Since the beginning of world-wide COVID-19 pandemic, facial masks have been recommended to limit the spread of the disease. However, these masks hide certain facial attributes. Hence, it has become difficult for existing face recognition systems to perform identity verification on masked faces. In this context, it is necessary to develop masked Face Recognition (MFR) for contactless biometric recognition systems. Thus, in this paper, we propose Complementary Attention Learning and Multi-Focal Spatial Attention that precisely removes masked region by training complementary spatial attention to focus on two distinct regions: masked regions and backgrounds. In our method, standard spatial attention and networks focus on unmasked regions, and extract mask-invariant features while minimizing the loss of the conventional Face Recognition (FR) performance. For conventional FR, we evaluate the performance on the IJB-C, Age-DB, CALFW, and CPLFW datasets. We evaluate the MFR performance on the ICCV2021-MFR/Insightface track, and demonstrate the improved performance on the both MFR and FR datasets. Additionally, we empirically verify that spatial attention of proposed method is more precisely activated in unmasked regions.

翻译：自全球新冠疫情以来，佩戴口罩被推荐以限制疾病传播。然而，口罩遮挡了部分面部特征，导致现有面部识别系统难以对佩戴口罩的人脸进行身份验证。在此背景下，为非接触式生物特征识别系统开发口罩人脸识别（MFR）技术显得尤为重要。本文提出互补注意力学习与多焦点空间注意力机制，通过训练互补性空间注意力聚焦于两个不同区域——口罩遮挡区域和背景区域，从而精确移除口罩遮挡区域。在该方法中，标准空间注意力与网络聚焦于未遮挡区域，在最小化传统人脸识别（FR）性能损失的同时提取口罩不变特征。针对传统人脸识别任务，我们在IJB-C、Age-DB、CALFW和CPLFW数据集上评估性能；针对口罩人脸识别任务，我们在ICCV2021-MFR/Insightface赛道进行评估，并在MFR和FR两类数据集上均验证了性能提升。此外，实验证明所提方法的空间注意力在未遮挡区域激活更为精准。