Masked face recognition with convolutional visual self-attention network

Abstract

With the global outbreak of COVID-19, wearing face masks has been actively introduced as an effective public measure to reduce the risk of virus infection. This measure leads to the failure of face recognition in many cases. Therefore, it is very necessary to improve the recognition performance of masked face recognition (MFR). Inspired by the successful application of self-attention in computer vision, we propose a Convolutional Visual Self-Attention Network (CVSAN), which uses self-attention to augment the convolution operator. Specifically, this is achieved by connecting a convolutional feature map, which enforces local features, to a self-attention feature map that is capable of modeling long-range dependencies. Since there is currently no publicly available large-scale masked face data, we generate a Masked VGGFace2 dataset based on the face detection algorithm to train the CVSAN model. Experiments show that the CVSAN algorithm significantly improves the performance of MFR compared to other algorithms.