WebFace260M: A Benchmark for Million-Scale Deep Face Recognition.

ABSTRACT

In this paper, we contribute a new million-scale recognition benchmark, containing uncurated 4M identities/260M faces (WebFace260M) and cleaned 2M identities/42M faces (WebFace42M) training data, as well as an elaborately designed time-constrained evaluation protocol. Firstly, we collect 4M name lists and download 260M faces from the Internet. Then, a Cleaning Automatically utilizing Self-Training pipeline is devised to purify the tremendous WebFace260M, which is efficient and scalable. To our best knowledge, the cleaned WebFace42M is the largest public face recognition training set in the community. Referring to practical deployments, Face Recognition under Inference Time conStraint (FRUITS) protocol and a new test set with rich attributes are constructed. Moreover, we gather a large-scale masked face sub-set for biometrics assessment under COVID-19. For a comprehensive evaluation of face matchers, three recognition tasks are performed under standard, masked and unbiased settings, respectively. Equipped with this benchmark, we delve into million-scale face recognition problems. Enabled by WebFace42M, we reduce 40% failure rate on the challenging IJB-C set and rank the 3rd among 430 entries on NIST-FRVT. Even 10% data (WebFace4M) shows superior performance compared with the public training set. The proposed benchmark shows enormous potential on standard, masked and unbiased face recognition scenarios.