Weighted sparse representation based on virtual test samples for face recognition

Face recognition with a very limited or even one training sample per subject is a very difficult task and it seems very challengeable to arise the accuracy of face recognition in such a condition. In this paper, we propose a novel weighted sparse representation method based on virtual test samples for face recognition. The presented method includes three steps. Firstly, generating virtual test samples for original test samples, and computing the distance between the test sample and each training sample to build a weighted training set. Secondly, representing the test sample over the weighted training set. Finally, computing the weight of each test sample and then conducting classification. The use of virtual samples of each individual allows us to get more distinguishing features and to obtain facial variations information from the external data. The used weight plays a role in enhancing the importance of these training images closer to a query image in representing this query image. An important advantage of the proposed approach is that the weight of each test sample is dynamically computed, instead of manual setting. Extensive experiments on YALE, AR and FERET face databases indicate that the proposed approach outperforms the other methods used in competition.