蒙面人脸识别挑战赛：InsightFace追踪报告
Masked Face Recognition Challenge: The InsightFace Track Report
摘要：
在COVID-19冠状病毒疫情期间，几乎每个人都戴着口罩，这对深度人脸识别提出了巨大挑战。在本次研讨会中，我们组织了Masked Face Recognition（MFR）挑战赛，并专注于在面具存在的情况下对深度人脸识别方法进行基准测试。在MFR挑战赛中，有两个主要赛道：InsightFace赛道和WebFace 260 M赛道。对于InsightFace跟踪，我们手动收集了一个具有7 K身份的大规模蒙面人脸测试集。此外，我们还收集了一个包含14 K身份的儿童测试集和一个包含242 K身份的多种族测试集。利用这三个测试集，我们建立了一个在线模型测试系统，可以对人脸识别模型进行综合评价。为了避免数据隐私问题，没有向公众发布测试图像。由于挑战仍在进行中，我们将继续更新排名靠前的解决方案以及这份关于arxiv的报告。
英文摘要：
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 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. 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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
