The DOI matches its Crossref record; 8 of the 8 references it cites were found in a registry or an index.
Additive Margin Softmax for Face Verification
Open accessAbstract
In this letter, we propose a conceptually simple and intuitive learning objective function, i.e., additive margin softmax, for face verification. In general, face verification tasks can be viewed as metric learning problems, even though lots of face verification models are trained in classification schemes. It is possible when a large-margin strategy is introduced into the classification model to encourage intraclass variance minimization. As one alternative, angular softmax has been proposed to incorporate the margin. In this letter, we introduce another kind of margin to the softmax loss function, which is more intuitive and interpretable. Experiments on LFW and MegaFace show that our algorithm performs better when the evaluation criteria are designed for very low false alarm rate.
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Cites
Deep Residual Learning for Image Recognition
He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.90 · Cited by 235,474
Focal Loss for Dense Object Detection
Lin, T.-Y., Goyal, P., Girshick, R., He, K., & Dollar, P. (2017) · 2017 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.324 · Cited by 30,581
FaceNet: A unified embedding for face recognition and clustering
Schroff, F., Kalenichenko, D., & Philbin, J. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298682 · Cited by 15,267
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Hinton, G., Vinyals, O., & Dean, J. (2015) · arXiv · doi:10.48550/arxiv.1503.02531 · Cited by 14,204
ArcFace: Additive Angular Margin Loss for Deep Face Recognition
Deng, J., Guo, J., Xue, N., & Zafeiriou, S. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2019.00482 · Cited by 7,781
A Discriminative Feature Learning Approach for Deep Face Recognition
Wen, Y., Zhang, K., Li, Z., & Qiao, Y. (2016) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-46478-7_31 · Cited by 4,077
CosFace: Large Margin Cosine Loss for Deep Face Recognition
Wang, H., Wang, Y., Zhou, Z., Ji, X., Gong, D., Zhou, J., Li, Z., & Liu, W. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00552 · Cited by 3,034
Self-citationRegularizing Neural Networks by Penalizing Confident Output Distributions
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Cited by
ArcFace: Additive Angular Margin Loss for Deep Face Recognition
Deng, J., Guo, J., Xue, N., & Zafeiriou, S. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2019.00482 · Cited by 7,781
Circle Loss: A Unified Perspective of Pair Similarity Optimization
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Multi-attentional Deepfake Detection
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“With the same motivation in designing auxiliary loss, we substitute the regional independence loss with Additive Angular Margin softmax(AMS)[42] that can also force feature vectors close to their center.”
MethodReview of Image Classification Algorithms Based on Convolutional Neural Networks
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MobileFaceNets: Efficient CNNs for Accurate Real-Time Face Verification on Mobile Devices
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Metadata
- Authors
- Wang, F., Cheng, J., Liu, W., & Liu, H.
- Venue
- IEEE Signal Processing Letters
- Published
- 2018-07
- Volume
- 25
- Issue
- 7
- Pages
- 926-930
- ISSN
- 1070-9908 (ISSN-L)
- 1558-2361
- OpenAlex
- W2784163702
- Cited by
- 1,106
- References
- 8
- FWCI
- 53.09
- Type
- journal article
- Language
- en
- Open access
- green
- Funders
- National Natural Science Foundation of China (61671125, 61201271, 61301269)
- State Key Laboratory of Synthetical Automation for Process Industries (PAL-N201401)
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