With the development of computer vision technology and the popularization of intelligent terminals, facial recognition under mask occlusion has become an important part of character identity information recognition. The large area occlusion of masks poses great challenges to the learning of facial features. To solve this problem, this paper proposed a multi feature fusion based masked face recognition algorithm based on contrastive learning. This algorithm improved the traditional face feature vector learning loss function based on the triple relationship. It proposed a loss function based on the multi-instance relationship, which fully excavated the intra-modal and inter- modal correlation between multiple positive and negative samples of the masked face and the full face. Then, the features with high discrimination ability were learnt from the face. Meanwhile, it combined the local features such as eyebrows and eyes, as well as global features such as contours, to learn the effective feature vector representation of the masked face. This paper compared it with the benchmark algorithm on real masked face datasets and generated masked face data. The experimental results show that the proposed algorithm has higher recognition accuracy than the traditional triple loss function and feature fusion model.

[2023-10-09] Accepted Paper /doc/2023.06.0266/v1

[2024-01-05] Printed Article /doc/2023.06.0266/v2