Gabor Feature Based Classification Using LDA/QZ Algorithm for Face Recognition

  • Weihong Deng
  • Jiani Hu
  • Jun Guo
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4221)


This paper proposes a LDA/QZ algorithm and its combination of Gabor Filter-based features for the face recognition. The LDA/QZ algorithm follows the common “PCA+LDA” framework, but it has two significant virtues compared with previous algorithms: 1) In PCA step, LDA/QZ transforms the feature space into complete PCA space, so that all discriminatory information is preserved, and 2) In LDA step, the QZ-decomposition is applied to solve the generalized eigenvalue problem, so that LDA can be performed stably even when within-class scatter matrix is singular. Moreover, the Gabor Filter-based Features and the new LDA/QZ algorithm are combined for face recognition. We also performed comparative experimental studies of several state-of-art dimension reduction algorithms and their combinations of Gabor feature for face recognition. The evaluation is based on six experiments involving various types of face images from ORL, FERET, and AR database and experimental results show the LDA/QZ algorithm is always the best or comparable to the best in term of recognition accuracy.


Face Recognition Linear Discriminant Analysis Face Image Generalize Eigenvalue Problem Discriminatory Information 
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 2006

Authors and Affiliations

  • Weihong Deng
    • 1
  • Jiani Hu
    • 1
  • Jun Guo
    • 1
  1. 1.Beijing University of Posts and TelecommunicationsBeijingChina

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