Face Recognition by Inverse Fisher Discriminant Features

  • Xiao-Sheng Zhuang
  • Dao-Qing Dai
  • P. C. Yuen
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3832)


For face recognition task the PCA plus LDA technique is a famous two-phrase framework to deal with high dimensional space and singular cases. In this paper, we examine the theory of this framework: (1) LDA can still fail even after PCA procedure. (2) Some small principal components that might be essential for classification are thrown away after PCA step. (3) The null space of the within-class scatter matrix S w contains discriminative information for classification. To eliminate these deficiencies of the PCA plus LDA method we thus develop a new framework by introducing an inverse Fisher criterion and adding a constrain in PCA procedure so that the singularity phenomenon will not occur. Experiment results suggest that this new approach works well.


Face Recognition Linear Discriminant Analysis Recognition Rate Principle Component Analysis Null Space 
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

  • Xiao-Sheng Zhuang
    • 1
  • Dao-Qing Dai
    • 1
  • P. C. Yuen
    • 2
  1. 1.Center for Computer Vision and Department of MathematicsSun Yat-Sen(Zhongshan)UniversityGuangzhouChina
  2. 2.Department of Computer ScienceHong Kong Baptist UniversityHong Kong

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