Quality Controlled Multimodal Fusion of Biometric Experts

  • Omolara Fatukasi
  • Josef Kittler
  • Norman Poh
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4756)

Abstract

The quality of biometric samples used by multimodal biometric experts to produce matching scores has a significant impact on their fusion. We address the problem of quality controlled fusion of multiple biometric experts and focus on the fusion problem in a scenario where biometric trait quality expressed in terms of quality measures can be coarsely quantised. We develop a fusion methodology based on fixed rules that exploit the respective advantages of the sum and product rules and can be easily trained. We show in experimental studies on the XM2VTS database that the proposed method is very promising.

Keywords

Biometric authentication fixed rules multiple classifiers system multimodal fusion quality dependent fusion 

References

  1. 1.
    Kittler, J., Hatef, M., Duin, R.P.W., Matas, J.: On combining classifiers. IEEE Transactions on Pattern Analysis and Machine Intelligence 20, 226–239 (1998)CrossRefGoogle Scholar
  2. 2.
    Poh, N., Bengio, S.: A score-level fusion benchmark database for biometric authentication. In: Kanade, T., Jain, A., Ratha, N.K. (eds.) AVBPA 2005. LNCS, vol. 3546, pp. 1059–1070. Springer, Heidelberg (2005)Google Scholar
  3. 3.
    Tabassi, E., Wilson, C., Watson, C.: Fingerprint image quality: Nistir 7151. Technical report, NIST (2004)Google Scholar
  4. 4.
    Fierrez-Aguilar, J., Ortega-Garcia, J., Gonzalez-Rodriguez, J., Bigun, J.: Kernel-Based Multimodal Biometric Verification Using Quality Signals. In: Defense and Security Symposium, Workshop on Biometric Technology for Human Identification, Proc. of SPIE. vol. 5404, pp. 544–554 (2004)Google Scholar
  5. 5.
    Bigun, J., Fierrez-Aguilar, J., Ortega-Garcia, J., Gonzalez-Rodriguez, J.: Multimodal Biometric Authentication using Quality Signals in Mobile Communnications. In: 12th Int’l Conf. on Image Analysis and Processing, Mantova, pp. 2–11 (2003)Google Scholar
  6. 6.
    Kryszczuk, K., Richiardi, J., Prodanov, P., Drygajlo, A.: Error Handling in Multimodal Biometric Systems using Reliability Measures. In: Proc. 12th European Conference on Signal Processing, Antalya, Turkey (September 2005)Google Scholar
  7. 7.
    Nandakumar, K., Chen, Y., Dass, S., Jain, A.: Quality-based score level fusion in multibiometric systems. In: ICPR, Hong Kong, pp. 473–476 (2006)Google Scholar
  8. 8.
    Kryszczuk, K., Drygajlo, A.: On combining evidence for reliability estimation in face verification. In: Proc. 13th European Conference on Signal Processing, Florence, Italy (2006)Google Scholar
  9. 9.
    Fierrez-Aguilar, J., Chen, Y., Ortega-Garcia, J., Jain, A.K.: Incorporating image quality in multi-algorithm fingerprint verification. In: ICB (2006)Google Scholar
  10. 10.
    Kittler, J., Poh, N., Fatukasi, O., Messer, K., Kryszczuk, K., Richiardi, J., Drygajlo, A.: Quality dependent fusion of intramodal and multimodal biometric experts. In: Proceedings of SPIE. vol. 6539 Orlando (2007)Google Scholar
  11. 11.
    Alkoot, F.M., Kittler, J.: Improving the performance of the product fusion strategy. In: ICPR, vol. 02, pp. 164–167. IEEE Computer Society, Los Alamitos, CA, USA (2000)Google Scholar
  12. 12.
    Tax, D., van Breukelen, M., Duin, R.: Combining multiple classifiers by averaging or by multiplying? Pattern Recognition 33, 1475–1485 (2000)CrossRefGoogle Scholar
  13. 13.
    Matas, J., Hamouz, M.: K.Jonsson, 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 xm2vts database. In: Proceedings of SPIE. Pattern Recognition. vol. 6539 Orlando (2007)Google Scholar
  14. 14.
    Messer, K., Kittler, J., Short, J., Heusch, G., Cardinaux, F., Marcel, S., Rodriguez, Y., Shan, S., Su, Y., Gao, W.: Performance characterisation of face recognition algorithms and their sensitivity to severe illumination changes. In: Zhang, D., Jain, A.K. (eds.) Advances in Biometrics. LNCS, vol. 3832, pp. 1–11. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  15. 15.
    Heusch, G., Rodriguez, Y., Marcel, S.: Local binary pattern as an image preprocessing face authentication. In: Proc. FGR 2006, Washington, DC, 9–14 (2006)Google Scholar
  16. 16.
    Kittler, J., Li, Y., Matas, J.: On matching score for lda-based face verification. In: BMVC (2000)Google Scholar
  17. 17.
    Reynolds, D.A., Quatieri, T., Dunn, T.: Speaker verification using adapted guassian mixture models. In: Digital Signal Processing, pp. 19–41 (2000)Google Scholar
  18. 18.
    Cardinaux, F., Sanderson, C., Bengio, S.: User authentication via adapted statistical models of face images. In: IEEE Trans. on Signal Processing, pp. 361–373 (January 2006)Google Scholar
  19. 19.
    Gross, R., Brajovic, V.: An image preprocessing algorithm for illumination invariant face recognition. In: AVBPA 2003, 10–18 (2003)Google Scholar
  20. 20.
    Bonastre, J.F., Wils, F., Meignier, S.: ALIZE, a free toolkit for speaker recognition. In: Proc. IEEE International Conference on Speech, Acoustics and Signal Processing, Philadelphia pp. 73–740 (2005)Google Scholar
  21. 21.
    Richiardi, J., Prodanov, P., Drygajlo, A.: Speaker verification with confidence and reliability measures. In: Proc. 2006 IEEE International Conference on Speech, Acoustics and Signal Processing, Toulouse, France (May 2006)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Omolara Fatukasi
    • 1
  • Josef Kittler
    • 1
  • Norman Poh
    • 1
  1. 1.Centre for Vision, Speech and Signal Processing, University of Surrey Guildford, GU2 7XH SurreyUK

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