IWCF 2010: Computational Forensics pp 173-184 | Cite as

Latent Fingerprint Rarity Analysis in Madrid Bombing Case

  • Chang Su
  • Sargur N. Srihari
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6540)

Abstract

Rarity of latent fingerprints is important to law enforcement agencies in forensics analysis. While tremendous efforts have been made in 10-print individuality studies, latent fingerprint rarity continues to be a difficult problem and has never been solved because of the small finger area and poor impression quality. The proposed method is able to predict the core points of latent prints using Gaussian processes and align the latent prints by overlapping the core points. A novel generative model is also proposed to take into account the dependency on nearby minutiae and the confidence of minutiae in the probability of random correspondence calculation. The new methods are illustrated by experiments on the well-known Madrid bombing case. The results show that the probability that at least one fingerprint in the FBI IAFIS databases (over 470 million fingerprints) matches the bomb site latent is 0.93 which is large enough to lead to misidentification.

Keywords

latent fingerprints rarity generative models 
This is a preview of subscription content, log in to check access.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    US Department of Justice Office of the Inspector General: A review of the FBI’s handling of the Brandon Mavfield case (March 2006), http://www.justice.gov/oig/special/s0601/PDF_list.htm
  2. 2.
    Wertheim, K., Maceo, A.: The critical stage of friction ridge and pattern formation. Journal of Forensic Identification 52(1), 35–85 (2002)Google Scholar
  3. 3.
    Henry, E.R.: Classification and Uses of Fingerprints, London (1900)Google Scholar
  4. 4.
    Trauring, M.: Automatic comparison of finger-ridge patterns. Nature 197, 938–940 (1963)CrossRefGoogle Scholar
  5. 5.
    Pankanti, S., Prabhakar, S., Jain, A.K.: On the individuality of fingerprints. IEEE Trans. Pattern Anal. Mach. Intell. 24(8), 1010–1025 (2002)CrossRefMATHGoogle Scholar
  6. 6.
    Zhu, Y., Dass, S.C., Jain, A.K.: Statistical models for assessing the individuality of fingerprints. IEEE Transactions on Information Forensics and Security 2(3-1), 391–401 (2007)CrossRefGoogle Scholar
  7. 7.
    Su, C., Srihari, S.N.: Generative models for fingerprint individuality using ridge models. In: ICPR 2008, pp. 1–4. IEEE, Los Alamitos (2008)Google Scholar
  8. 8.
    Chen, Y., Jain, A.K.: Beyond minutiae: A fingerprint individuality model with pattern, ridge and pore features. In: Tistarelli, M., Nixon, M.S. (eds.) ICB 2009. LNCS, vol. 5558, pp. 523–533. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  9. 9.
    Jain, A.K., Maltoni, D.: Handbook of Fingerprint Recognition. Springer-Verlag New York, Inc., Secaucus (2003)MATHGoogle Scholar
  10. 10.
    Kawagoe, M., Tojo, A.: Fingerprint pattern classification. Pattern Recogn 17(3), 295–303 (1984)CrossRefGoogle Scholar
  11. 11.
    Bazen, A.M., Gerez, S.H.: Systematic methods for the computation of the directional fields and singular points of fingerprints. IEEE Trans. Pattern Anal. Mach. Intell. 24(7), 905–919 (2002)CrossRefGoogle Scholar
  12. 12.
    Jain, A.K., Prabhakar, S., Hong, L.: A multichannel approach to fingerprint classification. IEEE Trans. Pattern Anal. Mach. Intell. 21(4), 348–359 (1999)CrossRefGoogle Scholar
  13. 13.
    Jain, A.K., Prabhakar, S., Hong, L., Pankanti, S.: Filterbank-based fingerprint matching. IEEE Transactions on Image Processing 9, 846–859 (2000)CrossRefGoogle Scholar
  14. 14.
    Phillips, D.: A fingerprint orientation model based on 2d fourier expansion (fomfe) and its application to singular-point detection and fingerprint indexing. IEEE Trans. Pattern Anal. Mach. Intell. 29(4), 573–585 (2007)CrossRefGoogle Scholar
  15. 15.
    Wang, X., Li, J., Niu, Y.: Definition and extraction of stable points from fingerprint images. Pattern Recogn. 40(6), 1804–1815 (2007)CrossRefMATHGoogle Scholar
  16. 16.
    Liu, M., Jiang, X., Kot, A.C.: Fingerprint reference-point detection. EURASIP J. Appl. Signal Process. 2005, 498–509 (2005)CrossRefMATHGoogle Scholar
  17. 17.
    Su, C., Srihari, S.N.: Latent fingerprint core point prediction based on gaussian processes. In: ICPR 2010. IEEE, Los Alamitos (2010)Google Scholar
  18. 18.
    Rasmussen, C.E., Williams, C.K.I.: Gaussian Processes for Machine Learning. The MIT Press, Cambridge (2006)MATHGoogle Scholar
  19. 19.
    Garris, M.D., McCabe, R.M.: Nist special database 27: Fingerprint minutiae from latent and matching tenprint images (2000), http://www.nist.gov/srd/nistsd27.htm
  20. 20.
    Scolve, S.C.: The occurence of fingerprint characteristics as a two dimensional process. Journal of the American Statistical Association 367(74), 588–595 (1979)CrossRefGoogle Scholar
  21. 21.
    Stoney, D.A.: Distribution of epidermal ridge minutiae. American Journal of Physical Anthropology 77, 367–376 (1988)CrossRefGoogle Scholar
  22. 22.
    Chen, J., Moon, Y.: A statistical study on the fingerprint minutiae distribution. In: ICASSP 2006 Proceedings., vol. 2, p. II (2006)Google Scholar
  23. 23.
    Watson, C., Garris, M., Tabassi, E., Wilson, C., McCabe, R., Janet, S.: User’s Guide to NIST Fingerprint Image Software 2 (NFIS2). NIST (2004)Google Scholar
  24. 24.
    Su, C., Srihari, S.N.: Probability of random correspondence for fingerprints. In: Geradts, Z.J.M.H., Franke, K.Y., Veenman, C.J. (eds.) IWCF 2009. LNCS, vol. 5718, pp. 55–66. Springer, Heidelberg (2009)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Chang Su
    • 1
  • Sargur N. Srihari
    • 1
  1. 1.University at BuffaloAmherstUSA

Personalised recommendations