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Regularization of LDA for Face Recognition: A Post-processing Approach

  • Wangmeng Zuo
  • Kuanquan Wang
  • David Zhang
  • Jian Yang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3723)

Abstract

When applied to high-dimensional classification task such as face recognition, linear discriminant analysis (LDA) can extract two kinds of discriminant vectors, those in the null space (irregular) and those in the range space (regular) of the within-class scatter matrix. Recently, regularization techniques, which alleviate the over-fitting to the training set, have been used to further improve the recognition performance of LDA. Most current regularization techniques, however, are pre-processing approaches and can’t be used to regularize irregular discriminant vectors. This paper proposes a post-processing method, 2D-Gaussian filtering, for regularizing both regular and irregular discriminant vectors. This method can also be combined with other regularization techniques. We present two LDA methods, regularization of subspace LDA (RSLD) and regularization of complete Fisher discriminant framework (RCFD) and test them on the FERET face database. Post-processing is shown to improve the recognition accuracy in face recognition.

Keywords

Face Recognition Linear Discriminant Analysis Recognition Rate Regularization Technique Average Recognition Rate 
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

  • Wangmeng Zuo
    • 1
  • Kuanquan Wang
    • 1
  • David Zhang
    • 2
  • Jian Yang
    • 2
  1. 1.School of Computer Science and TechnologyHarbin Institute of TechnologyHarbinChina
  2. 2.Department of ComputingThe Hong Kong Polytechnic UniversityKowloon, Hong Kong

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