During the COVID-19 coronavirus epidemic, almost everyone is wearing masks, which poses a huge challenge for deep learning-based face recognition algorithms. In this paper, we will present our \textbf{championship} solutions in ICCV MFR WebFace260M and InsightFace unconstrained tracks. We will focus on four challenges in large-scale masked face recognition, i.e., super-large scale training, data noise handling, masked and non-masked face recognition accuracy balancing, and how to design inference-friendly model architecture. We hope that the discussion on these four aspects can guide future research towards more robust masked face recognition systems.

翻译：在COVID-19冠状病毒疫情期间，几乎人人佩戴口罩，这给基于深度学习的人脸识别算法带来了巨大挑战。本文介绍我们在ICCV MFR WebFace260M和InsightFace无限制赛道中获得的\textbf{冠军}解决方案。我们重点关注大规模口罩人脸识别中的四个挑战：超大规模训练、数据噪声处理、口罩与非口罩人脸识别精度平衡，以及如何设计推理友好的模型架构。希望围绕这四个方面的讨论能够为未来构建更鲁棒的口罩人脸识别系统提供指导。