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Optimal Decision Fusion for a Face Verification System

  • Qian Tao
  • Raymond Veldhuis
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4642)

Abstract

Fusion is a popular practice to increase the reliability of the biometric verification. In this paper, optimal fusion at decision level by AND rule and OR rule is investigated. Both a theoretical analysis and the experimental results are given. Comparisons are presented between fusion at decision level and fusion at matching score level. For our face verification system, decision fusion proves to be a simple, practical, and effective approach, which significantly improves the performance of the original classifier.

Keywords

Face Image Local Binary Pattern Operation Point Decision Level Equal Error Rate 
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.
    Heusch, G., Rodriguez, Y., Marcel, S.: Local binary patterns as image preprocessing for face authentication. In: IEEE International Conference on Automatic Face and Gesture Recognition, IEEE Computer Society Press, Los Alamitos (2006)Google Scholar
  2. 2.
    Kittler, J., Hatef, M., Duin, R., Matas, J.: On combining classifiers. IEEE Transactions on Pattern Analysis and Machine Intelligence 20(3), 226–239 (1998)CrossRefGoogle Scholar
  3. 3.
    Kittler, J., Li, Y., Matas, J., Sanchez, M.: Combining evidence in multimodal personal identity recognition systems. In: Bigün, J., Borgefors, G., Chollet, G. (eds.) AVBPA 1997. LNCS, vol. 1206, Springer, Heidelberg (1997)Google Scholar
  4. 4.
    Ojala, T., Pietikainen, M., Maenpaa, T.: Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Transactions on Pattern Analysis and Machine Intelligence 24(7), 971–987 (2004)CrossRefGoogle Scholar
  5. 5.
    Ross, A., Jain, A.: Information fusion in biometrics 24(13) (2003)Google Scholar
  6. 6.
    Ross, A., Nandakumar, K., Jain, A.: Handbook of Multibiomtrics. Springer, Heidelberg (2006)Google Scholar
  7. 7.
    Tao, Q., Veldhuis, R.: Biometric authentication for mobile personal device. In: First International Workshop on Personalized Networks, San Jose, USA (2006)Google Scholar
  8. 8.
    Tao, Q., Veldhuis, R.: Verifying a user in a personal face space. In: 9th Int. Conf. Control, Automation, Robotics, and Vision, Singapore (2006)Google Scholar
  9. 9.
    van Trees, H.L.: Detectioin, Estimation, and Modulation Theory. John Wiley and Sons, New York (1969)Google Scholar
  10. 10.
    Viola, P., Jones, M.: Robust real-time face detection. International Journal of Computer Vision 57(2), 137–154 (2004)CrossRefGoogle Scholar
  11. 11.
    Zhang, W., Chang, Y., Chen, T.: Optimal thresholding for key generation based on biometrics. In: International Conference on Image Processing (2004)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Qian Tao
    • 1
  • Raymond Veldhuis
    • 1
  1. 1.Signals and Systems Group, Faculty of EEMCS, University of TwenteThe Netherlands

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