The global epidemic of COVID-19 makes people realize that wearing a mask is one of the most effective ways to protect ourselves from virus infections, which poses serious challenges for the existing face recognition system. To tackle the difficulties, a new method for masked face recognition is proposed by integrating a cropping-based approach with the Convolutional Block Attention Module (CBAM). The optimal cropping is explored for each case, while the CBAM module is adopted to focus on the regions around eyes. Two special application scenarios, using faces without mask for training to recognize masked faces, and using masked faces for training to recognize faces without mask, have also been studied. Comprehensive experiments on SMFRD, CISIA-Webface, AR and Extend Yela B datasets show that the proposed approach can significantly improve the performance of masked face recognition compared with other state-of-the-art approaches.

COVID-19的全球流行让人们认识到佩戴口罩是保护自己免受病毒感染的最有效方法之一，这对现有的人脸识别系统提出了严峻的挑战。为了解决这些困难，通过将基于裁剪的方法与卷积块注意力模块（CBAM）相结合，提出了一种新的蒙面人脸识别方法。针对每种情况探索最佳裁剪，同时采用 CBAM 模块重点关注眼睛周围区域。还研究了两种特殊的应用场景，即使用未戴口罩的人脸进行训练识别戴口罩的人脸，以及使用戴口罩的人脸进行训练识别未戴口罩的人脸。在 SMFRD、CISIA-Webface、AR 和 Extend Yela B 数据集上的综合实验表明，与其他最先进的方法相比，所提出的方法可以显着提高蒙面人脸识别的性能。