Face Recognition with Robust Matrix Factorization

In face recognition, we may encounter face images with shadow and illumination, which will affect the recognition. In this scenario, the low-rank matrix and a sparse matrix can be obtained by low-rank matrix decomposition of the collected original face image, where the low-rank matrix is the face image without shadow and illumination. In order to obtain the low-rank matrix, the Sub-gradient method and AIRLS method are used in this paper, and their effects are compared in the experimental verification of Yale face database.