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A Novel Null Space-Based Kernel Discriminant Analysis for Face Recognition

  • Tuo Zhao
  • Zhizheng Liang
  • David Zhang
  • Yahui Liu
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4642)

Abstract

The symmetrical decomposition is a powerful method to extract features for image recognition. It reveals the significant discriminative information from the mirror image of symmetrical objects. In this paper, a novel null space kernel discriminant method based on the symmetrical method with a weighted fusion strategy is proposed for face recognition. It can effectively enhance the recognition performance and shares the advantages of Null-space, kernel and symmetrical methods. The experiment results on ORL database and FERET database demonstrate that the proposed method is effective and outperforms some existing subspace methods.

Keywords

symmetrical decomposition symmetrical null-space based kernel LDA weighted fusion strategy face recognition 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Tuo Zhao
    • 1
  • Zhizheng Liang
    • 1
  • David Zhang
    • 2
  • Yahui Liu
    • 1
  1. 1.Harbin Institute of Technology 
  2. 2.Hongkong Polytechnic University 

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