WebFace260M: A Benchmark Unveiling the Power of Million-Scale Deep Face Recognition

@article{Zhu2021WebFace260MAB,
  title={WebFace260M: A Benchmark Unveiling the Power of Million-Scale Deep Face Recognition},
  author={Zheng Zhu and Guan Huang and Jiankang Deng and Yun Ye and Junjie Huang and Xinze Chen and Jiagang Zhu and Tian Yang and Jiwen Lu and Dalong Du and Jie Zhou},
  journal={2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year={2021},
  pages={10487-10497}
}
  • Zheng Zhu, Guan Huang, +8 authors Jie Zhou
  • Published 6 March 2021
  • Computer Science
  • 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
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… Expand
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