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Face recognition remains a hot topic in computer vision, and it ischallenging to tackle the problem that both the training and testing images arecorrupted. In this paper, we propose a novel semi-supervised method based onthe theory of the low-rank matrix recovery for face recognition, which cansimultaneously learn discriminative low-rank and sparse representations forboth training and testing images. To this end, a correlation penalty term isintroduced into the formulation of our proposed method to learn an incoherentdictionary. Experimental results on several face image databases demonstratethe effectiveness of our method, i.e., the proposed method is robust to theillumination, expression and pose variations, as well as images with noisessuch as block occlusion or uniform noises.

Low-rank representations with incoherent dictionary for face recognition