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Incorporating Image Quality in Multi-algorithm Fingerprint Verification

  • Julian Fierrez-Aguilar
  • Yi Chen
  • Javier Ortega-Garcia
  • Anil K. Jain
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3832)

Abstract

The effect of image quality on the performance of fingerprint verification is studied. In particular, we investigate the performance of two fingerprint matchers based on minutiae and ridge information as well as their score-level combination under varying fingerprint image quality. The ridge-based system is found to be more robust to image quality degradation than the minutiae-based system. We exploit this fact by introducing an adaptive score fusion scheme based on automatic quality estimation in the spatial frequency domain. The proposed scheme leads to enhanced performance over a wide range of fingerprint image quality.

Keywords

Image Quality Spatial Frequency Domain Fingerprint Verification Image Quality Degradation Increase Image Quality 
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.

References

  1. 1.
    Jain, A.K., Ross, A., Prabhakar, S.: An introduction to biometric recognition. IEEE Trans. on Circuits and Systems for Video Technology 14, 4–20 (2004)CrossRefGoogle Scholar
  2. 2.
    Maltoni, D., Maio, D., Jain, A.K., Prabhakar, S.: Handbook of Fingerprint Recognition. Springer, Heidelberg (2003)MATHGoogle Scholar
  3. 3.
    Simon-Zorita, D., et al.: Image quality and position variability assessment in minutiae-based fingerprint verification. IEE Proc. VISP 150, 402–408 (2003)CrossRefGoogle Scholar
  4. 4.
    Maio, D., Maltoni, D., et al.: FVC2004: Third Fingerprint Verification Competition. In: Zhang, D., Jain, A.K. (eds.) ICBA 2004. LNCS, vol. 3072, pp. 1–7. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  5. 5.
    Wilson, C., et al.: FpVTE2003: Fingerprint Vendor Technology Evaluation 2003 (NISTIR 7123), http://fpvte.nist.gov/
  6. 6.
    Jain, A.K., Ross, A.: Multibiometric systems. Communications of the ACM 47, 34–40 (2004)CrossRefGoogle Scholar
  7. 7.
    Ross, A., Jain, A.K., Reisman, J.: A hybrid fingerprint matcher. Pattern Recognition 36, 1661–1673 (2003)CrossRefGoogle Scholar
  8. 8.
    Fierrez-Aguilar, J., et al.: Combining multiple matchers for fingerprint verification: A case study in FVC 2004. In: Roli, F., Vitulano, S. (eds.) ICIAP 2005. LNCS, vol. 3617, pp. 1035–1042. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  9. 9.
    Fierrez-Aguilar, J., Ortega-Garcia, J., et al.: Discriminative multimodal biometric authentication based on quality measures. Pattern Recognition 38, 777–779 (2005)CrossRefGoogle Scholar
  10. 10.
    Hong, L., Wang, Y., Jain, A.K.: Fingerprint image enhancement: algorithm and performance evaluation. IEEE Trans. on PAMI 20, 777–789 (1998)Google Scholar
  11. 11.
    Yau, W.Y., Chen, T.P., Morguet, P.: Benchmarking of fingerprint sensors. In: Maltoni, D., Jain, A.K. (eds.) BioAW 2004. LNCS, vol. 3087, pp. 89–99. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  12. 12.
    Bigun, J., et al.: Multidimensional orientation estimation with applications to texture analysis and optical flow. IEEE Trans. on PAMI 13, 775–790 (1991)Google Scholar
  13. 13.
    Shen, L., Kot, A., Koo, W.: Quality measures for fingerprint images. In: Bigun, J., Smeraldi, F. (eds.) AVBPA 2001. LNCS, vol. 2091, pp. 266–271. Springer, Heidelberg (2001)CrossRefGoogle Scholar
  14. 14.
    Lim, E., Jiang, X., Yau, W.: Fingerprint quality and validity analysis. Proc. ICIP, 469–472 (2002)Google Scholar
  15. 15.
    Ratha, N., Bolle, R. (eds.): Automatic Fingerprint Recognition Systems. Springer, Heidelberg (2004)Google Scholar
  16. 16.
    Tabassi, E., Wilson, C., Watson, C.: Fingerprint image quality. NIST Research Report NISTIR 7151 (August 2004)Google Scholar
  17. 17.
    Chen, Y., Dass, S., Jain, A.: Fingerprint quality indices for predicting authentication performance. In: Kanade, T., Jain, A., Ratha, N.K. (eds.) AVBPA 2005. LNCS, vol. 3546, pp. 160–170. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  18. 18.
    Jain, A.K., Hong, L., Pankanti, S., Bolle, R.: An identity authentication system using fingerprints. Proceedings of the IEEE 85, 1365–1388 (1997)CrossRefGoogle Scholar
  19. 19.
    Ross, A., Reisman, J., Jain, A.K.: Fingerprint matching using feature space correlation. In: Tistarelli, M., Bigun, J., Jain, A.K. (eds.) ECCV 2002. LNCS, vol. 2359, pp. 48–57. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  20. 20.
    Bigun, E.S., et al.: Expert conciliation for multimodal person authentication systems by Bayesian statistics. In: Bigün, J., Borgefors, G., Chollet, G. (eds.) AVBPA 1997. LNCS, vol. 1206, pp. 291–300. Springer, Heidelberg (1997)CrossRefGoogle Scholar
  21. 21.
    Kittler, J., Hatef, M., Duin, R., Matas, J.: On combining classifiers. IEEE Trans. on PAMI 20, 226–239 (1998)Google Scholar
  22. 22.
    Jain, A., Nandakumar, K., Ross, A.: Score normalization in multimodal biometric systems (2005) (to appear) Google Scholar
  23. 23.
    Ross, A., Jain, A.: Information fusion in biometrics. Pattern Recognition Letters 24, 2115–2125 (2003)CrossRefGoogle Scholar
  24. 24.
    Ortega-Garcia, J., Fierrez-Aguilar, J., et al.: MCYT baseline corpus: A bimodal biometric database. IEE Proc. VISP 150, 395–401 (2003)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Julian Fierrez-Aguilar
    • 1
  • Yi Chen
    • 2
  • Javier Ortega-Garcia
    • 1
  • Anil K. Jain
    • 2
  1. 1.ATVS, Escuela Politecnica SuperiorUniversidad Autonoma de MadridMadridSpain
  2. 2.Department of Computer Science and EngineeringMichigan State UniversityEast LansingUSA

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