Towards Large-scale Masked Face Recognition
Manyuan Zhang Bingqi Ma Guanglu Song Yunxiao Wang Hongsheng Li Yu Liu
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.