A fast mask synthesis method for face recognition

Kaiwen Guo, Chaoyang Zhao, Jinqiao Wang

Abstract: Mask face recognition has recently gained increasing attention in the current context. Face mask occlusion seriously affects the performance of face recognition systems, because more than 75% of the face area remains unexposed and the mask directly causes an increase in intra-class differences and a decrease in inter-class separability in the feature space. To improve the performance of face recognition model against mask occlusion, we propose a fast and efficient method for mask generation in this paper, which can avoid the need for large-scale collection of real-world mask face training sets. This approach can be embedded in the training process of any mask face model as a module and is very flexible. Experiments on the MLFW, MFR2 and RMFD datasets show the effectiveness and flexibility of our approach that outperform the state-of-the-art methods.