Face recognition by inverse fisher discriminant features

Xiao Sheng Zhuang, Dao Qing Dai*, Pong Chi YUEN

*Corresponding author for this work

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

Abstract

For face recognition task the PCA plus LDA technique is a famous two-phrase framework to deal with high dimensional space and singular cases. In this paper, we examine the theory of this framework: (1) LDA can still fail even after PCA procedure. (2) Some small principal components that might be essential for classification are thrown away after PCA step. (3) The null space of the within-class scatter matrix Sw contains discriminative information for classification. To eliminate these deficiencies of the PCA plus LDA method we thus develop a new framework by introducing an inverse Fisher criterion and adding a constrain in PCA procedure so that the singularity phenomenon will not occur. Experiment results suggest that this new approach works well.

Original languageEnglish
Title of host publicationAdvances in Biometrics - International Conference, ICB 2006, Proceedings
Pages92-98
Number of pages7
Publication statusPublished - 2006
EventInternational Conference on Biometrics, ICB 2006 - Hong Kong, China
Duration: 5 Jan 20067 Jan 2006

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume3832 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

ConferenceInternational Conference on Biometrics, ICB 2006
Country/TerritoryChina
CityHong Kong
Period5/01/067/01/06

Scopus Subject Areas

  • Theoretical Computer Science
  • Computer Science(all)

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