Effective Attention-Based Mechanism for Masked Face Recognition

Abstract

Research on facial recognition has recently been flourishing, which has led to the introduction of many robust methods. However, since the worldwide outbreak of COVID-19, people have had to regularly wear facial masks, thus making existing face recognition methods less reliable. Although normal face recognition methods are nearly complete, masked face recognition (MFR)—which refers to recognizing the identity of an individual when people wear a facial mask—remains the most challenging topic in this area. To overcome the difficulties involved in MFR, a novel deep learning method based on the convolutional block attention module (CBAM) and angular margin ArcFace loss is proposed. In the method, CBAM is integrated with convolutional neural networks (CNNs) to extract the input image feature maps, particularly of the region around the eyes. Meanwhile, ArcFace is used as a training loss function to optimize the feature embedding and enhance the discriminative feature for MFR. Because of the insufficient availability of masked face images for model training, this study used the data augmentation method to generate masked face images from a common face recognition dataset. The proposed method was evaluated using the well-known masked image version of LFW, AgeDB-30, CFP-FP, and real mask image MFR2 verification datasets. A variety of experiments confirmed that the proposed method offers improvements for MFR compared to the current state-of-the-art methods.