Learning 3D Face Representation with Vision Transformer for Masked Face Recognition

Masked face recognition, a non-contact biometric technology, has attracted much attention and developed rapidly during the coronavirus disease 2019 (COVID-19) outbreak. The existing work trains the masked face recognition model based on a large number of 2D masked face images. However, in practical application scenarios, it is difficult to obtain a large number of masked face images in a short period of time. Therefore, combined with 3D face recognition technology, this paper proposes a masked face recognition model trained with non-masked face images. In this paper, we locate and segment the complete face region and the face region not occluded by masks from the face point clouds. The geometric features of the 3D face surface, namely depth, azimuth, and elevation, are extracted from the above two regions to generate training data. The proposed masked face recognition model based on vision Transformer divides the complete faces and part of the faces into sequence images, and then captures the relationship between the image slices to compensate for the impact caused by the lack of face information, thereby improving the recognition performance. Comparative experiments with the state-of-the-art masked face recognition work are carried out on four databases. The experimental results show that the recognition accuracy of the proposed model is improved by 9.86% on Bosphorus database, 16.77
^%
on CASIA-3D FaceV1 database, 2.32% on StirlingESRC database, and 34.81% on Ajmal main database, respectively, which verifies the effectiveness and stability of the proposed model.