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Nonparametric Fingerprint Deformation Modelling

  • Neil Yager
  • Adnan Amin
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3691)

Abstract

This paper presents a novel approach to modelling fingerprint deformations. When fingerprints are captured, they undergo a certain amount of distortion due to the application of a three dimensional elastic tissue against a flat surface. This poses a challenge for automated fingerprint verification systems, which are generally based on aligning two fingerprints and comparing their respective features. There are only a few methods reported in the literature for modelling fingerprint distortions. One prominent method is based on using minutiae correspondences as landmarks, and creating a deformation model using thin-plate splines. There are several disadvantages to this approach, and a nonparametric elastic modelling algorithm is developed in this paper to address these issues. Both algorithms have been implemented and are evaluated by incorporating them into a fingerprint verification system. The results show an improvement of the proposed algorithm over the existing method of deformation modelling.

Keywords

Query Image Deformation Modelling Equal Error Rate Distortion Modelling Fingerprint Match 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 1.
    Jain, A., Hong, L., Pankanti, S.: Biometric identification. Comm. ACM 43, 90–98 (2000)CrossRefGoogle Scholar
  2. 2.
    Cappelli, R., Maio, D., Maltoni, D.: Modelling plastic distortion in fingerprint images. In: Proceedings of the Second International Conference on Advances in Pattern Recognition, Rio De Janeiro, Brazil, pp. 369–376 (2001)Google Scholar
  3. 3.
    Senior, A., Bolle, R.: Improved fingerprint matching by distortion removal. IEICE Transactions on Information and Systems E84-D, 825–831 (2001)Google Scholar
  4. 4.
    Bookstein, F.: Principal warps: thin-plate splines and the decomposition of deformations. IEEE Trans. Pattern Analysis and Machine Intelligence 11, 567–585 (1989)zbMATHCrossRefGoogle Scholar
  5. 5.
    Almansa, A., Cohen, L.: Fingerprint matching by minimization of a thin-plate energy using a two-step algorithm with auxiliary variables. In: Proceedings of the Fifth IEEE Workshop on Applications of Computer Vision, Palm Springs, USA, pp. 35–40 (2000)Google Scholar
  6. 6.
    Bazen, A., Gerez, S.: Fingerprint matching by thin-plate spline modelling deformations. Pattern Recognition 36, 1859–1867 (2003)CrossRefGoogle Scholar
  7. 7.
    Ross, A., Dass, S., Jain, A.: A deformable model for fingerprint matching. Pattern Recognition 38, 95–103 (2005)CrossRefGoogle Scholar
  8. 8.
    Yager, N., Amin, A.: Fingerprint verification using two stage optimization. Pattern Recognition Letters (In Press)Google Scholar
  9. 9.
    Burr, D.J.: A dynamic model for image registration. Computer Graphics and Image Processing 15, 102–112 (1981)CrossRefGoogle Scholar
  10. 10.
    FVC2002: Second International Fingerprint Verification Competition, http://bias.csr.unibo.it/fvc2002/
  11. 11.
    Yager, N., Amin, A.: A novel verification criterion for distortion-free fingerprints. In: Gagalowicz, A., Philips, W. (eds.) CAIP 2005. LNCS, vol. 3691, pp. 65–72. Springer, Heidelberg (2005)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Neil Yager
    • 1
  • Adnan Amin
    • 1
  1. 1.School of Computer Science and EngineeringUniversity of New South WalesSydneyAustralia

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