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 proceedingConference proceedingpeer-review


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 publicationBiometric Authentication
Subtitle of host publicationFirst International Conference, ICBA 2004, Hong Kong, China, July 15-17, 2004. Proceedings
EditorsDavid Zhang, Anil K. Jain
PublisherSpringer Berlin Heidelberg
Number of pages7
ISBN (Electronic)9783540259480
ISBN (Print)9783540221463
Publication statusPublished - 2004
Event1st International Conference on Biometric Authentication - , Hong Kong
Duration: 15 Jul 200417 Jul 2004

Publication series

NameLecture Notes in Computer Science
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference1st International Conference on Biometric Authentication
Abbreviated titleICBA 2004
Country/TerritoryHong Kong

Scopus Subject Areas

  • Theoretical Computer Science
  • Computer Science(all)


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