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KDA Plus KPCA for Face Recognition

  • Wenming Zheng
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3972)

Abstract

Kernel discriminant analysis (KDA) and the kernel principal component analysis (KPCA), which are the extension of the linear discriminant analysis (LDA) and the principal component analysis (PCA), respectively, from linear domain to nonlinear domain via the kernel trick, are two very popular nonlinear feature extraction methods. In this paper, we present a new feature extraction algorithm by combing KDA and KPCA, and then apply it to the face recognition task. The experimental results on Yale face dataset show that the proposed method can significantly improve the performance both KDA and KPCA.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Wenming Zheng
    • 1
  1. 1.Research Center for Science of LearningSoutheast UniversityNanjingChina

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