Beyond Minutiae: A Fingerprint Individuality Model with Pattern, Ridge and Pore Features

  • Yi Chen
  • Anil K. Jain
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5558)

Abstract

Fingerprints are considered to be unique because they contain various distinctive features, including minutiae, ridges, pores, etc. Some attempts have been made to model the minutiae in order to get a quantitative measure for uniqueness or individuality of fingerprints. However, these models do not fully exploit information contained in non-minutiae features that is utilized for matching fingerprints in practice. We propose an individuality model that incorporates all three levels of fingerprint features: pattern or class type (Level 1), minutiae and ridges (Level 2), and pores (Level 3). Correlations among these features and their distributions are also taken into account in our model. Experimental results show that the theoretical estimates of fingerprint individuality using our model consistently follow the empirical values based on the public domain NIST-4 database.

Keywords

Fingerprint individuality statistical models probability of random correspondence (PRC) minutiae ridges pores 

References

  1. 1.
    Plaza, U.S. v., et al.: 179 F. Supp. 2d 492, E.D. Pa (2002)Google Scholar
  2. 2.
    Mitchell, U.S. v.: 365 F. 3d 215, 3d Cir. (2004)Google Scholar
  3. 3.
    Hearing Document of State of Maryland vs. Bryan Rose, Case No.: K06-0545, Circuit Court for Baltimore County (2007)Google Scholar
  4. 4.
    Ashbaugh, D.R.: Quantitative-Qualitative Friction Ridge Analysis: An Introduction to Basic and Advanced Ridgeology. CRC Press, Boca Raton (1999)Google Scholar
  5. 5.
    Pankanti, S., Prabhakar, S., Jain, A.K.: On the Individuality of Fingerprints. IEEE Trans. on Pattern Analysis and Machine Intelligence 24(8), 1010–1025 (2002)Google Scholar
  6. 6.
    Zhu, Y., Dass, S., Jain, A.K.: Statistical Models for Assessing the Individuality of Fingerprints. IEEE Trans. on Information Forensics and Security 2, 391–401 (2007)Google Scholar
  7. 7.
    Kingston, C.: Probabilistic Analysis of Partial Fingerprint Patterns. University of California, Berkeley (1964)Google Scholar
  8. 8.
    Champod, C., Lennard, C., Margot, P., Stoilovic, M.: Fingerprints and Other Ridge Skin Impressions. CRC Press, Boca Raton (2004)Google Scholar
  9. 9.
    Fang, G., Srihari, S., Srinivasan, H.: Generative Models for Fingerprint Individuality Using Ridge Types. In: Proc. International Workshop on Computational Forensics, Manchester, UK (2007)Google Scholar
  10. 10.
    Roddy, A.R., Stosz, J.D.: Fingerprint Features - Statistical Analysis and System Performance Estimates. Proc. IEEE 85(9), 1390–1421 (1997)Google Scholar
  11. 11.
    Henry, E.: Classification and Uses of Fingerprints. Routledge and Sons, London (1900)Google Scholar
  12. 12.
    Figueiredo, M., Jain, A.K.: Unsupervised Learning of Finite Mixture Models. IEEE Trans. on Pattern Analysis and Machine Intelligence 24(3), 381–396 (2002)Google Scholar
  13. 13.
    Mardia, K.V.: Statistics of Directional Data. Academic Press, London (1972)Google Scholar
  14. 14.
    Skellam, J.G.: The Frequency Distribution of the Difference Between Two Poisson Variates Belonging to Different Populations. Jounal of the Royal Statistical Society 109(3), 294 (1946)Google Scholar
  15. 15.
    Jain, A.K., Hong, L., Bolle, R.: On-line Fingerprint Verification. IEEE Trans. on Pattern Analysis and Machine Intelligence 19(4), 302–314 (1997)Google Scholar
  16. 16.
    NIST Special Database 4 (2009), http://www.nist.gov/srd/nistsd4.htm
  17. 17.
    NIST Special Database 30 (2009), http://www.nist.gov/srd/nistsd30.htm
  18. 18.
    Jain, A.K., Prabhakar, S., Pankanti, S.: On the Similarity of Identical Twin Fingerprints. Pattern Recognition 35(8), 2653–2663 (2002)Google Scholar
  19. 19.
    Jain, A.K., Chen, Y., Demirkus, M.: Pores and Ridges: High-Resolution Fingerprint Matching Using Level 3 Features. IEEE Trans. on Pattern Analysis and Machine Intelligence 29(1), 15–27 (2007)Google Scholar
  20. 20.
    Neurotechnologija Verifinger 4.2 SDK, http://www.neurotechnology.com (2009)

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Yi Chen
    • 1
    • 2
  • Anil K. Jain
    • 1
  1. 1.Michigan State UniversityUSA
  2. 2.DigitalPersona Inc.USA

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