The DOI matches its Crossref record; 44 of the 44 references it cites were found in a registry or an index.
AdaFace: Quality Adaptive Margin for Face Recognition
Open accessAbstract
Recognition in low quality face datasets is challenging because facial attributes are obscured and degraded. Advances in margin-based loss functions have resulted in enhanced discriminability of faces in the embedding space. Further, previous studies have studied the effect of adaptive losses to assign more importance to misclassified (hard) examples. In this work, we introduce another aspect of adaptiveness in the loss function, namely the image quality. We argue that the strategy to emphasize misclassified samples should be adjusted according to their image quality. Specifically, the relative importance of easy or hard samples should be based on the sample's image quality. We propose a new loss function that emphasizes samples of different difficulties based on their image quality. Our method achieves this in the form of an adaptive margin function by approximating the image quality with feature norms. Extensive experiments show that our method, AdaFace, improves the face recognition performance over the state-of-the-art (SoTA) on four datasets (IJB-B, IJB-C, IJB-S and TinyFace). Code and models are released in Supp.
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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
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
Deep Learning Face Attributes in the Wild
Liu, Z., Luo, P., Wang, X., & Tang, X. (2015) · 2015 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.425 · Cited by 7,747
Joint Face Detection and Alignment Using Multitask Cascaded Convolutional Networks
Zhang, K., Zhang, Z., Li, Z., & Qiao, Y. (2016) · IEEE Signal Processing Letters · doi:10.1109/lsp.2016.2603342 · Cited by 6,214
No-Reference Image Quality Assessment in the Spatial Domain
Mittal, A., Moorthy, A. K., & Bovik, A. C. (2012) · IEEE Transactions on Image Processing · doi:10.1109/tip.2012.2214050 · Cited by 6,064
Bengio, Y., Louradour, J., Collobert, R., & Weston, J. (2009) · Proceedings of the 26th Annual International Conference on Machine Learning · doi:10.1145/1553374.1553380 · Cited by 5,366 · cited 1×
“Typically, assigning different importance to different difficulties of samples is done by looking at the training progression (curriculum learning) [1,13].”
BackgroundLabeled Faces in the Wild: A Database forStudying Face Recognition in Unconstrained Environments
Huang, G. B., Mattar, M., Berg, T. L., & Miller, E. G. (2008) · HAL · Cited by 4,445
Image information and visual quality
Sheikh, H. R., & Bovik, A. C. (2006) · IEEE Transactions on Image Processing · doi:10.1109/tip.2005.859378 · Cited by 4,104
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
Cited by
On the Vulnerability of Deepfake Detectors to Attacks Generated by Denoising Diffusion Models
Ivanovska, M., & Štruc, V. (2024) · 2024 IEEE/CVF Winter Conference on Applications of Computer Vision Workshops (WACVW) · doi:10.1109/wacvw60836.2024.00115 · Cited by 16
Prakash, A., Thejaswin, S., Nambiar, A., & Bernardino, A. (2023) · 2023 IEEE International Joint Conference on Biometrics (IJCB) · doi:10.1109/ijcb57857.2023.10448765 · Cited by 5
Kwon, P., & Chen, C. (2025) · arXiv · doi:10.48550/arxiv.2512.01686 · Cited by 1
“The benchmark primarily measures Character Similarity (CIDS) [4, 12, 21, 42] and Style Similarity (CSD) [50] between reference and generated frames (cross) and be-tween generated frames (self).”
Edge-Constrained Video Analytics: A Comprehensive Survey on Detection, Tracking, and Recognition
Toshpulatov, M. A., Lee, W., & Cho, J. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.6614394
Metadata
- Authors
- Kim, M., Jain, A. K., & Liu, X.
- Venue
- 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
- Publisher
- IEEE
- Published
- 2022-06
- Pages
- 18729-18738
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W4312402191
- Cited by
- 584
- References
- 44
- FWCI
- 46.16
- Type
- conference paper
- Language
- en
- Open access
- green
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