An Efficient Method to Solve Small Sample Size Problem of LDA Using Householder QR Factorization for Face Recognition

In this paper, we propose an efficient method to solve small sample size problem of linear discriminant analysis (LDA) for face recognition by performing Householder QR factorization procedure only once in the difference space. The proposed method is equivalent to existing LDA methods since all methods search optimal discriminative vectors of LDA in range space of total scatter matrix St and null space of within-class scatter matrix Sw. Since in the proposed method, the discriminant vectors are immediately obtained by performing Householder QR factorization once, the efficiency is improved compared with the existing methods. The effectiveness of the proposed method is verified in the experiments on the standard face databases.