Abstract: Principal Component Analysis (PCA) and Linear Discriminant Analysis(LDA) are two popular feature extraction techniques in statisticalpattern recognition field. Due to small sample size problem LDA cannotbe directly applied to appearance-based face recognition tasks. As aconsequence, a lot of LDA-based facial feature extraction techniques areproposed to deal with the problem one after the other. Nullspace Methodis one of the most effective methods among them. The Nullspace Methodtries to find a set of discriminant vectors which maximize thebetween-class scatter in the null space of the within-class scattermatrix. The calculation of its discriminant vectors will involveperforming singular value decomposition on a high-dimensional matrix. Itis generally memory- and time-consuming.Borrowing the key idea in Nullspace method and the concept ofcoefficient of variance in statistical analysis we present a novelfacial feature extraction method, i.e., Discriminant based onCoefficient of Variance (DCV) in this paper. Experimental resultsperformed on the FERET and AR face image databases demonstrate that DCVis a promising technique in comparison with Eigenfaces, NullspaceMethod, and other state-of-the-art facial feature extraction methods.