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 this https URL

本研究提供新的百萬級別人臉識別測試基準，名為 WebFace260M，包括 4 百萬的姓名列表和 260 百萬的人臉圖像。使用此基準，研究人員開發了有效的分散式框架，並在各種環境中進行了全面的基準測試，顯示出極大的潛力。

WebFace260M: 百万级深度人脸识别基准测试

WebFace260M: A Benchmark for Million-Scale Deep Face Recognition

In this paper, we contribute a new million-scale face benchmark containing
noisy 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 list 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
scenarios, Face Recognition Under Inference Time conStraint (FRUITS) protocol
and a test set are constructed to comprehensively evaluate face matchers.
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. Empowered by WebFace42M, we
reduce relative 40% failure rate on the challenging IJB-C set, and ranks the
3rd among 430 entries on NIST-FRVT. Even 10% data (WebFace4M) shows superior
performance compared with public training set. Furthermore, comprehensive
baselines are established on our rich-attribute test set under
FRUITS-100ms/500ms/1000ms protocol, including MobileNet, EfficientNet,
AttentionNet, ResNet, SENet, ResNeXt and RegNet families. Benchmark website is
this https URL

该研究创建了一个新的百万级人脸基准数据集，包含清理后的面部图像，以及设计了一种时间约束的评估协议，建立了分布式框架以有效地训练人脸识别模型，并在 FRUITS 协议下建立了广泛的基准线。