Extraction of Stable Points from Fingerprint Images Using Zone Could-be-in Theorem

  • Xuchu Wang
  • Jianwei Li
  • Yanmin Niu
  • Weimin Chen
  • Wei Wang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3832)

Abstract

This paper presents a novel zone Could-be-in theorem, and applies it to interpret and extract singular points (cores and deltas) and estimate directions of cores in a fingerprint image. Where singular points are regarded as stable points (attracting points and rejecting points just according to their clockwise or anticlockwise rotation), and pattern zones are stable zones. Experimental results validate the theorem. The corresponding algorithm is compared with popular Poincaré index algorithm under two new indices: reliability index (RI) and accuracy cost (AC) in FVC2004 datasets. The proposed algorithm are higher 36.49% in average RI, less 2.47 in average AC, and the advantage is more remarkable with the decrease of block size.

References

  1. 1.
    Maltoni, D., Maio, D., Jain, A.K., Prabhakar, S.: Handbook of Fingerprint Recognition, pp. 96–99. Springer, New York (2003)MATHGoogle Scholar
  2. 2.
    Herny, E.R.: Classification and Uses of Finger Prints. George Routledge & Sons, London (1900)Google Scholar
  3. 3.
    Srinivasan, V.S., Murthy, N.N.: Detection of singular points in fingerprint images. PR 25, 139–153 (1992)Google Scholar
  4. 4.
    Ticu, M., Kuosmanen, P.: A multiresolution method for singular points detection in fingerprint images. In: Proc. 1999 IEEE ISCS, vol. 4, pp. 183–186 (1999)Google Scholar
  5. 5.
    Wang, X., Li, J., Niu, Y.: Fingerprint Classification Based on Curvature Sampling and RBF Neural Networks. In: Wang, J., Liao, X.-F., Yi, Z. (eds.) ISNN 2005. LNCS, vol. 3497, pp. 171–176. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  6. 6.
    Bazen, A.M., Gerez, S.H.: Systematic methods for the computation of the directional fields and dingular points of fingerprints. IEEE Trans. PAMI 24, 905–919 (2002)Google Scholar
  7. 7.
    Maio, D., Maltoni, D.: A structural approach to fingerprint classification. In: Proc. 13th ICPR, pp. 578–585 (1996)Google Scholar
  8. 8.
    Kawagoe, M., Tojo, A.: Fingerprint pattern classification. PR 17, 295–303 (1984)Google Scholar
  9. 9.
    Karu, K., Jain, A.K.: Fingerprint classification. PR 29, 389–404 (1996)Google Scholar
  10. 10.
    Kwak, N., Choi, C.-H.: Input feature selection by mutual information based on Parzen window. IEEE Trans. PAMI 24, 1667–1771 (2002)Google Scholar
  11. 11.
    Hong, L., Wan, Y., Jain, A.: Fingerprint image enhancement: algorithm and performance evaluation. IEEE Trans. PAMI, 20, 777–789 (1998)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Xuchu Wang
    • 1
  • Jianwei Li
    • 1
  • Yanmin Niu
    • 2
  • Weimin Chen
    • 1
  • Wei Wang
    • 1
  1. 1.Key Lab on Opto-Electronic Technique of State Education MinistryChongqing UniversityChongqingP.R. China
  2. 2.College of Physics and Information TechniquesChongqing Normal UniversityChongqingP.R.China

Personalised recommendations