, with about 33,000 haplotypes sampled in 63 countries [ 34 ]. More than 235 institutes have actually submitted data to the YHRD and 105 to EMPOP, a compelling demonstration of the level of networking activities between forensic science institutes around the world. That additional intelligence information is potentially derivable from such large datasets becomes obvious when a target DNA profile is searched against a collection of geographically annotated Y chromosomal or mtDNA profiles. Because linearly inherited markers have a highly non-random geographical distribution the target profile shares characteristic variants with geographical neighbors due to common ancestry [ 35 ]. This link between genetics, genealogy, and geography could provide investigative leads for investigators in non-suspect cases as illustrated in the following case [ 36 ]:

Screenshot of the 16169 C/T heteroplasmy present in Tsar Nicholas II using both forward and reverse sequencing primers ( Courtesy of Michael Coble, National Institute of Standards and Technology, Gaithersburg, USA ).

Correlation between the number of analyzed Y-STRs and the number of different haplotypes detected in a global population sample of 18,863 23-locus haplotypes.

In 2002, a woman was found with a smashed skull and covered in blood but still alive in her Berlin apartment. Her life was saved by intensive medical care. Later she told the police that she had let a man into her apartment, and he had immediately attacked her. The man was subletting the apartment next door. The evidence collected at the scene and in the neighboring apartment included a baseball cap, two towels, and a glass. The evidence was sent to the state police laboratory in Berlin, Germany and was analyzed with conventional autosomal STR profiling. Stains on the baseball cap and on one towel revealed a pattern consistent with that of the tenant, whereas two different male DNA profiles were found on a second bath towel and on the glass. The tenant was eliminated as a suspect because he was absent at the time of the offense, but two unknown men (different in autosomal but identical in Y-STRs) who shared the apartment were suspected. Unfortunately, the apartment had been used by many individuals of both European and African nationalities, so the initial search for the two men became very difficult. The police obtained a court order for Y-STR haplotyping to gain information about the unknown men’s population affiliation. Prerequisites for such biogeographic analyses are large reference databases containing Y-STR haplotypes also typed for ancestry informative single nucleotide markers (SNP) markers from hundreds of different populations. The YHRD proved useful to infer the population origin of the unknown man. The database inquiry indicated a patrilineage of Southern European ancestry, whereas an African descent was unlikely (Figure  5 ). The police were able to track down the tenant in Italy, and with his help, establish the identity of one of the unknown men, who was also Italian. When questioning this man, the police used the information retrieved from Y-STR profiling that he had shared the apartment in Berlin with a paternal relative. This relative was identified as his nephew. Because of the close-knit relationship within the family, this information would probably not have been easily retrieved from the uncle without the prior knowledge. The nephew was suspected of the attempted murder in Berlin. He was later arrested in Italy, where he had committed another violent robbery.

Screenshot from the YHRD depicting the radiation of a 9-locus haplotype belonging to haplogroup J in Southern Europe.

Information on the biogeographic origin of an unknown DNA could also be retrieved from a number of ancestry informative SNPs (AISNPs) on autosomes or insertion/deletion polymorphisms [ 37 , 38 ] but perhaps even better from so-called mini-haplotypes with only <10 SNPs spanning small molecular intervals (<10 kb) with very low recombination among sites [ 39 ]. Each 'minihap’ behaves like a locus with multiple haplotype lineages (alleles) that have evolved from the ancestral human haplotype. All copies of each distinct haplotype are essentially identical by descent. Thus, they fall like Y and mtDNA into the lineage-informative category of genetic markers and are thus useful for connecting an individual to a family or ancestral genetic pool.

Benefits and risks of forensic DNA databases

The steady growth in the size of forensic DNA databases raises issues on the criteria of inclusion and retention and doubts on the efficiency, commensurability, and infringement of privacy of such large personal data collections. In contrast to the past, not only serious but all crimes are subject to DNA analysis generating millions and millions of DNA profiles, many of which are stored and continuously searched in national DNA databases. And as always when big datasets are gathered new mining procedures based on correlation became feasible. For example, 'Familial DNA Database Searching’ is based on near matches between a crime stain and a databased person, which could be a near relative of the true perpetrator [ 40 ]. Again the first successful familial search was conducted in UK in 2004 and led to the conviction of Craig Harman of manslaughter. Craig Harman was convicted because of partial matches from Harman’s brother. The strategy was subsequently applied in some US states but is not conducted at the national level. It was during a dragnet that it first became public knowledge that the German police were also already involved in familial search strategies. In a little town in Northern Germany the police arrested a young man accused of rape because they had analyzed the DNA of his two brothers who had participated in the dragnet. Because of partial matches between crime scene DNA profiles and these brothers they had identified the suspect. In contrast to other countries, the Federal Constitutional Court of Germany decided in December 2012 against the future court use of this kind of evidence.

Civil rights and liberties are crucial for democratic societies and plans to extend forensic DNA databases to whole populations need to be condemned. Alec Jeffreys early on has questioned the way UK police collects DNA profiles, holding not only convicted individuals but also arrestees without conviction, suspects cleared in an investigation, or even innocent people never charged with an offence [ 41 ]. He also criticized that large national databases as the NDNAD of England and Wales are likely skewed socioeconomically. It has been pointed out that most of the matches refer to minor offences; according to GeneWatch in Germany 63% of the database matches provided are related to theft while <3% related to rape and murder. The changes to the UK database came in the 2012’s Protection of Freedoms bill, following a major defeat at the European Court of Human Rights in 2008. As of May 2013 1.1 million profiles (of about 7 million) had been destroyed to remove innocent people’s profiles from the database. In 2005 the incoming government of Portugal proposed a DNA database containing samples from every Portuguese citizen. Following public objections, the government limited the database to criminals. A recent study on the public views on DNA database-related matters showed that a more critical attitude towards wider national databases is correlated with the age and education of the respondents [ 42 ]. A deeper public awareness on the benefits and risks of very large DNA collections need to be built and common ethical and privacy standards for the development and governance of DNA databases need to be adopted where the citizen’s perspectives are taken into consideration.

The future of forensic DNA analysis

The forensic community, as it always has, is facing the question in which direction the DNA Fingerprint technology will be developed. A growing number of colleagues are convinced that DNA sequencing will soon replace methods based on fragment length analysis and there are good arguments for this position. With the emergence of current Next Generation Sequencing (NGS) technologies, the body of forensically useful data can potentially be expanded and analyzed quickly and cost-efficiently. Given the enormous number of potentially informative DNA loci - which of those should be sequenced? In my opinion there are four types of polymorphisms which deserve a place on the analytic device: an array of 20–30 autosomal STRs which complies with the standard sets used in the national and international databases around the world, a highly discriminating set of Y chromosomal markers, individual and signature polymorphisms in the control and coding region of the mitochondrial genome [ 43 ], as well as ancestry and phenotype inference SNPs [ 44 ]. Indeed, a promising NGS approach with the simultaneous analysis of 10 STRs, 386 autosomal ancestry and phenotype informative SNPs, and the complete mtDNA genome has been presented recently [ 45 ] (Figure  6 ). Currently, the rather high error rates are preventing NGS technologies from being used in forensic routine [ 46 ], but it is foreseeable that the technology will be improved in terms of accuracy and reliability. Time is another essential factor in police investigations which will be considerably reduced in future applications of DNA profiling. Commercial instruments capable of producing a database-compatible DNA profile within 2 hours exist [ 47 ] and are currently under validation for law enforcement use. The hands-free 'swab in - profile out’ process consists of automated extraction, amplification, separation, detection, and allele calling without human intervention. In the US the promise of on-site DNA analysis has already altered the way in which DNA could be collected in future. In a recent decision the Supreme court of the United States held that 'when officers make an arrest supported by probable cause to hold for a serious offense and bring the suspect to the station to be detained in custody, taking and analyzing a cheek swab of the arrestee’s DNA is, like fingerprinting and photographing, a legitimate police booking procedure’ (Maryland v. Alonzo Jay King, Jr.). In other words, DNA can be taken from any arrestee, rightly or wrongly arrested, as a part of the normal booking procedure. Twenty-eight states and the federal government now take DNA swabs after arrests with the aim of comparing profiles to the CODIS database, creating links to unsolved cases and to identify the person (Associated Press, 3 June 2013). Driven by the rapid technological progress DNA actually becomes another metric of quick identification. It remains to be seen whether rapid DNA technologies will alter the way in which DNA is collected by police in other countries. In Germany for example the DNA collection is still regulated by the code of the criminal procedure and the use of DNA profiling for identification purposes only is excluded. Because national legislations are basically so different, a worldwide system to interrogate DNA profiles from criminal justice databases seems currently a very distant project.

Schematic overview of Haloplex targeting and NGS analysis of a large number of markers simultaneously. Sequence data are shown for samples from two individuals and the D3S1358 STR marker, the rs1335873 SNP marker, and a part of the HVII region of mtDNA ( Courtesy of Marie Allen, Uppsala University, Sweden ).

At present the forensic DNA technology directly affects the lives of millions people worldwide. The general acceptance of this technique is still high, reports on the DNA identification of victims of the 9/11 terrorist attacks [ 48 ], of natural disasters as the Hurricane Katrina [ 49 ], and of recent wars (for example, in former Yugoslavia [ 50 ]) and dictatorship (for example, in Argentina [ 51 ]) impress the public in the same way as police investigators in white suits securing DNA evidence at a broken door. CSI watchers know, and even professionals believe, that DNA will inevitably solve the case just following the motto Do Not Ask, it’s DNA, stupid! But the affirmative view changes and critical questions are raised. It should not be assumed that the benefits of forensic DNA fingerprinting will necessarily override the social and ethical costs [ 52 ].

This short article leaves many of such questions unanswered. Alfred Nobel used his fortune to institute a prize for work 'in ideal direction’. What would be the ideal direction in which DNA fingerprinting, one of the great discoveries in recent history, should be developed?

Competing interests

The author declares that he has no competing interests.

• Doyle AC. A study in scarlet, Beeton’s Christmas Annual. London, New York and Melbourne: Ward, Lock & Co; 1887. [ Google Scholar ]
• Jeffreys AJ, Wilson V, Thein SL. Individual-specific “fingerprints” of Human DNA. Nature. 1985; 314 :67–74. doi: 10.1038/314067a0. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Jeffreys AJ, Brookfield JF, Semeonoff R. Positive identification of an immigration test-case using human DNA fingerprints. Nature. 1985; 317 :818–819. doi: 10.1038/317818a0. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• University of Leicester Bulletin Supplement August/September 2004.
• Jeffreys AJ. Foreword. Fingerprint News. 1989; 1 :1. [ Google Scholar ]
• Lander ES. DNA fingerprinting on trial. Nature. 1989; 339 :501–505. doi: 10.1038/339501a0. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Balding DJ. Evaluation of mixed-source, low-template DNA profiles in forensic science. Proc Natl Acad Sci U S A. 2013; 110 :12241–12246. doi: 10.1073/pnas.1219739110. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• The innocence project. [ http://www.innocenceproject.org ]
• Jeffreys AJ, Wilson V, Thein SL. Hypervariable 'minisatellite’ regions in human DNA. Nature. 1985; 314 :67–73. doi: 10.1038/314067a0. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Schäfer R, Zischler H, Birsner U, Becker A, Epplen JT. Optimized oligonucleotide probes for DNA fingerprinting. Electrophoresis. 1988; 9 :369–374. doi: 10.1002/elps.1150090804. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Budowle B, Giusti AM, Waye JS, Baechtel FS, Fourney RM, Adams DE, Presley LA, Deadman HA, Monson KL. Fixed-bin analysis for statistical evaluation of continuous distributions of allelic data from VNTR loci, for use in forensic comparisons. Am J Hum Genet. 1991; 48 :841–855. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Roewer L, Nürnberg P, Fuhrmann E, Rose M, Prokop O, Epplen JT. Stain analysis using oligonucleotide probes specific for simple repetitive DNA sequences. Forensic Sci Int. 1990; 47 :59–70. doi: 10.1016/0379-0738(90)90285-7. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Wong Z, Wilson V, Patel I, Povey S, Jeffreys AJ. Characterization of a panel of highly variable minisatellites cloned from human DNA. Ann Hum Genet. 1987; 51 :269–288. doi: 10.1111/j.1469-1809.1987.tb01062.x. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Jobling MA, Hurles ME, Tyler-Smith C. Human Evolutionary Genetics. Abingdon: Garland Science; 2003. Chapter 15: Identity and identification; pp. 474–497. [ Google Scholar ]
• Edwards A, Civitello A, Hammond HA, Caskey CT. DNA typing and genetic mapping with trimeric and tetrameric tandem repeats. Am J Hum Genet. 1991; 49 :746–756. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Budowle B, Chakraborty R, Giusti AM, Eisenberg AJ, Allen RC. Analysis of the VNTR locus D1S80 by the PCR followed by high-resolution PAGE. Am J Hum Genet. 1991; 48 :137–144. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Saiki RK, Bugawan TL, Horn GT, Mullis KB, Erlich HA. Analysis of enzymatically amplified beta-globin and HLA-DQ alpha DNA with allele-specific oligonucleotide probes. Nature. 1986; 324 :163–166. doi: 10.1038/324163a0. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Coble MD, Butler JM. Characterization of new miniSTR loci to aid analysis of degraded DNA. J Forensic Sci. 2005; 50 :43–53. [ PubMed ] [ Google Scholar ]
• Butler JM. Forensic DNA Typing: Biology, Technology, and Genetics of STR Markers. 2. New York: Elsevier Academic Press; 2005. [ Google Scholar ]
• Gill P, Fereday L, Morling N, Schneider PM. The evolution of DNA databases - Recommendations for new European STR loci. Forensic Sci Int. 2006; 156 :242–244. doi: 10.1016/j.forsciint.2005.05.036. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Budowle B, Moretti TR, Niezgoda SJ, Brown BL. CODIS and PCR-based short tandem repeat loci: law enforcement tools. Madison, WI: Promega Corporation; 1998. pp. 73–88. (Proceedings of the Second European Symposium on Human Identification). [ Google Scholar ]
• Nagy M, Otremba P, Krüger C, Bergner-Greiner S, Anders P, Henske B, Prinz M, Roewer L. Optimization and validation of a fully automated silica-coated magnetic beads purification technology in forensics. Forensic Sci Int. 2005; 152 :13–22. doi: 10.1016/j.forsciint.2005.02.027. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Martin PD, Schmitter H, Schneider PM. A brief history of the formation of DNA databases in forensic science within Europe. Forensic Sci Int. 2001; 119 :225–231. doi: 10.1016/S0379-0738(00)00436-9. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• ENFSI survey on DNA Databases in Europe. December 2011, published 2012-08-18. [ http://www.enfsi.eu ]
• Roewer L, Parson W. In: Encyclopedia of Forensic Sciences. 2. Siegel JA, Saukko PJ, editor. Amsterdam: Elsevier B.V; 2013. Internet accessible population databases: YHRD and EMPOP. [ Google Scholar ]
• Calacal GC, Delfin FC, Tan MM, Roewer L, Magtanong DL, Lara MC, Rd F, De Ungria MC. Identification of exhumed remains of fire tragedy victims using conventional methods and autosomal/Y-chromosomal short tandem repeat DNA profiling. Am J Forensic Med Pathol. 2005; 26 :285–291. doi: 10.1097/01.paf.0000177338.21951.82. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Coble MD, Loreille OM, Wadhams MJ, Edson SM, Maynard K, Meyer CE, Niederstätter H, Berger C, Berger B, Falsetti AB, Gill P, Parson W, Finelli LN. Mystery solved: the identification of the two missing Romanov children using DNA analysis. PLoS One. 2009; 4 :e4838. doi: 10.1371/journal.pone.0004838. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Haas C, Shved N, Rühli FJ, Papageorgopoulou C, Purps J, Geppert M, Willuweit S, Roewer L, Krawczak M. Y-chromosomal analysis identifies the skeletal remains of Swiss national hero Jörg Jenatsch (1596–1639) Forensic Sci Int Genet. 2013; 7 :610–617. doi: 10.1016/j.fsigen.2013.08.006. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Ballantyne KN, Keerl V, Wollstein A, Choi Y, Zuniga SB, Ralf A, Vermeulen M, de Knijff P, Kayser M. A new future of forensic Y-chromosome analysis: rapidly mutating Y-STRs for differentiating male relatives and paternal lineages. Forensic Sci Int Genet. 2012; 6 :208–218. doi: 10.1016/j.fsigen.2011.04.017. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Budowle B, Sinha SK, Lee HS, Chakraborty R. Utility of Y-chromosome short tandem repeat haplotypes in forensic applications. Forensic Sci Rev. 2003; 15 :153–164. [ PubMed ] [ Google Scholar ]
• Roewer L, Kayser M, de Knijff P, Anslinger K, Betz A, Caglià A, Corach D, Füredi S, Henke L, Hidding M, Kärgel HJ, Lessig R, Nagy M, Pascali VL, Parson W, Rolf B, Schmitt C, Szibor R, Teifel-Greding J, Krawczak M. A new method for the evaluation of matches in non-recombining genomes: application to Y-chromosomal short tandem repeat (STR) haplotypes in European males. Forensic Sci Int. 2000; 114 :31–43. doi: 10.1016/S0379-0738(00)00287-5. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Andersen MM, Caliebe A, Jochens A, Willuweit S, Krawczak M. Estimating trace-suspect match probabilities for singleton Y-STR haplotypes using coalescent theory. Forensic Sci Int Genet. 2013; 7 :264–271. doi: 10.1016/j.fsigen.2012.11.004. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Willuweit S, Roewer L. International Forensic Y Chromosome User Group. Y chromosome haplotype reference database (YHRD): update. Forensic Sci Int Genet. 2007; 1 :83–87. doi: 10.1016/j.fsigen.2007.01.017. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Parson W, Dür A. EMPOP - a forensic mtDNA database. Forensic Sci Int Genet. 2007; 1 :88–92. doi: 10.1016/j.fsigen.2007.01.018. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Roewer L, Croucher PJ, Willuweit S, Lu TT, Kayser M, Lessig R, de Knijff P, Jobling MA, Tyler-Smith C, Krawczak M. Signature of recent historical events in the European Y-chromosomal STR haplotype distribution. Hum Genet. 2005; 116 :279–291. doi: 10.1007/s00439-004-1201-z. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Roewer L. Male DNA Fingerprints say more. Profiles in DNA. 2004; 7 :14–15. [ Google Scholar ]
• Phillips C, Fondevila M, Lareu MV. A 34-plex autosomal SNP single base extension assay for ancestry investigations. Methods Mol Biol. 2012; 830 :109–126. doi: 10.1007/978-1-61779-461-2_8. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Pereira R, Phillips C, Pinto N, Santos C, dos Santos SE, Amorim A, Carracedo A, Gusmão L. Straightforward inference of ancestry and admixture proportions through ancestry-informative insertion deletion multiplexing. PLoS One. 2012; 7 :e29684. doi: 10.1371/journal.pone.0029684. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Pakstis AJ, Fang R, Furtado MR, Kidd JR, Kidd KK. Mini-haplotypes as lineage informative SNPs and ancestry inference SNPs. Eur J Hum Genet. 2012; 20 :1148–1154. doi: 10.1038/ejhg.2012.69. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Maguire CN, McCallum LA, Storey C, Whitaker JP. Familial searching: A specialist forensic DNA profiling service utilising the National DNA Database® to identify unknown offenders via their relatives - The UK experience. Forensic Sci Int Genet. 2013; 8 :1–9. [ PubMed ] [ Google Scholar ]
• Jeffreys A. Genetic Fingerprinting. Nat Med. 2005; 11 :1035–1039. doi: 10.1038/nm1005-1035. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Machado H, Silva S. Would you accept having your DNA profile inserted in the National Forensic DNA database? Why? Results of a questionnaire applied in Portugal. Forensic Sci Int Genet. 2013. Epub ahead of print. [ PubMed ]
• Parson W, Strobl C, Strobl C, Huber G, Zimmermann B, Gomes SM, Souto L, Fendt L, Delport R, Langit R, Wootton S, Lagacé R, Irwin J. Evaluation of next generation mtGenome sequencing using the Ion Torrent Personal Genome Machine (PGM) Forensic Sci Int Genet. 2013; 7 :632–639. doi: 10.1016/j.fsigen.2013.09.007. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Budowle B, van Daal A. Forensically relevant SNP classes. Biotechniques. 2008; 44 :603–608. 610. [ PubMed ] [ Google Scholar ]
• Allen M, Nilsson M, Havsjö M, Edwinsson L, Granemo J, Bjerke M. Haloplex and MiSeq NGS for simultaneous analysis of 10 STRs, 386 SNPs and the complete mtDNA genome. Melbourne; 2013. (Presentation at the 25th Congress of the International Society for Forensic Genetics). 2–7 September 2013. [ Google Scholar ]
• Bandelt HJ, Salas A. Current next generation sequencing technology may not meet forensic standards. Forensic Sci Int Genet. 2012; 6 :143–145. doi: 10.1016/j.fsigen.2011.04.004. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Tan E, Turingan RS, Hogan C, Vasantgadkar S, Palombo L, Schumm JW, Selden RF. Fully integrated, fully automated generation of short tandem repeat profiles. Investigative Genet. 2013; 4 :16. doi: 10.1186/2041-2223-4-16. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Biesecker LG, Bailey-Wilson JE, Ballantyne J, Baum H, Bieber FR, Brenner C, Budowle B, Butler JM, Carmody G, Conneally PM, Duceman B, Eisenberg A, Forman L, Kidd KK, Leclair B, Niezgoda S, Parsons TJ, Pugh E, Shaler R, Sherry ST, Sozer A, Walsh A. DNA Identifications after the 9/11 World Trade Center Attack. Science. 2005; 310 :1122–1123. doi: 10.1126/science.1116608. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Dolan SM, Saraiya DS, Donkervoort S, Rogel K, Lieber C, Sozer A. The emerging role of genetics professionals in forensic kinship DNA identification after a mass fatality: lessons learned from Hurricane Katrina volunteers. Genet Med. 2009; 11 :414–417. doi: 10.1097/GIM.0b013e3181a16ccc. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Huffine E, Crews J, Kennedy B, Bomberger K, Zinbo A. Mass identification of persons missing from the break-up of the former Yugoslavia: structure, function, and role of the International Commission on Missing Persons. Croat Med J. 2001; 42 :271–275. [ PubMed ] [ Google Scholar ]
• Corach D, Sala A, Penacino G, Iannucci N, Bernardi P, Doretti M, Fondebrider L, Ginarte A, Inchaurregui A, Somigliana C, Turner S, Hagelberg E. Additional approaches to DNA typing of skeletal remains: the search for “missing” persons killed during the last dictatorship in Argentina. Electrophoresis. 1997; 18 :1608–1612. doi: 10.1002/elps.1150180921. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Levitt M. Forensic databases: benefits and ethical and social costs. Br Med Bull. 2007; 83 :235–248. doi: 10.1093/bmb/ldm026. [ PubMed ] [ CrossRef ] [ Google Scholar ]

The Myth of Fingerprints

Police today increasingly embrace DNA tests as the ultimate crime-fighting tool. They once felt the same way about fingerprinting

Clive Thompson

At 9:00 a.m. last December 14, a man in Orange County, California, discovered he’d been robbed. Someone had swiped his Volkswagen Golf, his MacBook Air and some headphones. The police arrived and did something that is increasingly a part of everyday crime fighting: They swabbed the crime scene for DNA.

Normally, you might think of DNA as the province solely of high-profile crimes—like murder investigations, where a single hair or drop of blood cracks a devilish case. Nope: These days, even local cops are wielding it to solve ho-hum burglaries. The police sent the swabs to the county crime lab and ran them through a beige, photocopier-size “rapid DNA” machine, a relatively inexpensive piece of equipment affordable even by smaller police forces. Within minutes, it produced a match to a local man who’d been previously convicted of identity theft and burglary. They had their suspect.

DNA identification has gone mainstream—from the elite labs of “CSI” to your living room. When it first appeared over 30 years ago, it was an arcane technique. Now it’s woven into the fabric of everyday life: California sheriffs used it to identify the victims of their recent wildfires, and genetic testing firms offer to identify your roots if you mail them a sample.

Yet the DNA revolution has unsettling implications for privacy. After all, you can leave DNA on everything you touch—which means, sure, crimes can be more easily busted, but the government can also more easily track you. And while it’s fun to learn about your genealogy, your cheek samples can wind up in places you’d never imagine. FamilyTreeDNA, a personal genetic service, in January admitted it was sharing DNA data with federal investigators to help them solve crimes. Meanwhile consumer DNA testing firm 23andMe announced that it was now sharing samples sent to them with the pharmaceutical giant GlaxoSmithKline to make “novel treatments and cures.”

What happens to a society when there’s suddenly a new way to identify people—to track them as they move around the world? That’s a question that the denizens of the Victorian turn of the century pondered, as they learned of a new technology to hunt criminals: fingerprinting.

For centuries, scholars had remarked on the curious loops and “whorls” that decorated their fingertips. In 1788, the scientist J.C.A. Mayers declared that patterns seemed unique—that “the arrangement of skin ridges is never duplicated in two persons.”

It was an interesting observation, but one that lay dormant until 19th-century society began to grapple with an emerging problem: How do you prove people are who they say they are?

Carrying government-issued identification was not yet routine, as Colin Beavan, author of Fingerprints , writes. Cities like London were booming, becoming crammed full of strangers—and packed full of crime. The sheer sprawl of the population hindered the ability of police to do their work because unless they recognized criminals by sight, they had few reliable ways of verifying identities. A first-time offender would get a light punishment; a habitual criminal would get a much stiffer jail sentence. But how could the police verify whether a perpetrator they hauled in had ever been caught previously? When recidivists got apprehended, they’d just give out a fake name and claim it was their first crime.

“A lot of that is the function of the increasing anonymity of modern life,” notes Charles Rzepka, a Boston University professor who studies crime fiction. “There’s this problem of what Edgar Allan Poe called ‘The Man of the Crowd.’” It even allowed for devious cons. One man in Europe claimed to be “Roger Tichborne,” a long-lost heir to a family baronetcy, and police had no way to prove he was or wasn’t.

Faced with this problem, police tried various strategies for identification. Photographic mug shots helped, but they were painstakingly slow to search through. In the 1880s, a French police official named Alphonse Bertillon created a system for recording 11 body measurements of a suspect, but it was difficult to do so accurately.

The idea of fingerprints gradually dawned on several different thinkers. One was Henry Faulds, a Scottish physician who was working as a missionary in Japan in the 1870s. One day while sifting through shards of 2,000-year-old pottery, he noticed that the ridge patterns of the potter’s ancient fingerprints were still visible. He began inking prints of his colleagues at the hospital—and noticing they seemed unique. Faulds even used prints to solve a small crime. An employee was stealing alcohol from the hospital and drinking it in a beaker. Faulds located a print left on the glass, matched it to a print he’d taken from a colleague, and—presto—identified the culprit.

How reliable were prints, though? Could a person’s fingerprints change? To find out, Faulds and some students scraped off their fingertip ridges, and discovered they grew back in precisely the same pattern. When he examined children’s development over two years, Faulds found their prints stayed the same. By 1880 he was convinced, and wrote a letter to the journal Nature arguing that prints could be a way for police to deduce identity.

“When bloody finger-marks or impressions on clay, glass, etc., exist,” Faulds wrote, “they may lead to the scientific identification of criminals.”

Other thinkers were endorsing and exploring the idea—and began trying to create a way to categorize prints. Sure, fingerprints were great in theory, but they were truly useful only if you could quickly match them to a suspect.

The breakthrough in matching prints came from Bengal, India. Azizul Haque, the head of identification for the local police department, developed an elegant system that categorized prints into subgroups based on their pattern types such as loops and whorls. It worked so well that a police officer could find a match in only five minutes—much faster than the hour it would take to identify someone using the Bertillon body-measuring system. Soon, Haque and his superior Edward Henry were using prints to identify repeat criminals in Bengal “hand over fist,” as Beavan writes. When Henry demonstrated the system to the British government, officials were so impressed they made him assistant commissioner of Scotland Yard in 1901.

Fingerprinting was now a core tool in crime-busting. Mere months after Henry set up shop, London officers used it to fingerprint a man they’d arrested for pickpocketing. The suspect claimed it was his first offense. But when the police checked his prints, they discovered he was Benjamin Brown, a career criminal from Birmingham, who’d been convicted ten times and printed while in custody. When they confronted him with their analysis, he admitted his true identity. “Bless the finger-prints,” Brown said, as Beavan writes. “I knew they’d do me in!”

Within a few years, prints spread around the world. Fingerprinting promised to inject hard-nosed objectivity into the fuzzy world of policing. Prosecutors historically relied on witness testimony to place a criminal in a location. And testimony is subjective; the jury might not find the witness credible. But fingerprints were an inviolable, immutable truth, as prosecutors and professional “fingerprint examiners” began to proclaim.

“The fingerprint expert has only facts to consider; he reports simply what he finds. The lines of identification are either there or they are absent,” as one print examiner argued in 1919.

This sort of talk appealed to the spirit of the age—one where government authorities were keen to pitch themselves as rigorous and science-based.

“It’s this turn toward thinking that we have to collect detailed data from the natural world—that these tiniest details could be more telling than the big picture,” says Jennifer Mnookin, dean of the UCLA law school and an expert in evidence law. Early 20th-century authorities increasingly believed they could solve complex social problems with pure reason and precision. “It was tied in with these ideas of science and progressivism in government, and having archives and state systems of tracking people,” says Simon Cole, a professor of criminology, law, and society at the University of California, Irvine, and the author of Suspect Identities , a history of fingerprinting.

Prosecutors wrung high drama out of this curious new technique. When Thomas Jennings in 1910 was the first U.S. defendant to face a murder trial that relied on fingerprinted evidence, prosecutors handed out blown-up copies of the prints to the jury. In other trials, they would stage live courtroom demonstrations of print-lifting and print-matching. It was, in essence, the birth of the showily forensic policing that we now see so often on “CSI”-style TV shows: perps brought low by implacably scientific scrutiny. Indeed, criminals themselves were so intimidated by the prospect of being fingerprinted that, in 1907, a suspect arrested by Scotland Yard desperately tried to slice off his own prints while in the paddy wagon.

Yet it also became clear, over time, that fingerprinting wasn’t as rock solid as boosters would suggest. Police experts would often proclaim in court that “no two people have identical prints”—even though this had never been proven, or even carefully studied. (It’s still not proven.)

Although that idea was plausible, “people just asserted it,” Mnookin notes; they were eager to claim the infallibility of science. Yet quite apart from these scientific claims, police fingerprinting was also simply prone to error and sloppy work.

The real problem, Cole notes, is that fingerprinting experts have never agreed on “a way of measuring the rarity of an arrangement of friction ridge features in the human population.” How many points of similarity should two prints have before the expert analyst declares they’re the same? Eight? Ten? Twenty? Depending on what city you were tried in, the standards could vary dramatically. And to make matters more complex, when police lift prints from a crime scene, they are often incomplete and unclear, giving authorities scant material to make a match.

So even as fingerprints were viewed as unmistakable, plenty of people were mistakenly sent to jail. Simon Cole notes that at least 23 people in the United States have been wrongly connected to crime-scene prints.* In North Carolina in 1985, Bruce Basden was arrested for murder and spent 13 months in jail before the print analyst realized he’d made a blunder.

Nonetheless, the reliability of fingerprinting today is rarely questioned in modern courts. One exception was J. Spencer Letts, a federal judge in California who in 1991 became suspicious of fingerprint analysts who’d testified in a bank robbery trial. Letts was astounded to hear that the standard for declaring that two prints matched varied widely from county to county. Letts threw out the fingerprint evidence from that trial.

“I don’t think I’m ever going to use fingerprint testimony again,” he said in court, sounding astonished, as Cole writes. “I’ve had my faith shaken.” But for other judges, the faith still holds.

The world of DNA identification, in comparison, has received a slightly higher level of skepticism. When it was first discovered in 1984, it seemed like a blast of sci-fi precision. Alec Jeffreys, a researcher at the University of Leicester in England, had developed a way to analyze pieces of DNA and produce an image that, Jeffreys said, had a high likelihood of being unique. In a splashy demonstration of his concept, he found that the semen on two murder victims wasn’t from the suspect police had in custody.

DNA quickly gained a reputation for helping free the wrongly accused: Indeed, the nonprofit Innocence Project has used it to free over 360 prisoners by casting doubt on their convictions. By 2005, Science magazine said DNA analysis was the “gold standard” for forensic evidence.

Yet DNA identification, like fingerprinting, can be prone to error when used sloppily in the field. One problem, notes Erin Murphy, professor of criminal law at New York University School of Law, is “mixtures”: If police scoop up genetic material from a crime scene, they’re almost certain to collect not just the DNA of the offender, but stray bits from other people. Sorting relevant from random is a particular challenge for the simple DNA identification tools increasingly wielded by local police. The rapid-typing machines weren’t really designed to cope with the complexity of samples collected in the field, Murphy says—even though that’s precisely how some police are using them.

“There’s going to be one of these in every precinct and maybe in every squad car,” Murphy says, with concern. When investigating a crime scene, local police may not have the training to avoid contaminating their samples. Yet they’re also building up massive databases of local citizens: Some police forces now routinely request a DNA sample from everyone they stop, so they can rule them in or out of future crime investigations.

The courts have already recognized the dangers of badly managed DNA identification. In 1989—only five years after Jeffreys invented the technique—U.S. lawyers successfully contested DNA identification in court, arguing that the lab processing the evidence had irreparably contaminated it. Even the prosecution agreed it had been done poorly. Interestingly, as Mnookin notes, DNA evidence received pushback “much more quickly than fingerprints ever did.”

It even seems the public has grasped the dangers of its being abused and misused. Last November, a jury in Queens, New York, deadlocked in a murder trial—after several of them reportedly began to suspect the accused’s DNA had found its way onto the victim’s body through police contamination. “There is a sophistication now among a lot of jurors that we haven’t seen before,” Lauren-Brooke Eisen, a senior fellow at the Brennan Center for Justice, told the New York Times .

To keep DNA from being abused, we’ll have to behave like good detectives—asking the hard questions, and demanding evidence.

*Editor's Note, April 26, 2019: An earlier version of this story incorrectly noted that at least 23 people in the United States had been imprisoned after being wrongly connected to crime-scene prints. In fact, not all 23 were convicted or imprisoned. This story has been edited to correct that fact. Smithsonian regrets the error.

Body of Evidence

Now science can identify you by your ears, your walk and even your scent Research by Sonya Maynard

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Clive Thompson is author of Smarter Than You Think: How Technology is Changing Our Minds for the Better and Coders: The Making of a New Tribe and the Remaking of the World . He is a contributing writer to the New York Times Magazine and Wired . Photo: Tom Igoe.

You leave a ‘microbe fingerprint’ on every piece of clothing you wear – and it could help forensic scientists solve crimes

Associate Professor of Forensic Science, Murdoch University

Senior Research Fellow, School of Law and Policing, University of Central Lancashire

Associate professor, Università del Piemonte Orientale

Disclosure statement

We acknowledge all the volunteers and students involved in the mentioned studies.

Noemi Procopio receives funding from UKRI via a Future Leaders Fellowship.

Sarah Gino receives funding from University of Eastern Piedmont, FAR2017

University of Central Lancashire provides funding as a member of The Conversation UK.

Murdoch University provides funding as a member of The Conversation AU.

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When you think of a criminal investigation, you might picture detectives meticulously collecting and analysing evidence found at the scene: weapons, biological fluids, footprints and fingerprints. However, this is just the beginning of an attempt to reconstruct the events and individuals involved in the crime.

At the heart of the process lies the “ principle of exchange ” formulated by the French criminologist Edmond Locard in the early 1900s, which states that “every contact leaves a trace”. The transfer of materials between the parties involved in a crime (the victim, the perpetrator, objects, the environment) forms the basis for reconstructing the events.

In Locard’s time, these traces were typically things you could see with a magnifying glass or microscope, such as pollen, sand and fibres. However, such evidence is limited because much of it is not directly associated with a specific individual.

In our latest research , we have shown how the population of bacteria on a person’s skin leaves traces on the clothes they wear – and how these traces last for months and can be used to uniquely identify the wearer.

Microbial traces

Imagine a crime scene where an investigator finds a victim and a piece of clothing that doesn’t belong to them. Pollen or grains of sand might help the investigator find out where it came from, but what about identifying the owner of the clothing?

Skin cells, hairs and biological fluids are good contenders. However, another thing very specific to an individual is the unique community of microorganisms on and within their body.

These microbes are specific to different parts of the body, can persist over long periods of time and can be transferred to other people and to the environment. This makes them useful to address a variety of questions in forensics .

“Forensic microbiology” got its start in the early 2000s, as scientists set out to find ways to defend against bioterrorism . Today forensic microbiology is used to identify individuals after death, understand what their health was like before they died , determine how and why people have died , how long it has been since they died , and where they came from .

In a nutshell, today’s update on Locard’s principle is that “every contact leaves a microbiological trace”.

The ‘touch microbiome’

While this principle has been established, we still want to know more about how much of an individual’s microbiome is transferred to their surroundings. We also need to know how long it persists, and whether certain microbes may be more useful than others for identification.

We also want to understand how microbial traces may be contaminated by other items or the environment, and how different receiving surfaces affect microbial populations.

In 2021, two of the authors (Procopio and Gino) and colleagues at the University of Central Lancashire in the UK and the University of Eastern Piedmont in Italy first described the “ touch microbiome ” – the unique bacterial populations on individuals’ skin. This work also studied how these bacteria could be transferred and persist for up to a month on non-porous surfaces, such as a glass slide, in uncontrolled indoor surroundings.

This team also analysed DNA from samples belonging to dead bodies from old cases, which had been frozen for up to 16 years. They were able to identify specific populations of microbes linked to the manner of death and the decomposition stage of the bodies. This showed the microbial signature can be used to improve our understanding of cold cases when DNA extracts are still available.

Tracing T-shirts

In our most recent work, the third author (Magni) joined the collaboration to improve the potential of individual identification from clothes, items often collected as evidence at the crime scene.

In our study , cotton T-shirts were worn by two individuals for 24 hours in Australia. The T-shirts were then placed in a controlled environment for up to six months, alongside unworn items used as controls. Samples from both worn and unworn T-shirts were taken at various points in time and frozen.

The samples were then shipped (still frozen) to Italy for microbial DNA extraction. Next, sequencing was conducted in the UK, with the goal of identifying the microbial species present in the samples.

Results showed the two volunteers transferred distinct and recognisable microbes onto the clothing, each unique to the respective individual. Additionally, we could distinguish between worn and unworn items even after an extended period of time. The microbiome remained stable on the worn garments for up to 180 days.

We also observed the transfer of specific bacteria from the worn items to the unworn ones stored closest to them, showing the possibility of microbe transfer between items.

Learning more from clothes

Clothes at any crime scene can provide key evidence for the investigation process.

They can aid in profiling individuals by revealing indicators of gender, occupation, income, social status, political, religious or cultural affiliations, and even marital status.

Additionally, they can provide clues regarding the manner of death , the location of the crime, and in certain cases, even support the estimation of the time since death .

Clothes play a crucial role in reconstructing events associated with the crime and establishing the identity of individuals involved.

Our research shows clothing can provide even more evidence. The discovery of unique microbiomes capable of identifying individuals from clothing marks a significant stride forward.

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AI Finds Astonishing Male/Female Differences in Human Brain

A new study shows huge differences, with no overlap between women and men..

Posted May 24, 2024 | Reviewed by Tyler Woods

• A new study from Stanford shows remarkable differences between female and male brains.
• The researchers found no overlap between male and female, and rejected the idea of a "continuum."
• Male-pattern brain connectivity predicted male cognitive function but not female cognitive function.
• Female-pattern brain connectivity predicted female cognitive function but not male cognitive function.

Are male brains really that different from female brains? Does it matter? Why should anybody care?

More than 100 years ago, a German neurologist named Paul Julius Möbius published a book titled Über den physiologischen Schwachsinn des Weibes , “Regarding the physiological weakmindedness of women.” Möbius noted that women's brains are smaller than the brains of men, even after adjusting for differences in height. He concluded that women are “physiologically weak-minded.” In making that claim, Möbius was continuing a tradition in Western thought that we can trace back to Aristotle, namely: women and men are different. That means men are better.

The misogynistic arguments of men from Aristotle to Möbius have made many modern researchers leery of claims regarding female/male differences in the brain. Researchers have long since established that there are, in fact, no differences in average intelligence between men and women. But investigators have consistently found that women are more likely than men to experience anxiety and depression . Conversely, men are more likely than women to have autism , attention-deficit disorder , and schizophrenia . Are these male/female differences merely social constructs? Or might these robust differences in psychopathology reflect, at least in part, some underlying difference in neuroanatomy or neural connectivity ?

A recent review of MRI studies of female and male brains concluded that “MRI-based studies exploring differences between male and female brains revealed mostly inconsistent and inconclusive findings.” Others have argued that while there might be differences, on average, between male and female brains, the differences are on a continuum, with lots of overlap.

But researchers at Stanford recently used artificial-intelligence methods to examine brain activity in roughly 1,500 young adults 20 to 35 years of age. Neuroscientists have known for many years that every human brain is characterized by a “ fingerprint ” of brain activity at rest, unique to that individual. The Stanford neuroscientists used big-data artificial intelligence techniques to determine the fingerprint of every one of those 1,500 young adults and then compared females with males. Did females differ from males? Was there overlap? The results were astonishing.

As you can see, there wasn’t a continuum: the female fingerprints of brain activity were quite different from the male fingerprints of resting brain activity, with no overlap. These findings strongly suggest that what’s going on in a woman’s brain at rest is significantly different from what’s going on in a man’s brain at rest.

Just as remarkably, the Stanford team mapped fMRI patterns of connectivity onto cognitive functions such as intelligence. They found particular patterns of connectivity within male brains that accurately predicted cognitive functions such as intelligence. However, that male model had no predictive power for cognitive functions in women.

Conversely, they found particular patterns of connectivity within female brains that accurately predicted cognitive functions such as intelligence among women. However, that female model had no predictive power for cognitive functions in men.

These findings strongly suggest that the determinants of cognitive functions in male brains are profoundly different from the determinants of cognitive functions in female brains.

I have to admit that I was really surprised by these results. I have been writing about these topics for more than 20 years. In the first edition of my book Why Gender Matters, published by Doubleday in 2005, I devoted a chapter to kids who are psychologically “gender-atypical.” I suggested that these kids are somewhere in between male and female. But the Stanford study provides little support for that claim. I am hopeful that the researchers will do follow-up studies specifically looking at individuals who are gender-nonconforming, gender-atypical, and who have gender dysphoria , to see whether and how those characteristics influence these findings.

The researchers are well aware of the implications of their findings. They know all about the previous studies suggesting small effect sizes, lots of overlap, and a continuum of male/female differences. They conclude that the failure of previous work to demonstrate these huge effects is due to the "weaker algorithms" employed in earlier research. They conclude: "Our results provide the most compelling and generalizable evidence to date, refuting this continuum hypothesis and firmly demonstrating sex differences in the functional organization of the human brain."

There has been very little coverage of this report in the mainstream media. You will find no mention of this study in The New York Times, the Wall Street Journal, or National Public Radio. I suspect that’s because most mainstream media are cautious of anything having to do with brain-based differences between women and men. Many of us are understandably wary that any claim of difference will lead to claims regarding ability . If men’s brains are different from women’s brains, doesn’t that imply that men will be better at some things and women will be better at other things? Especially when there is no overlap in the findings?

But “different” doesn’t necessarily imply “better.” As I stressed in the second edition of my book Why Gender Matters , apples and oranges are different. That doesn’t mean apples are better than oranges. Men and women are turning out to be different, more different than we may have imagined. That doesn’t mean that women are better than men, or vice versa. But it does suggest that if we ignore the differences, we may disadvantage both women and men.

Ryali, S., Zhang, Y., de los Angeles, C., Supekar, K., & Menon, V. (2024). Deep learning models reveal replicable, generalizable and behaviorally relevant sex differences in human functional brain organization. Proceedings of the National Academy of Sciences, volume 121, number 9, https://doi.org/10.1073/pnas.2310012121 .

Leonard Sax, M.D., Ph.D. , is a family physician, PhD psychologist, and author of Boys Adrift and Girls on the Edge .

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At any moment, someone’s aggravating behavior or our own bad luck can set us off on an emotional spiral that threatens to derail our entire day. Here’s how we can face our triggers with less reactivity so that we can get on with our lives.

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A Leak of Biometric Police Data Is a Sign of Things to Come

Thousands of law enforcement officials and people applying to be police officers in India have had their personal information leaked online—including fingerprints, facial scan images, signatures, and details of tattoos and scars on their bodies. If that wasn’t alarming enough, at around the same time, cybercriminals have started to advertise the sale of similar biometric police data from India on messaging app Telegram .

Last month, security researcher Jeremiah Fowler spotted the sensitive files on an exposed web server linked to ThoughtGreen Technologies, an IT development and outsourcing firm with offices in India, Australia, and the US. Within a total of almost 500 gigabytes of data spanning 1.6 million documents, dated from 2021 until when Fowler discovered them in early April, was a mine of sensitive personal information about teachers, railway workers, and law enforcement officials. Birth certificates, diplomas, education certificates, and job applications were all included.

Fowler, who shared his findings exclusively with WIRED, says within the heaps of information, the most concerning were those that appeared to be verification documents linked to Indian law enforcement or military personnel. While the misconfigured server has now been closed off, the incident highlights the risks of companies collecting and storing biometric data, such as fingerprints and facial images, and how they could be misused if the data is accidentally leaked.

“You can change your name, you can change your bank information, but you can't change your actual biometrics,” Fowler says. The researcher, who also published the findings on behalf of Website Planet , says this kind of data could be used by cybercriminals or fraudsters to target people in the future, a risk that’s increased for sensitive law enforcement positions.

Within the database Fowler examined were several mobile applications and installation files. One was titled “facial software installation,” and a separate folder contained 8 GB of facial data. Photographs of people’s faces included computer-generated rectangles that are often used for measuring the distance between points of the face in face recognition systems.

There were 284,535 documents labeled as Physical Efficiency Tests that related to police staff, Fowler says. Other files included job application forms for law enforcement officials, profile photos, and identification documents with details such as “mole at nose” and “cut on chin.” At least one image shows a person holding a document with a corresponding photo of them included on it. “The first thing I saw was thousands and thousands of fingerprints,” Fowler says.

Prateek Waghre, executive director of Indian digital rights organization Internet Freedom Foundation, says there is “vast” biometric data collection happening across India , but there are added security risks for people involved in law enforcement. “A lot of times, the verification that government employees or officers use also relies on biometric systems,” Waghre says. “If you have that potentially compromised, you are in a position for someone to be able to misuse and then gain access to information that they shouldn’t.”

It appears that some biometric information about law enforcement officials may already be shared online. Fowler says after the exposed database was closed down he also discovered a Telegram channel, containing a few hundred members, which was claiming to sell Indian police data, including of specific individuals. “The structure, the screenshots, and a couple of the folder names matched what I saw,” says Fowler, who for ethical reasons did not purchase the data being sold by the criminals so could not fully verify it was exactly the same data.

By Kim Zetter

By Aarian Marshall

By Will Knight

By Matt Burgess

“We take data security very seriously, have taken immediate steps to secure the exposed data,” a member of ThoughtGreen Technologies wrote in an email to WIRED. “Due to the sensitivity of data, we cannot comment on specifics in an email. However, we can assure you that we are investigating this matter thoroughly to ensure such an incident does not occur again.”

In follow-up messages, the staff member said the company had “raised a complaint” with law enforcement in India about the incident, but did not specify which organization they had contacted. When shown a screenshot of the Telegram post claiming to sell Indian police biometric data, the ThoughtGreen Technologies staff member said it is “not our data.” Telegram did not respond to a request for comment.

Shivangi Narayan, an independent researcher in India, says the country’s data protection law needs to be made more robust, and companies and organizations need to take greater care with how they handle people’s data. “A lot of data is collected in India, but nobody's really bothered about how to store it properly,” Narayan says. Data breaches are happening so regularly that people have “lost that surprise shock factor,” Narayan says. In early May, one cybersecurity company said it had seen a face-recognition data breach connected to one Indian police force, including police and suspect information .

The issues are wider, though. As governments, companies, and other organizations around the world increasingly rely on collecting people’s biometric data for proving their identity or as part of surveillance technologies, there’s an increased risk of the information leaking online and being abused. In Australia, for instance, a recent face recognition leak impacting up to a million people led to a person being charged with blackmail .

“So many other countries are looking at biometric verification for identities, and all of that information has to be stored somewhere,” Fowler says. “If you farm it out to a third-party company, or a private company, you lose control of that data. When a data breach happens, you’re in deep shit, for lack of a better term.”

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Detecting 'Hawking radiation' from black holes using today's telescopes

by David Appell , Phys.org

In 1974 Stephen Hawking famously claimed that black holes should emit particles as well as absorb them. This so-called "Hawking radiation" has not yet been observed, but now a research group from Europe has found that Hawking radiation should be observable by existing telescopes that are capable of detecting very high energy particles of light.

When two massive black holes collide and merge, or a neutron star and black hole do so, they emit gravitational waves, undulations in the fabric of spacetime that travel outward. Some of these waves wash over Earth millions or billions of years later. These waves were predicted by Einstein in 1916 and first directly observed by the LIGO detectors in 2016. Dozens of gravitational waves from black hole mergers have been detected since .

These mergers also emit a number of "black hole morsels," smaller black holes with masses of the order of an asteroid, created in the resulting extremely strong gravitational field around the merger due to so-called "nonlinear," high velocity effects in general relativity. These nonlinearities arise due to the inherently complex solutions to Einstein's equations, as warped spacetime and masses feedback on one another and both respond to and create new spacetime and masses.

This complexity also generates gamma ray bursts of extremely energetic photons. These bursts have similar characteristics, with a time delay from the merger of the order of their evaporation time. A morsel mass of 20 kilotons has an evaporation lifetime of 16 years, but this number can change drastically since the evaporation time is proportional to the morsel mass cubed.

Heavier morsels will initially provide a steady gamma ray burst signal, characterized by reduced particle energies, proportional to the Hawking temperature. The Hawking temperature is inversely proportional to a black hole's mass.

The research team showed, through numerical calculations using an open source public code called BlackHawk that calculates the Hawking evaporation spectra for any distribution of black holes, that the Hawking radiation from the black hole morsels creates gamma ray bursts that have a distinctive fingerprint. The work is published on the arXiv preprint server.

Detecting such events, which have multiple signals— gravitational waves , electromagnetic radiation , neutrino emissions —is called multimessenger astronomy in the astrophysical community, and is part of the observing programs at the LIGO gravitational wave detectors in the US, VIRGO in Italy and, in Japan, the KAGRA gravitational wave telescope.

Visible signals from black hole evaporation always include photons above the TeV range (a trillion electron volts, about 0.2 microjoules; for example, the CERN Large Hadron Collider in Europe, the largest particle accelerator on the planet, collides protons head-on with a total energy of 13.6 TeV). This provides a "golden opportunity," the group writes, for so-called high energy atmospheric Cherenkov telescopes to detect this Hawking radiation.

These Cherenkov telescopes are ground-based antenna dishes that can detect very energetic photons (gamma rays) in the energy range of 50 GeV (billion electron volts) to 50 TeV. These antennae accomplish that by detecting Cherenkov radiation flashes that are produced as the gamma rays cascade through the Earth's atmosphere, traveling faster than the ordinary wave velocity of light in air.

Recall that light travels slightly slower in air than it does in a vacuum, because air has an index of refraction slightly greater than one. The Hawking gamma ray radiation cascading down through the atmosphere exceeds this slower value, creating Cerenkov radiation (also called braking radiation—Bremsstrahlung in German). The blue light seen in pools of water that surround reaction rods in a nuclear reactor is an example of Cerenkov radiation.

There are now four telescopes that can detect these cascades of Cerenkov radiation—the High Energy Stereoscopic System (HESS) in Namibia, the Major Atmospheric Gamma Imaging Cherenkov Telescopes (MAGIC) on one of the Canary Islands, the First G-APD Cherenkov Telescope (FACT), also on La Palma Island in the Canary archipelago, and Very Energetic Radiation Imaging Telescope Array System (VERITAS) in Arizona. Though each uses different technology, they all can detect Cerenkov photons in the GeV-TeV energy range.

Detecting such Hawking radiation would also shed light (ahem…) on the production of black hole morsels, as well as particle production at energies higher than can be attained on Earth, and may carry signs of new physics such as supersymmetry, extra dimensions, or the existence of composite particles based on the strong force.

"It was a surprise to find that black hole morsels can radiate above the detection capabilities of current high energy Cherenkov telescopes on Earth," said Giacomo Cacciapaglia, lead author from the Université Lyon Claude Bernard 1 in Lyon, France. Noting that direct detection of Hawking radiation from black hole morsels would be the first evidence of the quantum behavior of black holes, he said "if the proposed signal is observed, we will have to question the current knowledge of the nature of black holes" and morsel production.

Cacciapaglia said they plan to contact colleagues from experimental groups, then to use the data collected to search for the Hawking radiation they propose.

Journal information: arXiv

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