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Effective Implementation of Linear Discriminant Analysis for Face Recognition and Verification

  • Yongping Li
  • Josef Kittler
  • Jiri Matas
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1689)

Abstract

The algorithmic techniques for the implementation of the Linear Discriminant Analysis (LDA) play an important role when the LDA is applied to the high dimensional pattern recognition problem such as face recognition or verification. The LDA implementation in the context of face recognition and verification is investigated in this paper. Three main algorithmic techniques: matrix transformation, the Cholesky factorisation and QR algorithm, the Kronecker canonical form and QZ algorithm are proposed and tested on four publicly available face databases (M2VTS, YALE, XM2FDB, HARVARD)1. Extensive experimental results support the conclusion that the implementation based on the Kronecker canonical form and the QZ algorithm accomplishes the best performance in all experiments

Keywords

Face Recognition Linear Discriminant Analysis Face Image Face Database Cholesky Factorisation 
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 1999

Authors and Affiliations

  • Yongping Li
    • 1
  • Josef Kittler
    • 1
  • Jiri Matas
    • 1
  1. 1.Centre for Vision Speech and Signal ProcessingUniversity of SurreyGuildford, SurreyEngland

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