SWTFace: A Multi-branch Network for Masked Face Detection and Recognition

The globalization pandemic of COVID-19 has made wearing masks to become a norm in people's lives, and this preventive measure brings new challenges to face recognition algorithms. To address this problem, in this paper, a multi-branch network is proposed to simultaneously complete the task of the masked face detection and recognition. Firstly, this network improves the Swin Transformer for extraction of facial features. Secondly, a face organ attention mechanism, FOA, is proposed to make the model focus on the face organs that are not covered by masks. Then, in order to overcome the problem of inadequate masked face dataset, a data augmentation method using 3D face mesh is proposed to add face mask. Finally, the experimental results show that, compared with the benchmark model, the proposed model reduces the number of model parameters by 60.6%, while the AP of masked face recognition increases by 5.33%, which better balances speed and accuracy.