A singular value decomposition representation based approach for robust face recognition

In the field of face recognition, sparse representation based classification (SRC) and collaborative representation based classification (CRC) have been widely used. Although both SRC and CRC have shown good classification results, it is still controversial whether it is sparse representation or collaborative representation that helps face recognition. In this paper, a new singular value decomposition based classification (SVDC) is proposed for face recognition. The proposed approach performs SVD on the training data of each class, and then determines the class of a test sample by comparing in which class of singular vectors it can be better represented. Experimental results on Yale B, PIE and UMIST datasets show that the proposed method achieves better recognition performance compared with several existing representation based classification algorithms. In addition, by adding Gaussian noise and Salt pepper noise to these datasets, it is proved that SVDC has better robustness. At the same time, the experimental results show that the recognition accuracy of the method acting on the training samples constructed by each class is higher than that of the method acting on the training sets constructed by all classes.