Masked Face Recognition Challenge: The InsightFace Track Report
This addresses the problem of accurate face recognition in masked scenarios for security and identification systems, but it is incremental as it focuses on benchmarking existing methods rather than introducing new ones.
The paper tackles the challenge of deep face recognition when people wear masks during the COVID-19 pandemic by organizing a Masked Face Recognition challenge, resulting in the creation of a large-scale test set with 7K identities for masked faces and additional test sets for children and multi-racial evaluations.
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.