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Robust low-rank subspace recovery and face image denoising for face recognition

We propose a low-rank subspace recovery and image denoising method for face recognition. Traditional subspace methods commonly assume that face images from a single class lie on a low-rank subspace. However, due to shadows, specularities, occlusion and corruption, real face images seldom reveal such low-rank structure. To address this problem, we cast the problem of recovering face subspace from noisy images as a problem of recovering a low-rank matrix with sparse error of arbitrary large magnitude. By using the recent breakthroughs in convex optimization, we can exactly recover the subspaces from corrupted facial data. We apply this method to two well-known subspace methods: nearest subspace and sparse representation face recognition. The results show that our method is efficient in recovering the low-rank face subspaces by removing the noise in the training images, thus significantly improve the robustness of these methods in the presence of occlusion and corruption in both train and test images.