Facial feature extraction method based on coefficients of variances

Feng Xi Song, Dapeng Zhang, Cai Kou Chen, Jing Yu Yang

Research output: Journal article publicationJournal articleAcademic researchpeer-review

3 Citations (Scopus)


Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) are two popular feature extraction techniques in statistical pattern recognition field. Due to small sample size problem LDA cannot be directly applied to appearance-based face recognition tasks. As a consequence, a lot of LDA-based facial feature extraction techniques are proposed to deal with the problem one after the other. Nullspace Method is one of the most effective methods among them. The Nullspace Method tries to find a set of discriminant vectors which maximize the between-class scatter in the null space of the within-class scatter matrix. The calculation of its discriminant vectors will involve performing singular value decomposition on a high-dimensional matrix. It is generally memory- and time-consuming. Borrowing the key idea in Nullspace method and the concept of coefficient of variance in statistical analysis we present a novel facial feature extraction method, i.e., Discriminant based on Coefficient of Variance (DCV) in this paper. Experimental results performed on the FERET and AR face image databases demonstrate that DCV is a promising technique in comparison with Eigenfaces, Nullspace Method, and other state-of-the-art facial feature extraction methods.
Original languageEnglish
Pages (from-to)626-632
Number of pages7
JournalJournal of Computer Science and Technology
Issue number4
Publication statusPublished - 1 Jul 2007


  • Coefficient of variation
  • Face recognition
  • Gram-Schmidt orthogonalizing procedure
  • Linear feature extraction
  • Null space

ASJC Scopus subject areas

  • Software
  • Theoretical Computer Science
  • Hardware and Architecture
  • Computer Science Applications
  • Computational Theory and Mathematics


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