  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.
