Robust face recognition based on sparse representation in 2D Fisherface space

Sparse representation is being proved to be effective for many tasks in the field of face recognition. In this paper, we will propose an efficient face recognition algorithm via sparse representation in 2D Fisherface space. We firstly transformed the 2D image into 2D Fisherface in preprocessing, and classify the testing image via sparse representation in the 2D Fisherface space. Then we extend the proposed method using some supplementary matrices to deal with random pixels corruption. For face image with contiguous occlusion, we partition each image into some blocks, and define a new rule combining sparsity and reconstruction residual to discard the occluded blocks, the final result is aggregated by voting the classification result of the valid individual block. The experimental results have shown that the proposed algorithm achieves a satisfying performance in both accuracy and robustness.