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International Conference on Pattern Recognition and Image Analysis

ICAPR 2005: Pattern Recognition and Image Analysis pp 103–111Cite as

View Independent Video-Based Face Recognition Using Posterior Probability in Kernel Fisher Discriminant Space

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Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 3687))

Abstract

This paper presents a view independent video-based face recognition method using posterior probability in Kernel Fisher Discriminant (KFD) space. In practical environment, the view of faces changes dynamically. The robustness to view changes is required for video-based face recognition in practical environment. Since the view changes induces large non-linear variation, kernel-based methods are appropriate. We use KFD analysis to cope with non-linear variation. To classify image sequence, the posterior probability in KFD space is used. KFD analysis assumes that the distribution of each class in high dimensional space is Gaussian. This makes the computation of posterior probability in KFD space easy. The effectiveness of the proposed method is shown by the comparison with the other feature spaces and classification methods.

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References

  1. Chellappa, R., Wilson, C.L., Sirohey, S.: Human and machine recognition of faces: A survey. Proceedings of the IEEE 83(5), 705–740 (1995)

    Article  Google Scholar 

  2. Zhao, W., Chellappa, R., Phillips, P.J., Rosenfeld, A.: Face recognition: A literature survey. ACM Computing Surveys 35(4), 399–458 (2003)

    Article  Google Scholar 

  3. Zhou, S., Krueger, V., Chellappa, R.: Probabilistic recognition of human faces from video. Computer Vision and Image Understanding 91, 214–245 (2003)

    Article  Google Scholar 

  4. Lee, K.-C., Ho, J., Yang, M.-H., Kriegman, D.: Video-based face recognition using probabilistic appearance manifolds. In: Proc. IEEE Computer Society Conerence on Computer Vision and Pattern Recognition, pp. 313–320 (2003)

    Google Scholar 

  5. Liu, X., Chen, T.: Video-based face recognition using adaptive hidden markov models. In: Proc. IEEE Computer Society Conerence on Computer Vision and Pattern Recognition, pp. 340–345 (2003)

    Google Scholar 

  6. Hadid, A., Pietikäinen, M.: From still image to vide-based face recognition: An experimental aanlysis. In: Proc. Sixth IEEE International Conference on Automatic Face and Gesture Recognition, pp. 813–818 (2004)

    Google Scholar 

  7. Aggarwal, G., Chowdhury, A.K.R., Chellappa, R.: A system identification approach for video-based face recognition. In: Proc. 17th International Conference on Pattern Recognition, pp. 175–178 (2004)

    Google Scholar 

  8. Murase, H., Nayar, S.K.: Visual learning and recognition of 3d objects from appearance. International Journal of Computer Vision 14(1), 5–24 (1995)

    Article  Google Scholar 

  9. Kurita, T., Takahashi, T.: Viewpoint independent face recognition by competition of viewpoint dependent classifiers. Neurocomputing 51, 181–195 (2003)

    Article  Google Scholar 

  10. Müller, K.-R., Mika, S., Rätsch, G., Tsuda, K., Schölkopf, B.: An introduction to kernel-based learning algorithms. IEEE Trans. Neural Networks 12(2), 181–201 (2001)

    Article  Google Scholar 

  11. Shawe-Taylor, J., Cristianini, N.: Kernel Methods for Pattern Analysis. Cambridge University Press, Cambridge (2004)

    Google Scholar 

  12. Yang, M.-H.: Face recognition using kernel methods. Advances in Neural Information Processing Systems 14, 215–220 (2002)

    Google Scholar 

  13. Mika, S., Rätsch, G., Weston, J., Schölkopf, B., Müller, K.-R.: Fisher discriminant analysis with kernels. In: Proc. IEEE International Workshop on Neural Networks for Signal Processing, pp. 41–48 (1999)

    Google Scholar 

  14. Kurita, T., Taguchi, T.: A modification of kernel-based fisher discriminant analysis for face detection. In: Proc. fifth IEEE International Conference on Automatic Face and Gesture Recognition, pp. 300–305 (2002)

    Google Scholar 

  15. Feng, Y., Shi, P.: Face detection based on kernel fisher discriminant analysis. In: Proc. Sixth IEEE International Conference on Automatic Face and Gesture Recognition, pp. 381–384 (2004)

    Google Scholar 

  16. HOIP face database, http://www.hoip.jp/web_catalog/top.html

  17. Graham, D.B., Allinson, N.M.: Characterizing virtual eigensignatures for general purpose face recognition. In: Wechsler, H., Pillips, P.J., Bruce, V., Fogelman-Soulie, F., Huang, T.S. (eds.) Face Recognition: From Theory to Applications. NATO ASI Series F, Computer and Systems Science, vol. 163, pp. 446–456 (1998)

    Google Scholar 

  18. Debnath, R., Takahashi, H.: Kernel selection for the support vector machine. IEICE Trans. Info. & Syst. E87-D12, 2903–2904 (2004)

    Google Scholar 

  19. Rowley, H.A., Baluja, S., Kanade, T.: Neural network-based face detection. IEEE Trans. Pattern Analysis and Machine Intelligence 20(1), 23–38 (1998)

    Article  Google Scholar 

  20. Hotta, K.: Support vector machine with local summation kernel for robust face recognition. In: Proc. 17th International Conference on Pattern Recognition, pp. 482–485 (2004)

    Google Scholar 

  21. Hotta, K.: A robust face detector under partial occlusion. In: Proc. IEEE International Conference on Image Processing, pp. 597–600 (2004)

    Google Scholar 

  22. Yang, J., Frangi, A.F., Yang, J.-Y.: A new kernel fisher discriminant algorithm with application to face recogntion. Neurocomputing 56(1), 415–421 (2004)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. The University of Electro-Communications, 1-5-1 Chofugaoka,Chofu-shi, Tokyo, 182-8585, Japan

    Kazuhiro Hotta

Authors
  1. Kazuhiro Hotta
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Editor information

Editors and Affiliations

  1. Research School of Infomatics, Loughborough, UK

    Sameer Singh

  2. ATR Lab, Research School of Informatics, University of Loughborough, Loughborough, UK

    Maneesha Singh

  3. IBM Corporation, 1133 Wetchester Avenue, White Plains, 10604, New York, United States

    Chid Apte

  4. Institute of Computer Vision and applied Computer Sciences, IBaI, Germany

    Petra Perner

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© 2005 Springer-Verlag Berlin Heidelberg

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Hotta, K. (2005). View Independent Video-Based Face Recognition Using Posterior Probability in Kernel Fisher Discriminant Space. In: Singh, S., Singh, M., Apte, C., Perner, P. (eds) Pattern Recognition and Image Analysis. ICAPR 2005. Lecture Notes in Computer Science, vol 3687. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11552499_12

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  • DOI: https://doi.org/10.1007/11552499_12

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-28833-6

  • Online ISBN: 978-3-540-31999-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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