The DOI matches its Crossref record; 64 of the 68 references it cites were found in a registry or an index.
Racial Faces in the Wild: Reducing Racial Bias by Information Maximization Adaptation Network
Open accessAbstract
Racial bias is an important issue in biometric, but has not been thoroughly studied in deep face recognition. In this paper, we first contribute a dedicated dataset called Racial Faces in-the-Wild (RFW) database, on which we firmly validated the racial bias of four commercial APIs and four state-of-the-art (SOTA) algorithms. Then, we further present the solution using deep unsupervised domain adaptation and propose a deep information maximization adaptation network (IMAN) to alleviate this bias by using Caucasian as source domain and other races as target domains. This unsupervised method simultaneously aligns global distribution to decrease race gap at domain-level, and learns the discriminative target representations at cluster level. A novel mutual information loss is proposed to further enhance the discriminative ability of network output without label information. Extensive experiments on RFW, GBU, and IJB-A databases show that IMAN successfully learns features that generalize well across different races and across different databases.
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“Domain adaption techniques are also studied to enhance the generalizability of face recognition models between different data domains [84].”
From Trustworthy AI: From Principles to Practices (2023) · cites this paper as [84]
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Salimans, T., Goodfellow, I., Zaremba, W., Cheung, V., Radford, A., & Chen, X. (2016) · arXiv · doi:10.48550/arxiv.1606.03498 · Cited by 6,965
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Deep face recognition: A survey
Wang, M., & Deng, W. (2020) · Neurocomputing · doi:10.1016/j.neucom.2020.10.081 · Cited by 1,268
Self-citationTrustworthy AI: From Principles to Practices
Li, B., Qi, P., Liu, B., Di, S., Liu, J., Pei, J., Yi, J., & Zhou, B. (2023) · ACM Computing Surveys · doi:10.1145/3555803 · Cited by 612
WebFace260M: A Benchmark Unveiling the Power of Million-Scale Deep Face Recognition
Zhu, Z. D., Huang, G., Deng, J., Ye, Y., Huang, J., Chen, X., Zhu, J., Yang, T., Lu, J., Du, D., et al. (2021) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr46437.2021.01035 · Cited by 310
Towards Fairness in Visual Recognition: Effective Strategies for Bias Mitigation
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Timmons, A. C., Duong, J. B., Simo Fiallo, N., Lee, T., Vo, H. P. Q., Ahle, M. W., Comer, J. S., Brewer, L. C., Frazier, S. L., & Chaspari, T. (2023) · Perspectives on Psychological Science · doi:10.1177/17456916221134490 · Cited by 263
Metadata
- Authors
- Wang, M., Deng, W., Hu, J., Tao, X., & Huang, Y.
- Affiliations
- Beijing University of Posts and Telecommunications (CN)
- Venue
- 2019 IEEE/CVF International Conference on Computer Vision (ICCV)
- Publisher
- IEEE
- Published
- 2019-10
- Pages
- 692-702
- OpenAlex
- W2982232682
- Cited by
- 353
- References
- 68
- Citations by year
- 2026: 19 · 2025: 47 · 2024: 51 · 2023: 60 · 2022: 69 · 2021: 68 · 2020: 37 · 2019: 2
- FWCI
- 14.34
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Licence
- Locations
- IEEE/CVF International Conference on Computer Vision (ICCV) (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Face recognition and analysis
- Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Face and Expression Recognition
- Keywords
- racial bias, face recognition, deep face recognition, unsupervised domain adaptation, mutual information loss, cross-dataset generalization
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