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.
翻译:首先,我们收集了4M名名单,并从互联网上下载了260M个高性能面部识别系统。然后,我们设计了一个自动使用自我培训(CAST)管道,以净化巨大的WebFace260M这个高效和可缩放的WebFace260M的确认基准。为了最先进的知识,清洁的WebFace42M是最大的公众面部识别培训,我们期望缩小学术界和工业界之间的数据差距。谈到实际部署,Science Inference Inference ConStra(FRUITS)协议,以及一个新的具有丰富属性的测试集。此外,我们还在COVID-19下收集了一个大规模隐性脸部脸部脸部脸部套,在面部和可缩放的40CefaceFace260M模型中,三种识别任务在标准下进行,在标准基底线下进行。