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

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Pattern Recognition and Image Analysis (ICAPR 2005)

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

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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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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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