Skip to main content
SpringerLink
Log in

Altered Fingerprint Detection

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

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

Abstract

The success of Automated Fingerprint Identification Systems (AFIS) has lead to an increased number of incidents where individuals alter their fingerprints in order to evade identification. This is especially seen at border crossings where fingerprints are subject to comparison against a watch list. This chapter discusses methods for automatically detecting altered fingerprints. The methods are based on analyses of two different local characteristics of a fingerprint image. The first analysis identifies irregularities in the pixel-wise orientations which share similar characteristics to singular point. The second analysis compares minutia orientations covering a local, but larger area than the first analysis. A global density map is created in each of the analysis in order to identify the distribution of the analyzed discrepancies. Experimental results suggest that the method yields performance fully comparable to the current state-of-the-art method. Further improvements can be achieved by combining the most efficient analysis of the two methods. The promising results achieved in this study are attractive for further investigations. Especially, studies into the possibility of introducing alteration detection into standard quality measures of fingerprints which would improve AFIS and contribute to the fight against fraud.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 79.99
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

References

  1. GUC100 multisensor fingerprint database for in-house (Semipublic) performance test, January 2009. Gjøvik University College (GUC)

    Google Scholar 

  2. Fingerprint database. Alterations caused by diseases, March 2013. Faculty of Information Technology at Brno University of Technology and the research group STRaDe in collaboration with dermatologists from FN Olomouc

    Google Scholar 

  3. Bazen AM, Gerez SH (2001) Extraction of singular points from directional fields of fingerprints. In: The 7th annual CTIT workshop mobile communications in perspective workshop. Centre for Telematics and Information Technology, Enschede, pp 41–44

    Google Scholar 

  4. Bazen AM, Gerez SH (2002) Systematic methods for the computation of the directional fields and singular points of fingerprints. IEEE Trans Pattern Anal Mach Intell 24(7):905–919. 060.02

    Google Scholar 

  5. Bo J, Ping TH, Lan XM (2008) Fingerprint singular point detection algorithm by poincaré index. WTOS 7(12):1453–1462

    MATH  Google Scholar 

  6. Cappelli MM, Maio D, Maltoni D, Wayman JL, Jain AK (2004) FVC2004: Third fingerprint verification competition. In Proceedings of the first international conference on biometric authentication 1–7

    Google Scholar 

  7. Chikkerur SS, Cartwright AN, Govindaraju V (2005) Fingerprint image enhancement using STFT analysis. In IN PROC. ICAPR, pp 20–29

    Google Scholar 

  8. Commission, E. (2012) Visa information system—VIS. http://ec.europa.eu/dgs/home-affairs/what-we-do/policies/borders-and-visas/visa-information-system/index_en.htm

  9. Cummins H (1935) Attempts to alter and obliterate finger-prints. J Crim Law Criminol 25:982–991

    Google Scholar 

  10. Doležel M, Drahanský M, Urbánek J (2013) Einfluss von hauterkrankungen auf den biometrischen erkennungsprozess. Datenschutz un Datensicherheit (DuD) Heft 6-2013, 358–362

    Google Scholar 

  11. Ellingsgaard J (2013) Fingerprint alteration detection. Master thesis, Technical University of Denmark. (June 2013)

    Google Scholar 

  12. Ellingsgaard J, Sousedik C, Busch C (2014) Detecting fingerprint alterations by orientation field and minutiae orientation analysis. In 2014 international workshop on biometrics and forensics (IWBF), (March 2014), pp 1–6

    Google Scholar 

  13. Feng J, Jain AK, Ross A (2009) Fingerprint alteration. Tech. Rep. MSU-CSE-09-30, Department of Computer Science, Michigan State University, East Lansing, Michigan. (December 2009)

    Google Scholar 

  14. Gottschlich C, Mikaelyan A, Olsen M, Bigun J, Busch C (2015) Improving fingerprint alteration detection. In Proceedings of 9th international symposium on image and signal processing and analysis (ISPA 2015)

    Google Scholar 

  15. Gu J, Zhou J (2003) A novel model for orientation field of fingerprints. In IEEE computer society conference on computer vision and pattern recognition, pp 493–498

    Google Scholar 

  16. Hoover JE, Collins FL (1943) The man without fingerprints. Collier’s Weekly (January 1943), p 16

    Google Scholar 

  17. ISO/IEC JTC1 SC37 Biometrics (2009) ISO/IEC 29794-1:2009 Information Technology—Biometric Sample Quality—Part 1: Framework. International Organization for Standardization

    Google Scholar 

  18. ISO/IEC JTC1 SC37 Biometrics (2016) ISO/IEC 30107-1. Information Technology—Biometric presentation attack detection—Part 1: Framework. International Organization for Standardization

    Google Scholar 

  19. ISO/IEC JTC1 SC37 Biometrics (2016) ISO/IEC DIS 30107-3. Information Technology—Biometric presentation attack detection—Part 3: Testing and Reporting. International Organization for Standardization

    Google Scholar 

  20. Jain AK (1989) Fundamentals of digital image processing. Prentice-Hall Inc, Upper Saddle River, NJ

    MATH  Google Scholar 

  21. Jain AK, Prabhakar S, Hong L, Pankanti S (2000) Filterbank-based fingerprint matching. IEEE Trans Image Process 9:846–859

    Article  Google Scholar 

  22. Jain AK, Yoon S (2012) Automatic detection of altered fingerprints. IEEE Comput 45(1):79–82

    Article  Google Scholar 

  23. Jin C, Kim H, Elliott S (2007) Liveness detection of fingerprint based on band-selective fourier spectrum. In Proceedings of the 10th international conference on information security and cryptology, ICISC’07. Springer, Berlin, pp 168–179

    Google Scholar 

  24. Kamei T, Mizoguchi M (1995) Image filter design for fingerprint enhancement. In international symposium on computer vision, 1995. Proceedings, pp 109–114

    Google Scholar 

  25. Kawagoe M, Tojo A (1984) Fingerprint pattern classification. Pattern Recognit 17(3):295–303

    Article  Google Scholar 

  26. Kim B-G, Park D-J (2002) Adaptive image normalisation based on block processing for enhancement of fingerprint image. Electron Lett 38(14):696–698

    Article  Google Scholar 

  27. Lim E, Jiang X, Yau W-Y (2002) Fingerprint quality and validity analysis. In 2002 international conference on image processing (ICIP), vol 1, pp 469–472

    Google Scholar 

  28. Liu M, Jiang X, Kot AC (2005) Fingerprint reference-point detection. EURASIP J. Appl Signal Process 2005:498–509

    Article  MATH  Google Scholar 

  29. MailOnline (2009) Calais migrants mutilate fingertips to hide true identity (July 2009). http://www.dailymail.co.uk/news/article-1201126/Calais-migrants-mutilate-fingertips-hide-true-identity.html

  30. Maltoni D, Maio D, Jain AK, Prabhakar S (2009) Handbook of fingerprint recognition, 2nd edn. Springer Publishing Company, Incorporated

    Book  MATH  Google Scholar 

  31. Merkle J, Ihmor H, Korte U, Niesing M, Schwaiger M (2010) Performance of the fuzzy vault for multiple fingerprints (extended version). arXiv:1008.0807

  32. NIST (2012) Development of NFIQ 2.0—quality feature definitions. Tech. rep. (June 2012)

    Google Scholar 

  33. Olsen M, Smida V, Busch C (2015) Finger image quality assessment features—definitions and evaluation. IET J Biom. (December 2015)

    Google Scholar 

  34. Olsen MA, Xu H, Busch C (2012) Gabor filters as candidate quality measure for NFIQ 2.0. In 2012 5th IAPR international conference on biometrics (ICB), pp 158–163

    Google Scholar 

  35. Petrovici A, Lazar C (2010) Identifying fingerprint alteration using the reliability map of the orientation field. The Annals of the Univeristy of Craiova. Series: Automation, Computers, Electronics and Mechatronics 7(34), 45–52. (1)

    Google Scholar 

  36. Rajanna U, Erol A, Bebis G (2010) A comparative study on feature extraction for fingerprint classification and performance improvements using rank-level fusion. Pattern Anal Appl 13(3):263–272

    Article  MathSciNet  Google Scholar 

  37. Ravishankar RA (1990) A taxonomy for texture description and identification. Springer, New York, NY

    Google Scholar 

  38. Samischenko S (2001) Atlas of the unusual papilla patterns/atlas neobychnykh papilliarnykh uzorov. Urisprudentsiia, Moscow

    Google Scholar 

  39. Watson C, Garris M, Tabassi C, Wilson RM (2012) NIST biometric image software. (December 2012). http://www.nist.gov/itl/iad/ig/nbis.cfm

  40. Watson CI, Candela G, Grother P (1994) Comparison of fft fingerprint filtering methods for neural network classification. NISTIR 5493 (1994)

    Google Scholar 

  41. Wertheim K (1998) An extreme case of fingerprint mutilation. J Forensic Identif 48:466–477

    Google Scholar 

  42. Xie SJ, Yang JC, Yoon S, Park DS (2008) An optimal orientation certainty level approach for fingerprint quality estimation. In Second international symposium on intelligent information technology application, 2008. IITA’08. (2008), vol 3, pp 722–726

    Google Scholar 

  43. Yoon S, Feng J, Jain AK (2012) Altered fingerprints: analysis and detection. IEEE Trans Pattern Anal Mach Intell 34(3):451–464

    Article  Google Scholar 

  44. Yoon S, Feng J, Jain AK, Ross A (2009) Automatic detection of altered fingerprints. ICPR. (August 2009, Presentation)

    Google Scholar 

  45. Zhou J, Chen F, Gu J (2009) A novel algorithm for detecting singular points from fingerprint images. IEEE Trans Pattern Anal Mach Intell 31(7):1239–1250

    Article  Google Scholar 

Download references

Acknowledgements

The authors would like to thank Anil Jain (Michigan State University), Christophe Champod (Universite de Lausanne), Martin Drahansky (Brno University of Technology, Faculty of Information Technology—STRaDe) and FN Olomouc that kindly provided access to the altered fingerprint data used in this work. Also thanks to Ctirad Sousedik and Martin Olsen for fruitful discussions. This work is carried out under the funding of the EU-FP7 INGRESS project (Grant No. SEC-2012-312792).

Author information

Authors and Affiliations

  1. Technical University of Denmark, Anker Engelunds Vej 1, 2800, Kongens Lyngby, Denmark

    John Ellingsgaard

  2. Norwegian Biometrics Laboratory, Norwegian University of Science and Technology (NTNU), P.O. Box 191, 2802, Gjøvik, Norway

    Christoph Busch

Authors
  1. John Ellingsgaard
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Christoph Busch
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Christoph Busch .

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

Ellingsgaard, J., Busch, C. (2017). Altered Fingerprint Detection. 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_5

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-50673-9_5

  • 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