Skip to main content
SpringerLink
Log in

Automated Fingerprint Identification Systems: From Fingerprints to Fingermarks

  • Chapter
  • First Online:
Handbook of Biometrics for Forensic Science

Part of the book series: Advances in Computer Vision and Pattern Recognition ((ACVPR))

Abstract

The aim of this chapter is to present the automated fingerprint recognition technology and its use for forensic applications. After a brief historical review, we provide an introduction to modern Automated Fingerprint Identification Systems (AFIS ) by discussing their functionalities and accuracy. The topic then becomes more technical and goes through some of the recently introduced approaches for fingerprint recognition (both for fingerprint and fingermarks ). Forensic applications exploiting the recognition of fingerprints (identity verification and identification) and fingermarks (forensic intelligence, investigation and evaluation) are then described. Finally, a discussion about the current topics and foreseeable challenges in terms of technology and application concludes the chapter.

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

Access this chapter

Log in via an institution
eBook
USD 16.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 99.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 129.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    The finger dermatoglyphics and their standard rolled or flat inked or scanned impressions are named fingerprints, whereas the recovered or lifted traces are named fingermarks (latent fingerprints is a popular but imprecise synonym for fingermarks) [2].

  2. 2.

    In open-set scenarios, some of the searched users have not a record in the database (non-mated search), while in closed-set scenarios it is assumed to search only users with at least one record in the database (mated search).

References

  1. Lee HC, Gaensslen RE (2001) Advances in fingerprint technology. Elsevier Publishing, New York

    Book  Google Scholar 

  2. Meuwly D (2015) Forensic use of fingermarks and fingerprints. In: Stan Z., Jain, AL (eds) Encyclopedia of biometrics, 2nd edn. Springer

    Google Scholar 

  3. Federal Bureau of Investigation (1984) The Science of fingerprints: classification and uses. Federal Bureau of Investigation, Government Publication, Washington, DC, US

    Google Scholar 

  4. Federal Bureau of Investigation (1991) The FBI fingerprint identification automation program: issues and options. Federal Bureau of Investigation, Government Publication, Washington, DC, US

    Google Scholar 

  5. Moses KR, Higgins P, McCabe M, Prabhakar S, Swann S (2011) Automated fingerprint identification system (AFIS). In: SWGFAST—the fingerprint sourcebook, scientific working group on friction ridge analysis study and technology and national institute of Justice, pp 1–33

    Google Scholar 

  6. Wegstein JH, Rafferty JF, Pencak WJ (1968) Matching fingerprints by computer, National Bureau of Standards, U.S. Department of Commerce, Washington, DC, Technical Note 466

    Google Scholar 

  7. Wegstein JH (1969) A computer oriented single-fingerprint identification system, National Bureau of Standards, U.S. Department of Commerce, Washington, DC, Technical Note 443 1969

    Google Scholar 

  8. Wegstein JH (1970) Automated fingerprint identification, National Bureau of Standards, U.S. Department of Commerce, Washington, DC, Technical Note 538

    Google Scholar 

  9. Wegstein JH (1972) Manual and automated fingerprint registration, National Bureau of Standards, U.S. Department of Commerce, Washington, DC, NBS Technical Note 730

    Google Scholar 

  10. Wegstein JH (1972) The M40 fingerprint matcher, National Bureau of Standards, U.S. Department of Commerce, Washington, DC, Technical Note 878

    Google Scholar 

  11. Wegstein JH, Rafferty JF (1978) The LX39 latent fingerprint matcher, National Bureau of Standards, U.S. Department of Commerce, Washington, DC, Special Publication 500–536

    Google Scholar 

  12. Wegstein JH, Rafferty JF (1979) The automated identification of fingerprints. In: Dermatoglyphics—fifty years later, Washington, DC

    Google Scholar 

  13. Wegstein JH (1982) An automated fingerprint identification system, National Bureau of Standards, U.S. Department of Commerce, Washington, DC, NBS Special Publication 500–589

    Google Scholar 

  14. Stoney DA (1991) What made us ever think we could individualize using statistics. J Foren Sci Soc 31(2)

    Google Scholar 

  15. Watson CI et al (2015) Fingerprint vendor technology evaluation 2012, NIST, NIST Interagency/Internal Report (NISTIR)—8034

    Google Scholar 

  16. Maltoni D, Maio D, Jain AK, Prabhakar S (2009) Handbook of fingerprint recognition, 2nd edn. Springer, New York, NJ, USA

    Google Scholar 

  17. FBI—CJIS division (1999) Electronic fingerprint transmission specification, FBI, CJIS-RS-0010 (V7)

    Google Scholar 

  18. Indovina M, Hicklin RA, Kiebuzinski GI (2011) ELFT-EFS evaluation of latent fingerprint technologies: extended feature sets (Evaluation 1), National Institute of Standards and Technology, US Department of Commerce, NISTIR 7775

    Google Scholar 

  19. Indovina MD, Dvornychenko V, Hicklin RA, Kiebuzinski GI (2012) ELFT-EFS evaluation of latent fingerprint technologies: extended feature sets (Evaluation 2), National Institute of Standards and Technology, US Department of Commerce, NISTIR 7859

    Google Scholar 

  20. Jain AK, Feng J (2011) Latent fingerprint matching. IEEE Trans Pattern Anal Mach Intell 33(1):88–100

    Article  Google Scholar 

  21. Zhao Q, Jain AK (2010) On the utility of extended fingerprint features: a study on pores. In: CVPR workshop on biometrics, San Francisco

    Google Scholar 

  22. Cappelli R, Ferrara M, Maltoni D (2010) Minutia Cylinder-Code: a new representation and matching technique for fingerprint recognition. IEEE Trans Pattern Anal Mach Intell 32(12):2128–2141

    Article  Google Scholar 

  23. Cappelli R, Ferrara M, Maio D (2012) A fast and accurate palmprint recognition system based on minutiae. IEEE Trans Syst Man Cybern Part B 42(3):956–962

    Article  Google Scholar 

  24. Cappelli R, Ferrara M, Maltoni D (2011) Fingerprint Indexing based on minutia cylinder code. IEEE Trans Pattern Anal Mach Intell 33(5):1051–1057

    Article  Google Scholar 

  25. Cappelli R, Ferrara M, Maltoni D, Tistarelli M (2010) MCC: a baseline algorithm for fingerprint verification in FVC-onGoing. In: Proceedings 11th international conference on control, automation, robotics and vision (ICARCV), Singapore

    Google Scholar 

  26. Cappelli R, Ferrara M, Maltoni D (2015) Large-scale fingerprint identification on GPU. Inf Sci 306:1–20

    Article  Google Scholar 

  27. Paulino AA, Feng J, Jain AK (2013) Latent fingerprint matching using descriptor-based hough transform. IEEE Trans Inf Forensics Secur 8(1):31–45

    Article  Google Scholar 

  28. Si X, Feng J, Zhou J (2014) Enhancing latent fingerprints on banknotes. In: IEEE international joint conference on biometrics, Clearwater, FL, USA, pp 1–8

    Google Scholar 

  29. Cao K, Liu E, Jain AK (2014) Segmentation and enhancement of latent fingerprints: a coarse to fine ridge structure dictionary. IEEE Trans Pattern Anal Mach Intell 36(9):1847–1859

    Google Scholar 

  30. Zhang J, Lai R, Kuo C-CJ (2013) Adaptive directional total-variation model for latent fingerprint segmentation. IEEE Trans Inf Forensics Secur 8(8):1261–1273

    Google Scholar 

  31. Choi H, Boaventura M, Boaventura IAG, Jain AK (2012) Automatic segmentation of latent fingerprints. In: IEEE fifth international conference on biometrics: theory, applications, Arlington, VA, USA, pp 303–310

    Google Scholar 

  32. Zhao Q, Jain AK (2012) Model based separation of overlapping latent fingerprints. IEEE Trans Inf Forensics Secur 7(3):904–918

    Google Scholar 

  33. Zhang N, Zang Y, Yang X, Jia X, Tian J (2014) Adaptive orientation model fitting for latent overlapped fingerprints separation. IEEE Trans Inf Forensics Secur 9(10):1547–1556

    Article  Google Scholar 

  34. Feng J, Shi Y, Zhou J (2012) Robust and efficient algorithms for separating latent overlapped fingerprints. IEEE Trans Inf Forensics Secur 7(5):1498–1510

    Article  Google Scholar 

  35. Cappelli R, Maio D, Maltoni D (2009) Semi-automatic enhancement of very low quality fingerprint. In: 6th international symposium on image and signal processing and analysis (ISPA09), Salzburg, pp 678–683

    Google Scholar 

  36. Feng J, Zhou J, Jain AK (2013) Orientation field estimation for latent fingerprint enhancement. IEEE Trans Pattern Anal Mach Intell 35(4):925–940

    Google Scholar 

  37. Yang X, Feng J, Zhou J (2014) Localized dictionaries based orientation field estimation for latent fingerprints. IEEE Trans Pattern Anal Mach Intell 36(5):955–969

    Article  Google Scholar 

  38. BioLab. (2015) FVC-onGoing web site. http://biolab.csr.unibo.it/fvcongoing

  39. Arora SS, Liu E, Cao K, Jain AK (2014) Latent fingerprint matching: performance gain via feedback from exemplar prints. IEEE Trans Pattern Anal Mach Intell 36(12):2452–2465

    Article  Google Scholar 

  40. Meuwly D (2014) Friction ridge skin—AFIS. In: Jamieson A, Moenssens A (eds) Encyclopedia of forensic science, Chichester, UK. Wiley

    Google Scholar 

  41. Meuwly D (2010) ID management in 2020, ID.academy. The Hague

    Google Scholar 

  42. Plomp MGA, Grijpink JHAM (2011) Combating identity fraud in the public domain: information strategies for healthcare and criminal justice. In Proceedings of the 11th European conference on e-government, Ljubljana, Slovenia

    Google Scholar 

  43. Meuwly D, Veldhuis R (2012) Forensic biometrics: from two communities to one discipline. In: 2012 BIOSIG-proceedings of the international conference of the biometrics special interest group (BIOSIG), pp 207–218

    Google Scholar 

  44. Interpol (2009) Disaster victims identification guide. Interpol, Lyon

    Google Scholar 

  45. Biedermann A, Taroni F, Margot P (2012) Reply to Budowle, Ge, Chakraborty and Gill-King: use of prior odds for missing persons identifications. Investig Genet 3:1–2

    Google Scholar 

  46. Budowle B, Ge J, Chakraborty R, Gill-King H (2011) Use of prior odds for missing persons identifications. Investig Genet 2:1–6

    Article  Google Scholar 

  47. Ribaux O, Walsh SJ, Margot P (2006) The contribution of forensic science to crime analysis and investigation: forensic intelligence. Forensic Sci Int 156:171–181

    Article  Google Scholar 

  48. Europol (2011) Europol information management: products and services. Europol, The Hague

    Google Scholar 

  49. Locard E (1920) L’enquête criminelle et les méthodes scientifiques. Ernst Flammarion, Paris

    Google Scholar 

  50. Langenburg GM (2012) A critical analysis and study of the ACE-V process, University of Lausanne, Switzerland, PhD thesis

    Google Scholar 

  51. Fine GE (2006) A review of the FBI’s handling of the Brandon Mayfield case, Office of the Inspector General, U.S. Department of Justice

    Google Scholar 

  52. Dessimoz D, Champod C (2008) Linkages between biometrics and forensic science. In: Handbook of biometrics. Springer, pp. 425–459

    Google Scholar 

  53. Evett I (1998) Towards a uniform framework for reporting opinions in forensic science casework. Sci Justice 38(3):198–202

    Article  Google Scholar 

  54. Good IJ (1991) Weight of evidence and the Bayesian likelihood ratio. In: Aitken CGG, Stoney DA (eds) The use of statistics in forensic science. Ellis Horwood, Chichester UK, pp 85–106

    Google Scholar 

  55. Saks M, Koehler J (2005) The coming paradigm shift in forensic identification science. Science 309(5736):892–895

    Article  Google Scholar 

  56. Meuwly D (2006) Forensic individualisation from biometric data. Sci Justice 46(4):205–213

    Article  Google Scholar 

  57. Neumann C et al (2006) Computation of likelihood ratios in fingerprint identification for configurations of three minutiae. J Forensic Sci 51(6):1255–1266

    Article  Google Scholar 

  58. Lindley DV (1977) A problem in forensic science. Biometrika 64(2):207–213

    Article  MathSciNet  Google Scholar 

  59. Van Leeuwen DA, Brümmer N (2007) An introduction to application-independent evaluation of speaker recognition systems. In: Speaker classification I. Springer, pp 330–353

    Google Scholar 

  60. Bolck A, Weyermann C, Dujourdy L, Esseiva P, van den Berg J (2009) Different likelihood ratio approaches to evaluate the strength of evidence of MDMA tablet comparisons. Forensic Sci Int 191(1):42–51

    Article  Google Scholar 

  61. Gonzalez-Rodriguez J, Drygajlo A, Ramos-Castro D, Garcia-Gomar M, Ortega-Garcia J (2006) Robust estimation, interpretation and assessment of likelihood ratios in forensic speaker recognition. Comput Speech Lang 20(2):331–355

    Article  Google Scholar 

  62. Jain A, Ross A (2015) Bridging the gap: from biometrics to forensics. In: Philosoph Trans Roy Soc B Biol Sci 370(1674)

    Google Scholar 

  63. Alberink I, de Jongh A (2015) Authors’ Response. J Forensic Sci 60(1):257–258

    Article  Google Scholar 

  64. Alberink I, de Jongh A, Rodriguez C (2014) Fingermark evidence evaluation based on automated fingerprint identification system matching scores: the effect of different types of conditioning on likelihood ratios. J Forensic Sci 59(1):70–81

    Article  Google Scholar 

  65. Neumann C, Saunders CP (2014) Commentary on: Alberink I, de Jongh A, Rodriguez C. Fingermark evidence evaluation based on automated fingerprint identification system matching scores: the effect of different types of conditioning on likelihood ratios. J Forensic Sci 59(1):70–81

    Article  Google Scholar 

  66. Smith ML, Noorman ME, Martin AK (2010) Automating the public sector and organizing accountabilities. Commun Assoc Inf Syst 26(1)

    Google Scholar 

  67. Neumann C, Evett IW, Skerrett J (2012) Quantifying the weight of evidence from a forensic fingerprint comparison: a new paradigm. J Roy Stat Soc: Ser A (Stat Soc) 175(2):371–415

    Article  MathSciNet  Google Scholar 

  68. Neumann C (2012) Statistics and probabilities as a means to support fingerprint examination. In: Ramotowski R (ed) Lee and Gaensslen’s advances in fingerprint technology. CRC Press, pp 419–466

    Google Scholar 

  69. Lindoso A, Entrena L, Izquierdo J (2007) FPGA-based acceleration of fingerprint minutiae matching. In: 2007 3rd southern conference on programmable logic, 2007. SPL’07, Mar del Plata, Argentina, pp 81–86

    Google Scholar 

  70. Jiang RM, Crookes D (2008) FPGA-based minutia matching for biometric fingerprint image database retrieval. J Real-Time Image Proc 3(3):177–182

    Article  Google Scholar 

  71. Peralta D, Triguero I, Sanchez-Reillo R, Herrera F, Benitez JM (2014) Fast fingerprint identification for large databases. Pattern Recogn 47(2):588–602

    Article  Google Scholar 

  72. Gutierrez PD, Lastra M, Herrera F, Benitez JM (2014) A high performance fingerprint matching system for large databases based on GPU. IEEE Trans Inf Forensics Secur 9(1):62–71

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Ingegneria e Scienze Informatiche, Università di Bologna, Via Sacchi 3, 47521, Cesena, Forlì-Cesena, Italy

    Davide Maltoni & Raffaele Cappelli

  2. Services, Cybersecurity and Safety, Netherlands Forensic Institute, University of Twente, Drienerlolaan 5, 7522, NB Enschede, The Netherlands

    Didier Meuwly

Authors
  1. Davide Maltoni
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Raffaele Cappelli
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Didier Meuwly
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Davide Maltoni .

Editor information

Editors and Affiliations

  1. University of Sassari, Alghero, Sassari, Italy

    Massimo Tistarelli

  2. University of Lausanne, Lausanne, Switzerland

    Christophe Champod

Rights and permissions

Reprints and permissions

Copyright information

© 2017 Springer International Publishing AG

About this chapter

Cite this chapter

Maltoni, D., Cappelli, R., Meuwly, D. (2017). Automated Fingerprint Identification Systems: From Fingerprints to Fingermarks. In: Tistarelli, M., Champod, C. (eds) Handbook of Biometrics for Forensic Science. Advances in Computer Vision and Pattern Recognition. Springer, Cham. https://doi.org/10.1007/978-3-319-50673-9_3

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-50673-9_3

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-50671-5

  • Online ISBN: 978-3-319-50673-9

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation