Undersampled face recognition with one-pass dictionary learning

Undersampled face recognition deals with the problem in which, for each subject to be recognized, only one or few images are available in the gallery (training) set. Thus, it is very difficult to handle large intra-class variations for face images. In this paper, we propose a one-pass dictionary learning algorithm to derive an auxiliary dictionary from external data, which consists of image variants of the subjects not of interest (not to be recognized). The proposed algorithm not only allows us to efficiently model intra-class variations such as illumination and expression changes, it also exhibits excellent abilities in recognizing corrupted images due to occlusion. In our experiments, we will show that our method would perform favorably against existing sparse representation or dictionary learning based approaches. Moreover, our computation time is remarkably less than that of recent dictionary learning based face recognition methods. Therefore, the effectiveness and efficiency of our proposed algorithm can be successfully verified.