Robust Face Recognition via Sparse Representation 论文

摘要

In this project, we implement a robust face recognition system via sparse representation and convex optimization. We treat each test sample as sparse linear combination of training samples, and get the sparse solution via L1-minimization. We also explore the group sparseness (L2-norm) as well as normal L1-norm regularization.We discuss the role of feature extraction and classification robustness to occlusion or pixel corruption of face recognition system. The experiments demonstrate the choice of features is no longer critical once the sparseness is properly harnessed. We also verify that the proposed algorithm outperforms other methods.