Kavli Affiliate: Zheng Zhu
| First 5 Authors: Zheng Zhu, Guan Huang, Jiankang Deng, Yun Ye, Junjie Huang
| Summary:
Face benchmarks empower the research community to train and evaluate
high-performance face recognition systems. 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 (CAST) pipeline is
devised to purify the tremendous WebFace260M, which is efficient and scalable.
To the best of our knowledge, the cleaned WebFace42M is the largest public face
recognition training set and we expect to close the data gap between academia
and industry. Referring to practical deployments, Face Recognition Under
Inference Time conStraint (FRUITS) protocol and a new test set with rich
attributes are constructed. Besides, 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. A distributed
framework is developed to train face recognition models efficiently without
tampering with the performance. Enabled by WebFace42M, we reduce 40% failure
rate on the challenging IJB-C set and rank 3rd among 430 entries on NIST-FRVT.
Even 10% data (WebFace4M) shows superior performance compared with the public
training sets. Furthermore, comprehensive baselines are established under the
FRUITS-100/500/1000 milliseconds protocols. The proposed benchmark shows
enormous potential on standard, masked and unbiased face recognition scenarios.
Our WebFace260M website is https://www.face-benchmark.org.
| Search Query: ArXiv Query: search_query=au:”Zheng Zhu”&id_list=&start=0&max_results=10