A Joint Feature Aggregation Method for Robust Masked Face Recognition

Masked face recognition becomes an important issue of prevention and monitor in outbreak of COVID-19. Due to loss of facial features caused by masks, unmasked face recognition could not identify the specific person well. Current masked faces methods focus on local features from the unmasked regions or recover masked faces to fit standard face recognition models. These methods only focus on partial information of faces thus these features are not robust enough to deal with complex situations. To solve this problem, we propose a joint feature aggregation method for robust masked face recognition. Firstly, we design a multi-module feature extraction network to extract different features, including local module (LM), global module (GM), and recovery module (RM). Our method not only extracts global features from the original masked faces but also extracts local features from the unmasked area since it is a discriminative part of masked faces. Specially, we utilize a pretrained recovery model to recover masked faces and get some recovery features from the recovered faces. Finally, features from three modules are aggregated as a joint feature of masked faces. The joint feature enhances the feature representation of masked faces thus it is more discriminative and robust than that in previous methods. Experiments show that our method can achieve better performance than previous methods on LFW dataset.