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.
@article{arxiv.2310.16364,
title = {Towards Large-scale Masked Face Recognition},
author = {Manyuan Zhang and Bingqi Ma and Guanglu Song and Yunxiao Wang and Hongsheng Li and Yu Liu},
journal= {arXiv preprint arXiv:2310.16364},
year = {2023}
}