A SVM face recognition method based on optimized gabor features

Linlin Shen, Li Bai, Zhen Ji

Research output: Chapter in Book/Conference proceedingConference contributionpeer-review

12 Citations (Scopus)

Abstract

A novel Support Vector Machine (SVM) face recognition method using optimized Gabor features is presented in this paper. 200 Gabor features are first selected by a boosting algorithm, which are then combined with SVM to build a two-class based face recognition system. While computation and memory cost of the Gabor feature extraction process has been significantly reduced, our method has achieved the same accuracy as a Gabor feature and Linear Discriminant Analysis (LDA) based multi-class system.

Original languageEnglish
Title of host publicationAdvances in Visual Information Systems - 9th International Conference, VISUAL 2007, Revised Selected Papers
PublisherSpringer Verlag
Pages165-174
Number of pages10
ISBN (Print)9783540764137
DOIs
Publication statusPublished - 2007
Externally publishedYes
Event9th International Conference on Visual Information Systems, VISUAL 2007 - Shanghai, China
Duration: 28 Jun 200729 Jun 2007

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume4781 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference9th International Conference on Visual Information Systems, VISUAL 2007
Country/TerritoryChina
CityShanghai
Period28/06/0729/06/07

Keywords

  • Gabor features
  • Linear discriminant analysis
  • Support vector machine

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science (all)

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