Masked Face Recognition Challenge: The WebFace260M Track Report
Zheng Zhu, Guan Huang, Jiankang Deng, Yun Ye, Junjie Huang, and Xinze Chen, Jiagang Zhu, Tian Yang, Jia Guo, Jiwen Lu and, Dalong Du, Jie Zhou

TL;DR
This paper reports on a challenge to improve masked face recognition using the large WebFace260M dataset, addressing the urgent need for accurate recognition systems during COVID-19 mask-wearing conditions.
Contribution
It introduces a new benchmark and challenge for masked face recognition, utilizing the WebFace260M dataset and a real-world masked face test set, fostering advancements in practical MFR solutions.
Findings
49 teams outperformed the baseline
Large-scale real-world masked face test set created
Active ongoing leaderboard with multiple solutions
Abstract
According to WHO statistics, there are more than 204,617,027 confirmed COVID-19 cases including 4,323,247 deaths worldwide till August 12, 2021. During the coronavirus epidemic, almost everyone wears a facial mask. Traditionally, face recognition approaches process mostly non-occluded faces, which include primary facial features such as the eyes, nose, and mouth. Removing the mask for authentication in airports or laboratories will increase the risk of virus infection, posing a huge challenge to current face recognition systems. Due to the sudden outbreak of the epidemic, there are yet no publicly available real-world masked face recognition (MFR) benchmark. To cope with the above-mentioned issue, we organize the Face Bio-metrics under COVID Workshop and Masked Face Recognition Challenge in ICCV 2021. Enabled by the ultra-large-scale WebFace260M benchmark and the Face Recognition Under…
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Taxonomy
TopicsFace recognition and analysis · Face Recognition and Perception · Biometric Identification and Security
MethodsMeta Face Recognition
