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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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“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]
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“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]
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“Current face recognition systems [8,24,43], already reach very high levels of accuracy in public non-forensic benchmarks, and it is expected that in the coming years they will keep improving.”
From Calibration of score based likelihood ratio estimation in automated forensic facial image comparison (2022) · cites this paper as [8]
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“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]
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“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]
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“So far, there are two versions of ShuffleNets, namely ShuffleNet v1 [49] and ShuffleNet v2 [50].”
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“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]
Experimental — accuracy not yet measuredIn the citing paper
“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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Unequal-Training for Deep Face Recognition With Long-Tailed Noisy Data
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Co-Mining: Deep Face Recognition With Noisy Labels
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Decorrelated Adversarial Learning for Age-Invariant Face Recognition
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NormFace: L2 Hypersphere Embedding for Face VerificationPreprint
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The Knowledge Within: Methods for Data-Free Model Compression
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Deep Hyperspherical LearningPreprint
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GroupFace: Learning Latent Groups and Constructing Group-Based Representations for Face Recognition
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Domain Balancing: Face Recognition on Long-Tailed Domains
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Self-citationNoise-Tolerant Paradigm for Training Face Recognition CNNs
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Global-Local GCN: Large-Scale Label Noise Cleansing for Face Recognition
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Flagged first, then by citations
Research works printed but not linked: 2
- 0, MxNetModels
- zheng, 2018, Cross-pose lfw: A database for studying cross-pose face recognition in unconstrained environments, Technical Report
Cited by
Sorted by citations
A Survey of Convolutional Neural Networks: Analysis, Applications, and Prospects
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Metadata
- 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
- 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
About these checks
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.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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