Extended sparse representation-based classification method for face recognition

In sparse representation algorithms, a test sample can be sufficiently represented by exploiting only the training samples from the same class. However, due to variations of facial expressions, illuminations and poses, the other classes also have different degrees of influence on the linear representation of the test sample. Therefore, in order to represent a test sample more accurately, we propose a new sparse representation-based classification method which can strengthen the discriminative property of different classes and obtain a better representation coefficient vector. In our method, we introduce a weighted matrix, which can make small deviations correspond to higher weights and large deviations correspond to lower weights. Meanwhile, we improve the constraint term of representation coefficients, which can enhance the distinctiveness of different classes and make a better positive contribution to classification. In addition, motivated by the work of ProCRC algorithm, we take into account the deviation between the linear combination of all training samples and of each class. Thereby, the discriminative representation of the test sample is further guaranteed. Experimental results on the ORL, FERET, Extended-YaleB and AR databases show that the proposed method has better classification performance than other methods.