A novel regularized fisher discriminant method for face recognition based on subspace and rank lifting scheme

Wen Sheng Chen*, Pong Chi YUEN, Jian Huang, Jianhuang Lai, Jianliang Tang

*Corresponding author for this work

Research output: Chapter in book/report/conference proceedingConference proceedingpeer-review

1 Citation (Scopus)

Abstract

The null space N(St) of total scatter matrix St contains no useful information for pattern classification. So, discarding the null space N(St) results in dimensionality reduction without loss discriminant power. Combining this subspace technique with proposed rank lifting scheme, a new regularized Fisher discriminant (SL-RFD) method is developed to deal with the small sample size (S3) problem in face recognition. Two public available databases, namely FERET and CMU PIE databases, are exploited to evaluate the proposed algorithm. Comparing with existing LDA-based methods in solving the S3 problem, the proposed SL-RFD method gives the best performance.

Original languageEnglish
Title of host publicationAffective Computing and Intelligent Interaction - First International Conference, ACII 2005, Proceedings
PublisherSpringer Verlag
Pages152-159
Number of pages8
ISBN (Print)3540296212, 9783540296218
DOIs
Publication statusPublished - 2005
Event1st International Conference on ffective Computing and Intelligent Interaction, ACII 2005 - Beijing, China
Duration: 22 Oct 200524 Oct 2005

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume3784 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference1st International Conference on ffective Computing and Intelligent Interaction, ACII 2005
Country/TerritoryChina
CityBeijing
Period22/10/0524/10/05

Scopus Subject Areas

  • Theoretical Computer Science
  • Computer Science(all)

User-Defined Keywords

  • Face recognition
  • Linear discriminant analysis
  • Null space
  • Regularized method
  • Small sample size problem

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