Low-rank matrix recovery with structural incoherence for robust face recognition

We address the problem of robust face recognition, in which both training and test image data might be corrupted due to occlusion and disguise. From standard face recognition algorithms such as Eigenfaces to recently proposed sparse representation-based classification (SRC) methods, most prior works did not consider possible contamination of data during training, and thus the associated performance might be degraded. Based on the recent success of low-rank matrix recovery, we propose a novel low-rank matrix approximation algorithm with structural incoherence for robust face recognition. Our method not only decomposes raw training data into a set of representative basis with corresponding sparse errors for better modeling the face images, we further advocate the structural incoherence between the basis learned from different classes. These basis are encouraged to be as independent as possible due to the regularization on structural incoherence. We show that this provides additional discriminating ability to the original low-rank models for improved performance. Experimental results on public face databases verify the effectiveness and robustness of our method, which is also shown to outperform state-of-the-art SRC based approaches.