Fake Finger Detection Based on Thin-Plate Spline Distortion Model

  • Yangyang Zhang
  • Jie Tian
  • Xinjian Chen
  • Xin Yang
  • Peng Shi
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4642)

Abstract

This paper introduces a novel method based on the elasticity analysis of the finger skin to discriminate fake fingers from real ones. We match the fingerprints before and after special distortion and gained their corresponding minutiae pairs as landmarks. The thin-plate spline (TPS) model is used to globally describe the finger distortion. For an input finger, we compute the bending energy vector by the TPS model and calculate the similarity of the bending energy vector to the bending energy fuzzy feature set. The similarity score is in the range [0, 1], indicating how much the current finger is similar to the real finger. The method realizes fake finger detection based on the normal steps of fingerprint processing without special hardware, so it is easily implemented and efficient. The experimental results on a database of real and fake fingers show that the performance of the method is available.

Keywords

fake finger distortion Thin-plate Spline model bending energy vector fuzzy feature set 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Derakhshani, R., Schuckers, S.A.C., Hornak, L.A., O’Gorman, L.: Determination of vitality from a non-invasive biomedical measurement for use in fingerprint scanners. Pattern Recognition 36(2), 383–396 (2003)CrossRefGoogle Scholar
  2. 2.
    Antonelli, A., Cappelli, R., Maio, D., Maltoni, D.: A new approach to fake finger detection based on skin distortion. In: Zhang, D., Jain, A.K. (eds.) Advances in Biometrics. LNCS, vol. 3832, pp. 221–228. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  3. 3.
    Hong, L., Wan, Y., Jain, A.K.: Fingerprint image enhancement: algorithms and performance evaluation. IEEE Trans. Pattern Analysis Machine Intelligence 20(8), 777–789 (1998)CrossRefGoogle Scholar
  4. 4.
    Chen, X.J., Tian, J., Yang, X.: An algorithm for distorted fingerprint matching based on local triangle features set. IEEE Trans. on Information, Forensics and Security 1(2) (2006)Google Scholar
  5. 5.
    Bazen, A.M., Gerez, S.H.: Fingerprint matching by thin-plate spline modelling of elastic deformations. Pattern Recognition 36(8), 1859–1867 (2003)CrossRefGoogle Scholar
  6. 6.
    Ross, A., Dass, S., Jain, A.K.: A deformable model for fingerprint matching. Pattern Recognition 38(1), 95–103 (2005)CrossRefGoogle Scholar
  7. 7.
    Hoppner, F., Klawonn, F., Kruse, R., Runkler, T.: Fuzzy cluster analysis: methods for classification, Data Analysis and Image Recognition. John Wiley & Sons, Chichester (1999)Google Scholar
  8. 8.
    Bookstein, F.L.: Principal warps: Thin-plate splines and the decomposition of deformations. IEEE Transactions on Pattern Analysis and Machine Intelligence 2(6), 567–585 (1989)CrossRefGoogle Scholar
  9. 9.
    Zhu, E., Yin, J.P., Zhang, G.M.: Fingerprint matching based on global alignment of multiple reference minutiae. Pattern Recognition 38(10), 1685–1694 (2005)CrossRefGoogle Scholar
  10. 10.
    Matsumoto, T.H., Yamada, K., Hoshino, S.: Impact of Artificial ’Gummy’ Fingers on. Fingerprint Systems. In: Proceedings of SPIE, vol. 4677 (2002)Google Scholar
  11. 11.
    Rohr, K., Fornefett, M., Stiehl, H.S.: Approximating Thin-Plate Splines for Elastic Registration: Integration of Landmark Errors and Orientation Attributes. In: Proceedings of the 16th International Conference on Information Processing in Medical Imaging, pp. 252–265 (1999)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Yangyang Zhang
    • 1
  • Jie Tian
    • 1
  • Xinjian Chen
    • 1
  • Xin Yang
    • 1
  • Peng Shi
    • 1
  1. 1.Center for Biometrics and Security Research, Key Laboratory of Complex Systems, and Intelligence Science, Institute of Automation, Chinese Academy of Sciences, Graduate School of the Chinese Academy of Sciences, P.O. Box 2728 Beijing 100080, Email:tian@ieee.orgChina

Personalised recommendations