Wavelet based discriminant analysis for face recognition

Dao Qing Dai*, Pong Chi YUEN

*Corresponding author for this work

Research output: Contribution to journalJournal articlepeer-review

28 Citations (Scopus)


The linear (Fisher) discriminant analysis (LDA) is a well-known and popular statistical method in pattern recognition and classification. When applied to face recognition problem the small sample size problem occurs. We investigate the nature of this phenomenon and use wavelet transform for dimension reduction. Moreover we propose a regularized scheme based face recognition system. Comparisons are made with the Tikhonov regularization method and the infinity Fisher index method. We find out that when the small sample size problem appears optimizing the Fisher index does not lead to good results.

Original languageEnglish
Pages (from-to)307-318
Number of pages12
JournalApplied Mathematics and Computation
Issue number1
Publication statusPublished - 1 Apr 2006

Scopus Subject Areas

  • Computational Mathematics
  • Applied Mathematics

User-Defined Keywords

  • Face recognition
  • Linear discriminant analysis
  • Regularization
  • Singular value decomposition
  • Small sample size problem
  • Wavelet transform


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