Face Recognition Based on Discriminative Low-Rank Matrix Recovery with Sparse Constraint

Abstract

In consideration of the problem that the existing face recognition methods cannot handle the face recognition under unsatisfactory situations, such as shadows, occlusions, stains, which cause low recognition rate. Therefore, an algorithm based on discriminative low-rank matrix recovery with sparse constraint (DLRRSC) is proposed. First, discriminative low-rank matrix recovery is used to correct the unsatisfactory training samples, and then it learns a low-rank projection matrix to correct the corrupted testing sample by projecting the sample onto its corresponding underlying subspace. Finally, the sparse representation method is used to classify the testing sample. Comparative experiments made on Yale B and AR Databases show that the performance of the method is better than other face recognition methods.