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Application of the Discriminative Common Vector Approach to one sample problem

Matrix-based (2D) methods have advantages over vector-based (1D) methods. Matrix-based methods generally have less computational costs and higher recognition performances with respect to vector-based variants. In this work a two dimensional variation of Discriminative Common Vector Approach (2D-DCVA) is implemented. The performance of the method in single image problem is compared with the one dimensional Discriminative Common Vector Approach (1D-DCVA) and the two dimensional Fisher Linear Discriminant Analysis (2D-FLDA) on ORL, FERET, and YALE face databases. The best recognition performances are achieved in all databases with the proposed method.