关键词: 深度学习, 关键点, 遮挡判别, 模型组, 人脸识别

Abstract: Most face images taken from natural environment have occlusion, which has always been a huge challenge for face recognition. The mainstream deep model used for face recognition does not have particularly good identification performance for occlusion of face images. In order to solve the problem that the recognition rate decreases because occlusion exists and the occlusion position is uncertain for deep model, this paper proposes a face recognition algorithm of occlusion location based on PCANet (principal components analysis network), which combines deep learning and feature point occlusion detection. Classifier is used for key point detection, and PCANet deep learning model is used for feature extraction to form SVM (support vector machine) training model group. The occlusion discrimination classifier locates occlusion, combines the group of feature model to complete the occlusion face recognition task, and has strong robustness to facial expression changes. The experimental results show that the algorithm has achieved very good results for common occlusion types, and the extreme types of large-area occlusion also have a high recognition rate.

Key words: deep learning, key points, occlusion discrimination, group of model, face recognition