Papers › MagFace: A Universal Representation for Face Recognition and Quality Assessment
MagFace: A Universal Representation for Face Recognition and Quality Assessment
Qiang Meng, Shichao Zhao, Zhida Huang, Feng Zhou
The performance of face recognition system degrades when the variability of the acquired faces increases. Prior work alleviates this issue by either monitoring the face quality in pre-processing or predicting the data uncertainty along with the face feature. This paper proposes MagFace, a category of losses that learn a universal feature embedding whose magnitude can measure the quality of the given face. Under the new loss, it can be proven that the magnitude of the feature embedding monotonically increases if the subject is more likely to be recognized. In addition, MagFace introduces an adaptive mechanism to learn a wellstructured within-class feature distributions by pulling easy samples to class centers while pushing hard samples away. This prevents models from overfitting on noisy low-quality samples and improves face recognition in the wild. Extensive experiments conducted on face recognition, quality assessments as well as clustering demonstrate its superiority over state-of-the-arts. The code is available at https://github.com/IrvingMeng/MagFace.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Face Verification | IJB-C | MagFace++ | TAR @ FAR=1e-4 | 95.97% | #10 of 26 | Archive leaderboard | report |
| Face Verification | IJB-C | MagFace++ | TAR @ FAR=1e-5 | 90.36% | #10 of 26 | Archive leaderboard | report |
| Face Verification | IJB-C | MagFace++ | model | R100 | #10 of 26 | Archive leaderboard | report |
| Face Verification | IJB-C | MagFace++ | training dataset | MS1MV2 | #10 of 26 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Methods
Introduced by this paper: MagFace
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