Special Issue: Artificial Intelligence and Pattern Recognition; Computer Graphics and Multimedia

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Facial Feature Extraction Method Based on Coefficients of Variances

Feng-Xi Song{1, 2, David Zhang{3, Cai-Kou Chen{4, and Jing-Yu Yang{4   

  1. {1}New Star Research Institute of Applied Technology in Hefei City, Hefei 230031, China {2}Shenzhen Graduate School, Harbin Institute of Technology, Shenzhen 518055, China {3}Hong Kong Polytechnic University, Hong Kong, China {4}Nanjing University of Science and Technology, Nanjing 210094, China
  • Received:2006-03-22 Revised:2007-01-24 Online:2007-07-10 Published:2007-07-10

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.

Key words: version sequence of specification; reconstruction; ECC (Extended Calculus of Construction); language ML;

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