An alternative to face image representation and classification

Sparse representation has brought a breakthrough to the face recognition community. It mainly attributes to the creative idea representing the probe face image by a linear combination of the gallery images. However, for face recognition applications, sparse representation still suffers from the following problem: because the face image varies with the illuminations, poses and facial expressions, the difference between the test sample and training samples from the same subject is usually large. Consequently, the representation on the probe face image provided by the original gallery images is not competent in accurately representing the probe face, which may lead to misclassification. In order to overcome this problem, we propose to modify training samples to produce an alternative set of the original training samples, and use both of the original set and produced set to obtain better representation on the test sample. The experimental results show that the proposed method can greatly improve previous sparse representation methods. It is notable that the error rate of classification of the proposed method can be 10% lower than previous sparse representation methods.