A novel subspace-based facial discriminant feature extraction method

Fengxi Song, Yong Xu, Dapeng Zhang, Tianwei Liu

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

Abstract

This paper presented a novel subspace-based facial discriminant feature extraction method, i.e. Orthogonalized Direct Linear Discriminant Analysis (OD-LDA), whose discriminant vectors could be obtained by performing Gram-Schmidt orthogonal procedure on a set of discriminant vectors of D-LDA. Experimental studies conducted on ORL, FERET, Yale, and AR face image databases showed that OD-LDA could compete with prevailing subspace-based facial discriminant feature extraction methods such as Fisherfaces, N-LDA D-LDA, Uncorrelated LDA, Parameterized D-LDA, K-L expansion based the between-class scatter matrix, and Orthogonal Complimentary Space Method in terms of recognition rate.
Original languageEnglish
Title of host publicationProceedings of the 2009 Chinese Conference on Pattern Recognition, CCPR 2009, and the 1st CJK Joint Workshop on Pattern Recognition, CJKPR
Pages869-873
Number of pages5
DOIs
Publication statusPublished - 1 Dec 2009
Event2009 Chinese Conference on Pattern Recognition, CCPR 2009 and the 1st CJK Joint Workshop on Pattern Recognition, CJKPR - Nanjing, China
Duration: 4 Nov 20096 Nov 2009

Conference

Conference2009 Chinese Conference on Pattern Recognition, CCPR 2009 and the 1st CJK Joint Workshop on Pattern Recognition, CJKPR
Country/TerritoryChina
CityNanjing
Period4/11/096/11/09

Keywords

  • Face recognition
  • Feature extraction
  • Linear discriminant analysis
  • Orthogonal procedure

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Vision and Pattern Recognition
  • Software

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