关键词: 人脸识别, 深度学习, 最大间距准则, 最小二乘估计

Abstract: At present, the outstanding problems encountered in face recognition technology are the decline of recognition precision caused by illumination, attitude, occlusion and expression. These problems are the main reasons for the imperfection of face recognition system. Deep learning is a new way to solve these problems effectively. Firstly, the deep learning algorithm is introduced to the multi-level learning, and then the high-level features are extracted to describe the human face. Finally, the Maximum Margin Criterion is used to reduce the reconstruction error caused by the least square estimation and realize the effective face recognition classification. The algorithm is simulated in the ORL, CAS-PEAL and the extended Yale-B face database under different illumination, attitude, occlusion, expression and changes in facial features conditions. The results show that the proposed algorithm has higher efficiency and accuracy than the traditional linear classification algorithm.

Key words: face recognition, deep learning, Maximum Margin Criterion（MMC）, the least square estimation