Wavelet-based 2-parameter regularized discriminant analysis for face recognition

Dao Qing Dai*, Pong Chi YUEN

*Corresponding author for this work

Research output: Chapter in book/report/conference proceedingChapterpeer-review

7 Citations (Scopus)

Abstract

This paper addresses the small-size problem in Fisher Discriminant Analysis. We propose to use wavelet transform for preliminary dimensionality reduction and use a two-parameter regularization scheme for the within-class scatter matrix. The novelty of the proposed method comes from: (1) Wavelet transform with linear computation complexity is used to carry out the preliminary dimensionality reduction instead of employing a principal component analysis. The wavelet filtering also acts as smoothing out noise. (2) An optimal solution is found in the full space instead of a sub-optimal solution in a restricted subspace. (3) Detailed analysis for the contribution of the eigenvectors of the within-class scatter matrix to the overall classification performance is carried out. (4) An enhanced algorithm is developed and applied to face recognition. The recognition accuracy (rank 1) for the Olivetti database using only three images of each person as training set is 96.7859%. The experimental results show that the proposed algorithm could further improve the recognition performance.

Original languageEnglish
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
EditorsJosef Kittler, Mark S. Nixon
PublisherSpringer Verlag
Pages137-144
Number of pages8
ISBN (Electronic)9783540403029
DOIs
Publication statusPublished - 2003

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume2688
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Scopus Subject Areas

  • Theoretical Computer Science
  • Computer Science(all)

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