Optimal subspace analysis for face recognition

Haitao Zhao*, Pong Chi YUEN, Jingyu Yang

*Corresponding author for this work

Research output: Contribution to journalJournal articlepeer-review

9 Citations (Scopus)


Fisher Linear Discriminant Analysis (LDA) has been successfully used as a data discriminantion technique for face recognition. This paper has developed a novel subspace approach in determining the optimal projection. This algorithm effectively solves the small sample size problem and eliminates the possibility of losing discriminative information. Through the theoretical derivation, we compared our method with the typical PCA-based LDA methods, and also showed the relationship between our new method and perturbation-based method. The feasibility of the new algorithm has been demonstrated by comprehensive evaluation and comparison experiments with existing LDA-based methods.

Original languageEnglish
Pages (from-to)375-393
Number of pages19
JournalInternational Journal of Pattern Recognition and Artificial Intelligence
Issue number3
Publication statusPublished - May 2005

Scopus Subject Areas

  • Software
  • Computer Vision and Pattern Recognition
  • Artificial Intelligence

User-Defined Keywords

  • Face recognition
  • Linear discriminant analysis
  • Small sample size problem
  • Subspace analysis


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