Region inpainting algorithm of mouth-muffles for facial recognition - Yue LI; Yaguan QIAN; Xiaohui GUAN; Wei LI; Bin WANG; Zhaoquan GU (Dianxin kexue)
Facial recognition under occlusion is a well-known difficult problem in real scenes.Especially after the outbreak of COVID-19, in airports, stations and other places that need to verify the identity of visitors, the mouth-muffle occlusion greatly reduces the facial features that can be important for identification, and the accuracy of face recognition algorithm decreases.Face de-occlusion was studied, and a novel framework was proposed to restore face, which used the edge generation network to generate edge maps.On this basis, the occluded region was restored by the region completion network while preserving identity information.In order to improve the performance, the spatial weighted adversarial loss and identity-preserving loss were introduced to train the above two networks.Then two face datasets with mouth-muffles were constructed by face landmarks.The experimental results show that the accuracy of facial recognition algorithm ArcFace on the face dataset restored by the proposed model was 98.39%, which was 4.13% higher than that of directly using ArcFace.