An optimal subspace analysis for face recognition

Haitao Zhao*, Pong Chi YUEN, Jingyu Yang

*Corresponding author for this work

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

Abstract

Fisher Linear Discriminant Analysis (LDA) has recently been successfully used as a data discriminantion technique. However, LDA-based face recognition algorithms suffer from a small sample size (S3) problem. It results in the singularity of the within-class scatter matrix Sw. To overcome this limitation, 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.

Original languageEnglish
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
EditorsDavid Zhang, Anil K. Jain
PublisherSpringer Verlag
Pages95-101
Number of pages7
ISBN (Print)3540221468, 9783540221463
DOIs
Publication statusPublished - 2004

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume3072
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Scopus Subject Areas

  • Theoretical Computer Science
  • Computer Science(all)

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