Face recognition by using discriminative common vectors

In face recognition tasks, the dimension of the sample space is typically larger than the number of the samples in the training set. As a consequence, the within-class scatter matrix is singular and the linear discriminant analysis (LDA) method cannot be applied directly. This problem is also known as the "small sample size" problem. In this paper, we propose a new face recognition method based on the discriminative common vectors for the small sample size case. The discriminative common vectors representing the people in the face database were found by using the space of the within-class scatter matrix. Then, these vectors were used for classification of new faces. Test results show that the proposed method is superior to other methods in terms of accuracy, efficiency, and numerical stability.