Robust Face Recognition With Structurally Incoherent Low-Rank Matrix Decomposition

For the task of robust face recognition, we particularly focus on the scenario in which training and test image data are corrupted due to occlusion or disguise. Prior standard face recognition methods like Eigenfaces or state-of-the-art approaches such as sparse representation-based classification did not consider possible contamination of data during training, and thus their recognition performance on corrupted test data would be degraded. In this paper, we propose a novel face recognition algorithm based on low-rank matrix decomposition to address the aforementioned problem. Besides the capability of decomposing raw training data into a set of representative bases for better modeling the face images, we introduce a constraint of structural incoherence into the proposed algorithm, which enforces the bases learned for different classes to be as independent as possible. As a result, additional discriminating ability is added to the derived base matrices for improved recognition performance. Experimental results on different face databases with a variety of variations verify the effectiveness and robustness of our proposed method.