ArcFace: Additive Angular Margin Loss for Deep Face Recognition
Open accessAbstract
Recently, a popular line of research in face recognition is adopting margins in the well-established softmax loss function to maximize class separability. In this paper, we first introduce an Additive Angular Margin Loss (ArcFace), which not only has a clear geometric interpretation but also significantly enhances the discriminative power. Since ArcFace is susceptible to the massive label noise, we further propose sub-center ArcFace, in which each class contains K sub-centers and training samples only need to be close to any of the K positive sub-centers. Sub-center ArcFace encourages one dominant sub-class that contains the majority of clean faces and non-dominant sub-classes that include hard or noisy faces. Based on this self-propelled isolation, we boost the performance through automatically purifying raw web faces under massive real-world noise. Besides discriminative feature embedding, we also explore the inverse problem, mapping feature vectors to face images. Without training any additional generator or discriminator, the pre-trained ArcFace model can generate identity-preserved face images for both subjects inside and outside the training data only by using the network gradient and Batch Normalization (BN) priors. Extensive experiments demonstrate that ArcFace can enhance the discriminative feature embedding as well as strengthen the generative face synthesis.
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The pre-trained face recognition model ArcFace has already captured comprehensive features for real-world faces.
“It is reasonable to believe that the pre-trained face recognition model has already captured comprehensive features for real-world faces.”
From Transcending Forgery Specificity with Latent Space Augmentation for Generalizable Deepfake Detection (2024) · cites this paper as [10]
The Benchmark Results (p. 9) show strong face-recognition performance across datasets with pose and age variation, supporting the inference that pre-trained ArcFace captures useful features of real-world faces. But the face-synthesis discussion (p. 14) says its embedding features are trained to ignore variations such as age, gender and pose. The paper therefore does not support describing those features as comprehensive without qualification. (LLM, conf=0.84)
Experimental — accuracy not yet measuredUtilizing the knowledge gained from face recognition, as offered by ArcFace, is important for deepfake detection tasks.
“This highlights the importance of utilizing the knowledge gained from face recognition, as offered by ArcFace, for deepfake detection tasks.”
From Transcending Forgery Specificity with Latent Space Augmentation for Generalizable Deepfake Detection (2024) · cites this paper as [10]
ShuffleNets are a series of CNN-based models proposed by MEGVII to solve the problem of insufficient computing power of mobile devices; they combine pointwise group convolution, channel shuffle, and some other techniques, which significantly reduce computational cost with little loss of accuracy.
“ShuffleNets are a series of CNN-based models proposed by MEGVII to solve the problem of insufficient computing power of mobile devices.These models combine pointwise group convolution, channel shuffle, and some other techniques, which significantly reduce the computational cost with little loss of accuracy.”
From A Survey of Convolutional Neural Networks: Analysis, Applications, and Prospects (2022) · cites this paper as [50]
Split into 2 atoms — 0 confirmed, 0 partially supported, 0 disputed, 0 misattributed, 2 unverifiable. Expand the claim to see per-atom verdicts.
Experimental — accuracy not yet measuredShuffleNet v2 uses computational economical pointwise group convolution (GConv) and channel shuffle; the second ReLU activation function is removed; a 3×3 average pooling with two strides is utilized in shortcut paths, and element-wise addition is replaced by concatenation; these tricks further reduce the number of parameters.
“computational economical pointwise group convolution (GConv) and channel shuffle.Besides, the second ReLU activation function is removed; In Fig. 18 (c), a 3 × 3 average pooling with two strides is utilized in shortcut paths, and element-wise addition is replaced by concatenation.These tricks further reduce the number of parameters.”
From A Survey of Convolutional Neural Networks: Analysis, Applications, and Prospects (2022) · cites this paper as [50]
Split into 2 atoms — 0 confirmed, 0 partially supported, 0 disputed, 0 misattributed, 2 unverifiable. Expand the claim to see per-atom verdicts.
Experimental — accuracy not yet measuredThere are two versions of ShuffleNets: ShuffleNet v1 and ShuffleNet v2.
“So far, there are two versions of ShuffleNets, namely ShuffleNet v1 [49] and ShuffleNet v2 [50].”
From A Survey of Convolutional Neural Networks: Analysis, Applications, and Prospects (2022) · cites this paper as [50]
The cited paper’s Implementation Details (p. 8) discuss ResNet50 and ResNet100, while Benchmark Results (p. 12) discuss EfficientNet-B0 as a lightweight backbone. Searches for “ShuffleNet v1 ShuffleNet v2” and “ShuffleNet lightweight architecture channel shuffle” found no passage identifying ShuffleNet v1 or stating that there are two ShuffleNet versions. (LLM, conf=0.76)
Experimental — accuracy not yet measured
The ArcFace model performs poorly on images reconstructed from the anonymized event stream, with AUC=0.517 close to random (0.50).
“This suggests that the ArcFace model performs poorly on the images reconstructed from the anonymized event stream as the AUC=0.517, which is close to the random performance (AUC=0.50).”
From Event Anonymization: Privacy-Preserving Person Re-Identification and Pose Estimation in Event-Based Vision (2024) · cites this paper as [65]
The Benchmark Results (p. 11) evaluate ArcFace on face-recognition benchmarks, and the face-generation results (p. 14) concern a different reconstruction method. Our copy does not evaluate images reconstructed from an anonymized event stream or report AUC=0.517 (searched for the event-stream setting and AUC figure). Its results on other images do not establish or contradict the claimed performance. (LLM, conf=0.78)
Experimental — accuracy not yet measuredZakharov et al. used the cosine similarity between embedding vectors of the ArcFace network for measuring identity mismatch.
“Zakharov et al . [117] used the cosine similarity between embedding vectors of the ArcFace network [118] for measuring identity mismatch.”
From Deep learning for visual speech analysis: A survey (2024) · cites this paper as [118]
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- Authors
- Deng, J., Guo, J., Xue, N., & Zafeiriou, S.
- Affiliations
- Imperial College London (GB)
- insightface
- Venue
- 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
- Publisher
- IEEE
- Published
- 2019-06
- Volume
- 44
- Issue
- 10
- Pages
- 4685-4694
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W2784874046
- Cited by
- 7,781
- References
- 144
- Citations by year
- 2026: 879 · 2025: 1226 · 2024: 1265 · 2023: 1333 · 2022: 1100 · 2021: 1017 · 2020: 641 · 2019: 280 · 2018: 34 · 2017: 1 · 2016: 3 · 2013: 1
- FWCI
- 247.59
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings (publishedVersion)
- IEEE Transactions on Pattern Analysis and Machine Intelligence (publishedVersion)
- PubMed (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- Spiral (Imperial College London) (submittedVersion)
- arXiv (Cornell University) (cc-by)
- TIB Data Manager
- Funders
- University of Nottingham
- Engineering and Physical Sciences Research Council (EP/N007743/1, EP/S010203/1)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Face recognition and analysis
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Face and Expression Recognition
- Life Sciences › Neuroscience › Cognitive Neuroscience › Face Recognition and Perception
- Keywords
- ArcFace, deep face recognition, softmax loss, margin-based loss, label noise, class separability
- MeSH terms
- Facial Recognition, Algorithms, Face, Humans
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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