Masked Face Recognition Challenge: The InsightFace Track Report

被引:41
|
作者
Deng, Jiankang [1 ]
Guo, Jia [2 ]
An, Xiang [2 ]
Zhu, Zheng [3 ]
Zafeiriou, Stefanos [1 ]
机构
[1] Imperial Coll London, London, England
[2] InsightFace, Nanjing, Peoples R China
[3] Tsinghua Univ, Beijing, Peoples R China
关键词
D O I
10.1109/ICCVW54120.2021.00165
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
During the COVID-19 coronavirus epidemic, almost everyone wears a facial mask, which poses a huge challenge to deep face recognition. In this workshop, we organize Masked Face Recognition (MFR) challenge(1) and focus on bench-marking deep face recognition methods under the existence of facial masks. In the MFR challenge, there are two main tracks: the InsightFace track and the WebFace260M track [38]. For the InsightFace track, we manually collect a large-scale masked face test set with 7K identities. In addition, we also collect a children test set including 14K identities and a multi-racial test set containing 242K identities. By using these three test sets, we build up an online model testing system, which can give a comprehensive evaluation of face recognition models. To avoid data privacy problems, no test image is released to the public. As the challenge is still under-going, we will keep on updating the top-ranked solutions as well as this report on the arxiv.
引用
收藏
页码:1437 / 1444
页数:8
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