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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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Learning from Biased Data: A Semi-Parametric Approach
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Jacquet, M., & Grossrieder, L. (2021) · Criminologie · doi:10.7202/1076696ar · Cited by 1
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Reducing Geographic Performance Differential for Face RecognitionPreprint
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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
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.
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