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A Novel Method for Multibiometric Fusion Based on FAR and FRR

  • Yong Li
  • Jianping Yin
  • Jun Long
  • En Zhu
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5861)

Abstract

Based on the fusion of multiple biometric sources, Multibiometric systems can be expected to be more accurate due to the presence of multiple pieces of evidence. Multibiometric system design is a challenging problem because it is very difficult to choose the optimal fusion strategy. Score level fusion is the most commonly used approach in Multibiometric systems. The distribution of genuine and imposter scores are very important for score fusion of Multibiometric systems. FRR (False Reject Rate) and FAR (False Accept Rate) are two key parameters to cultivate the distribution of genuine and imposter scores. In this paper, we first present a model for Multibiometric fusion and then proposed a novel approach for score level fusion which is based on FAR and FRR. By this method, the match scores first are transformed into LL1s and then the sum rule is used to combine the LL1s of the scores. The experimental results show that the new fusion scheme is efficient for different Multibiometric systems.

Keywords

biometrics Multibiometric score level fusion multi-modal FRR FAR 

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References

  1. 1.
    Jain, A.K.: Biometric Recognition: Overview and Recent Advances. In: Rueda, L., Mery, D., Kittler, J. (eds.) CIARP 2007. LNCS, vol. 4756, pp. 13–19. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  2. 2.
    Ross, A., Nandakumar, D., Jain, A.K.: Handbook of Multibiometrics. Springer, Heidelberg (2006)Google Scholar
  3. 3.
    Silverman, B.W.: Density Estimation for Statistics and Data Analysis. Chapman & Hall, Boca Raton (1986)MATHGoogle Scholar
  4. 4.
    Wand, M.P., Jones, M.C.: Kernel Smoothing. Chapman & Hall, CRC Press (1995)Google Scholar
  5. 5.
    Prabhakar, S., Jain, A.K.: Decision-level Fusion in Fingerprint Verification. Pattern Recognition 35(4), 861–874 (2002)CrossRefMATHGoogle Scholar
  6. 6.
    Griffin, P.: Optimal Biometric Fusion for Identity Verification. Technical Report RDNJ-03-0064, Identix Corporate Research Center (2004)Google Scholar
  7. 7.
    Nandakumar, K., Chen, Y., Dass, S.C., Jain, A.K.: Likelihood Ratio Based Biometric Score Fusion. IEEE Trans. on PAMI 30(2), 342–347 (2008)Google Scholar
  8. 8.
    Verlinde, P., Druyts, P., Cholet, G., Acheroy, M.: Applying Bayes based Classifiers for Decision Fusion in a Multi-modal Identity Verification System. In: Proceedings of International Symposium on Pattern Recognition In Memoriam Pierre Devijver, Brussels, Belgium (1999)Google Scholar
  9. 9.
    Kittler, J., Hatef, M., Duin, R.P.W., Matas, J.: On combining classifiers. IEEE Trans. on Pattern Anal. Machine Intell. 20(3), 226–239 (1998)CrossRefGoogle Scholar
  10. 10.
    Alkoot, F.M., Kittler, J.: Improving the performance of the product fusion strategy. In: ICPR, vol. 2, pp. 164–167. IEEE Computer Society, Los Alamitos (2000)Google Scholar
  11. 11.
    Tax, D., Breukelen, M., Duin, R.: Combining multiple classifiers by averaging or by multiplying. Pattern Recognition 33, 1475–1485 (2000)CrossRefGoogle Scholar
  12. 12.
    Matas, J., Hamouz, M., Jonsson, K., Kittler, J., Li, Y., Kotropoulos, C., Tefas, A., Pitas, I., Tan, T., Yan, H., Smeraldi, F., Begun, J., Capdevielle, N., Gerstner, W., Ben-Yacoub, S., Abdeljaoued, Y., Mayoraz, E.: Comparison of Face Verification Results on the XM2VTS Database. In: Proc.15th Int’l. Conf. Pattern Recognition, Barcelona, vol. 4, pp. 858–863 (2000)Google Scholar
  13. 13.
    Poh, N., Bengio, S.: Database, Protocol and Tools for Evaluating Score-Level Fusion Algorithms in Biometric Authentication. Pattern Recognition 39(2), 223–233 (2006)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Yong Li
    • 1
  • Jianping Yin
    • 1
  • Jun Long
    • 1
  • En Zhu
    • 1
  1. 1.School of ComputerNational University of Defense TechnologyChangshaChina

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