Abstract:

The principal component analysis and linear discriminant analysis are important face recognition methods. Both of them can realize feature extraction by solving the eigenvalue problem. However, the small sample and singularity problem can be caused by the curse of dimensionality. We propose a simple face recognition method, which can effectively reduce computation cost without singular value decomposition. Firstly, we divide the image into blocks, and then calculate the polynomial coefficients to obtain the companion matrix which is used for feature extraction. A symmetric matrix based on the polynomial companion matrix of two different images is calculated. Finally, the nullity of symmetric matrix is calculated to recognize similar face images. Experiments on the ORL, Yale and FERET face databases verify the effectiveness of the proposed method. The results show that this method has high recognition performance in  recognizing the face with big variation in pose and illumination.

Key words: feature extraction, face recognition, companion matrix, block image