Papers › SphereFace: Deep Hypersphere Embedding for Face Recognition
SphereFace: Deep Hypersphere Embedding for Face Recognition
Weiyang Liu, Yandong Wen, Zhiding Yu, Ming Li, Bhiksha Raj, Le Song
This paper addresses deep face recognition (FR) problem under open-set protocol, where ideal face features are expected to have smaller maximal intra-class distance than minimal inter-class distance under a suitably chosen metric space. However, few existing algorithms can effectively achieve this criterion. To this end, we propose the angular softmax (A-Softmax) loss that enables convolutional neural networks (CNNs) to learn angularly discriminative features. Geometrically, A-Softmax loss can be viewed as imposing discriminative constraints on a hypersphere manifold, which intrinsically matches the prior that faces also lie on a manifold. Moreover, the size of angular margin can be quantitatively adjusted by a parameter m. We further derive specific m to approximate the ideal feature criterion. Extensive analysis and experiments on Labeled Face in the Wild (LFW), Youtube Faces (YTF) and MegaFace Challenge show the superiority of A-Softmax loss in FR tasks. The code has also been made publicly available.
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Code
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22 repositories listed; official and paper-mentioned ones first.
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Code Syntology ran Syntology
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Face Identification | MegaFace | SphereFace (3-patch ensemble) | Accuracy | 75.766% | #10 of 13 | Archive leaderboard | report |
| Face Identification | MegaFace | SphereFace (single model) | Accuracy | 72.729% | #12 of 13 | Archive leaderboard | report |
| Face Identification | Trillion Pairs Dataset | A-Softmax | Accuracy | 43.89 | #4 of 6 | Archive leaderboard | report |
| Face Verification | CK+ | SphereFace | Accuracy | 93.80 | #1 of 1 | Archive leaderboard | report |
| Face Verification | MegaFace | SphereFace (3-patch ensemble) | Accuracy | 89.142% | #10 of 12 | Archive leaderboard | report |
| Face Verification | MegaFace | SphereFace (single model) | Accuracy | 85.561% | #11 of 12 | Archive leaderboard | report |
| Face Verification | Trillion Pairs Dataset | A-Softmax | Accuracy | 43.76 | #4 of 6 | Archive leaderboard | report |
| Face Verification | YouTube Faces DB | SphereFace | Accuracy | 95.0% | #10 of 12 | 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
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