A Novel PCA-Based Bayes Classifier and Face Analysis

  • Zhong Jin
  • Franck Davoine
  • Zhen Lou
  • Jingyu Yang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3832)

Abstract

The classical Bayes classifier plays an important role in the field of pattern recognition. Usually, it is not easy to use a Bayes classifier for pattern recognition problems in high dimensional spaces. This paper proposes a novel PCA-based Bayes classifier for pattern recognition problems in high dimensional spaces. Experiments for face analysis have been performed on CMU facial expression image database. It is shown that the PCA-based Bayes classifier can perform much better than the minimum distance classifier. And, with the PCA-based Bayes classifier, we can obtain a better understanding of data.

Keywords

Covariance Matrix Facial Expression Minimum Distance Linear Discriminant Analysis Conditional Distribution 
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.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Zhong Jin
    • 1
    • 2
  • Franck Davoine
    • 3
  • Zhen Lou
    • 2
  • Jingyu Yang
    • 2
  1. 1.Centre de Visió per ComputadorUniversitat Autònoma de BarcelonaBarcelonaSpain
  2. 2.Department of Computer ScienceNanjing University of Science and TechnologyNanjingPeople’s Republic of China
  3. 3.HEUDIASYC – CNRS Mixed Research UnitCompiègne University of TechnologyCompiègneFrance

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