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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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README: REpresentation learning by fairness-Aware Disentangling MEthodPreprint
Park, S., Kim, D., Hwang, S., & Byun, H. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2007.03775 · Cited by 7
Fairness Testing of Deep Image Classification with Adequacy MetricsPreprint
Zhang, P., Wang, J., Sun, J., & Wang, X. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2111.08856 · Cited by 12
MFFI: Multi-Dimensional Face Forgery Image Dataset for Real-World Scenarios
Miao, C., Zhang, Y., Luo, M., Feng, W., Zheng, K., Chu, Q., Gong, T., Li, J., Diao, Y., Zhou, W., et al. (2025) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3746027.3758280 · Cited by 7
Enhanced Sample Self-Revised Network for Cross-Dataset Facial Expression Recognition
Xu, X., Zong, Y., Lu, C., & Jiang, X. (2022) · Entropy · doi:10.3390/e24101475 · Cited by 7
Open-set face identification with automatic detection of out-of-distribution images
Sokolova, A. D., Savchenko, A., & Nikolenko (2022) · Computer Optics · doi:10.18287/2412-6179-co-1061 · Cited by 6
Demographic Fairness Transformer for Bias Mitigation in Face Recognition
Kotwal, K., & Marcel, S. (2024) · IEEE International Joint Conference on Biometrics (IJCB) · doi:10.1109/ijcb62174.2024.10744457 · Cited by 6
Learnable Anchor Embedding for Asymmetric Face Recognition
Kim, J., Ng, T.-S., & Teoh, A. B. J. (2025) · Electronics · doi:10.3390/electronics14030455 · Cited by 6
Trinidad, J. E. (2025) · Qualitative Sociology · doi:10.1007/s11133-024-09590-w · Cited by 6
Two-stage unsupervised facial image quality measurement
Wang, G., Wang, Z., Huang, B., Jiang, K., He, Z., Zhu, H., Xiao, J., & Tian, X. S. (2022) · Information Sciences · doi:10.1016/j.ins.2022.08.064 · Cited by 6
OneFace: One Threshold for All
Liu, J., Yu, Z., Qin, H., Wu, Y., Liang, D., Zhao, G., & Xu, K. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-19775-8_32 · Cited by 6
On bias and fairness in deep learning-based facial analysis
Mittal, S., Majumdar, P., Mayank Vatsa, A., & Singh, R. (2023) · Handbook of statistics · doi:10.1016/bs.host.2023.01.002 · Cited by 8
Prototype Memory for Large-Scale Face Representation Learning
Smirnov, E., Garaev, N., Galyuk, V., & Lukyanets, E. (2022) · IEEE Access · doi:10.1109/access.2022.3146059 · Cited by 7
Through a Fair Looking-Glass: Mitigating Bias in Image Datasets
Rajabi, A., Yazdani-Jahromi, M., Garibay, Ö. Ö., & Sukthankar, G. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-35891-3_27 · Cited by 9
CASIA-SURF CeFA: A Benchmark for Multi-modal Cross-ethnicity Face Anti-spoofingPreprint
Li, A., Tan, Z., Li, X., Wan, J., Escalera, S., Guo, G., & Li, S. Z. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2003.05136 · Cited by 6
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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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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