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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].”
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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
Very Deep Convolutional Networks for Large-Scale Image Recognition
Simonyan, K., & Zisserman, A. (2014) · arXiv · doi:10.48550/arxiv.1409.1556 · Cited by 114,148
ImageNet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017) · Communications of the ACM · doi:10.1145/3065386 · Cited by 112,155
Going deeper with convolutions
Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., & Rabinovich, A. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298594 · Cited by 48,562
Squeeze-and-Excitation Networks
Hu, J., Shen, L., & Sun, G. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00745 · Cited by 35,667
Self-citationFaceNet: 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
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
Improved Techniques for Training GANs
Salimans, T., Goodfellow, I., Zaremba, W., Cheung, V., Radford, A., & Chen, X. (2016) · arXiv · doi:10.48550/arxiv.1606.03498 · Cited by 6,965
Parkhi, O. M., Vedaldi, A., & Zisserman, A. (2015) · Procedings of the British Machine Vision Conference 2015 · doi:10.5244/c.29.41 · Cited by 5,479
Unsupervised Domain Adaptation by BackpropagationPreprint
Ganin, Y., & Lempitsky, V. (2014) · arXiv (Cornell University) · doi:10.48550/arxiv.1409.7495 · Cited by 5,441
Adversarial Discriminative Domain Adaptation
Tzeng, E., Hoffman, J., Saenko, K., & Darrell, T. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.316 · Cited by 5,154
Learning Transferable Features with Deep Adaptation Networks
Long, M., Cao, Y., Wang, J., & Jordan, M. I. (2015) · arXiv · doi:10.48550/arxiv.1502.02791 · Cited by 4,505
Labeled 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
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
DeCAF: A Deep Convolutional Activation Feature for Generic Visual Recognition
Donahue, J., Jia, Y., Vinyals, O., Hoffman, J., Zhang, N., Tzeng, E., & Darrell, T. (2013) · arXiv · doi:10.48550/arxiv.1310.1531 · Cited by 4,019
InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial NetsPreprint
Chen, X., Duan, Y., Houthooft, R., Schulman, J., Sutskever, I., & Abbeel, P. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1606.03657 · Cited by 3,557
(2018) · Cited by 3,510
VGGFace2: A Dataset for Recognising Faces across Pose and Age
Cao, Q., Shen, L., Xie, W., Parkhi, O. M., & Zisserman, A. (2018) · 2018 13th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2018) · doi:10.1109/fg.2018.00020 · Cited by 3,032
Deep domain confusion Maximizing for domain invariance
Tzeng, E., Hoffman, J., Zhang, N., Saenko, K., & Darrell, T. (2014) · arXiv · doi:10.48550/arxiv.1412.3474 · Cited by 2,285
Learning Face Representation from ScratchPreprint
Yi, D., Lei, Z., Liao, S., & Li, S. Z. (2014) · arXiv (Cornell University) · doi:10.48550/arxiv.1411.7923 · Cited by 2,282
SphereFace: Deep Hypersphere Embedding for Face Recognition
Liu, W., Wen, Y., Yu, Z., Li, M., Raj, B., & Song, L. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.713 · Cited by 2,280
Deep Transfer Learning with Joint Adaptation NetworksPreprint
Long, M., Han, Z., Wang, J., & Jordan, M. I. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1605.06636 · Cited by 2,233
Deep visual domain adaptation: A survey
Wang, M., & Deng, W. (2018) · Neurocomputing · doi:10.1016/j.neucom.2018.05.083 · Cited by 2,209
Self-citationMS-Celeb-1M: A Dataset and Benchmark for Large-Scale Face Recognition
Guo, Y., Zhang, L., Hu, Y., He, X., & Gao, J. (2016) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-46487-9_6 · Cited by 2,122
Deep Learning Face Representation by Joint Identification-Verification
Sun, Y., Wang, X., & Tang, X. (2014) · arXiv · doi:10.48550/arxiv.1406.4773 · Cited by 1,906
Integrating structured biological data by Kernel Maximum Mean Discrepancy
Borgwardt, K. M., Gretton, A., Rasch, M. J., Kriegel, H.-P., Schölkopf, B., & Smola, A. J. (2006) · Bioinformatics · doi:10.1093/bioinformatics/btl242 · Cited by 1,674
Semi-supervised Learning by Entropy Minimization
Grandvalet, Y., & Bengio, Y. (2004) · Cited by 1,585
Deep face recognition: A survey
Wang, M., & Deng, W. (2020) · Neurocomputing · doi:10.1016/j.neucom.2020.10.081 · Cited by 1,268
Self-citationCross-domain sentiment classification via spectral feature alignment
Pan, S. J., Ni, X., Sun, J.-T., Yang, Q., & Chen, Z. (2010) · International World Wide Web Conference (WWW) · doi:10.1145/1772690.1772767 · Cited by 791
Barratt, S., & Sharma, R. (2018) · arXiv · doi:10.48550/arxiv.1801.01973 · Cited by 732
Pushing the frontiers of unconstrained face detection and recognition: IARPA Janus Benchmark A
Klare, B. F., Klein, B., Taborsky, E., Blanton, A., Cheney, J., Allen, K., Grother, P. J., Mah, A., Burge, M. J., & Jain, A. K. (2015) · 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/cvpr.2015.7298803 · Cited by 706
Deep Face Recognition: A Survey
Masi, I., Wu, Y., Hassner, T., & Natarajan, P. (2018) · SIBGRAPI Conference on Graphics, Patterns and Images (SIBGRAPI) · doi:10.1109/sibgrapi.2018.00067 · Cited by 611
Collaborative and Adversarial Network for Unsupervised Domain Adaptation
Zhang, W., Ouyang, W., Li, W., & Xu, D. (2018) · 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/cvpr.2018.00400 · Cited by 528
Face Recognition Performance: Role of Demographic Information
Klare, B. F., Burge, M. J., Klontz, J. C., Vorder Bruegge, R. W., & Jain, A. K. (2012) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2012.2214212 · Cited by 516 · cited 3×
“Several studies [49, 29, 23, 50, 36] have uncovered racial bias in non-deep 1 http://www.whdeng.cn/RFW/index.html face recognition algorithms.”
BackgroundProgressive Feature Alignment for Unsupervised Domain Adaptation
Chen, C., Xie, W., Huang, W., Rong, Y., Ding, X., Huang, Y., Xu, T., & Huang, J. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2019.00072 · Cited by 481
Self-citationAsymmetric Tri-training for Unsupervised Domain AdaptationPreprint
Saito, K., Ushiku, Y., & Harada, T. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1702.08400 · Cited by 475
Learning Semantic Representations for Unsupervised Domain Adaptation
Xie, S., Zheng, Z., Chen, L., & Chen, C. (2018) · International Conference on Machine Learning · Cited by 419
Co-Training for Domain Adaptation
Chen, M., Weinberger, K. Q., & Blitzer, J. C. (2011) · Cited by 374
Face recognition vendor test 2002
Phillips, P. J., Grother, P. J., Micheals, R. J., Blackburn, D. M., Tabassi, E., & Bone, M. (2004) · IEEE International SOI Conference (SOI) · doi:10.1109/amfg.2003.1240822 · Cited by 333
Discriminative Clustering by Regularized Information Maximization
Krause, A., Perona, P., & Gomes, R. G. (2010) · neural information processing systems · Cited by 273
Unconstrained face verification using deep CNN features
Chen, J.-C., Patel, V. M., & Chellappa, R. (2016) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv.2016.7477557 · Cited by 271
Turning a Blind Eye: Explicit Removal of Biases and Variation from Deep Neural Network Embeddings
Alvi, M., Zisserman, A., & Nellåker, C. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-11009-3_34 · Cited by 228
Uncovering and Mitigating Algorithmic Bias through Learned Latent Structure
Amini, A., Soleimany, A. P., Schwarting, W., Bhatia, S. N., & Rus, D. (2019) · Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society · doi:10.1145/3306618.3314243 · Cited by 206
Information-Theoretical Learning of Discriminative Clusters for Unsupervised Domain AdaptationPreprint
Shi, Y., & Sha, F. (2012) · arXiv (Cornell University) · doi:10.48550/arxiv.1206.6438 · Cited by 204
Nicholas Furl, A., Phillips, P. J., & O'Toole, A. J. (2002) · Cognitive Science · doi:10.1016/s0364-0213(02)00084-8 · Cited by 185
An other-race effect for face recognition algorithms
Phillips, P. J., Jiang, F., Narvekar, A., Ayyad, J., & O’Toole, A. J. (2011) · ACM Transactions on Applied Perception · doi:10.1145/1870076.1870082 · Cited by 173
Face recognition using deep multi-pose representations
AbdAlmageed, W., Wu, Y., Rawls, S., Harel, S., Hassner, T., Masi, I., Choi, J., Lekust, J., Kim, J., Natarajan, P., et al. (2016) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv.2016.7477555 · Cited by 144
Semi-adversarial Networks: Convolutional Autoencoders for Imparting Privacy to Face Images
Mirjalili, V., Raschka, S., Namboodiri, A., & Ross, A. A. (2018) · International Conference on Biometrics · doi:10.1109/icb2018.2018.00023 · Cited by 129
Unsupervised Domain Adaptation for Face Recognition in Unlabeled Videos
Sohn, K., Liu, S., Zhong, G., Yu, X., Yang, M., & Chandraker, M. K. (2017) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.630 · Cited by 110
One-to-many face recognition with bilinear CNNs
RoyChowdhury, A., Lin, T., Maji, S., & Miller, E. G. (2016) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv.2016.7477593 · Cited by 107
Report on the evaluation of 2D still-image face recognition algorithms
Grother, P. J., Quinn, G. W., & Phillips, P. J. (2011) · doi:10.6028/nist.ir.7709 · Cited by 104
Privacy of Facial Soft Biometrics: Suppressing Gender But Retaining Identity
Othman, A., & Ross, A. A. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-16181-5_52 · Cited by 84
Face Search at Scale: 80 Million GalleryPreprint
Wang, D., Otto, C., & Jain, A. K. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1507.07242 · Cited by 79
Bi-Shifting Auto-Encoder for Unsupervised Domain Adaptation
Kan, M., Shan, S., & Chen, X. (2015) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.438 · Cited by 64
Mirjalili, V., Raschka, S., & Ross, A. A. (2018) · IEEE International Conference on Biometrics: Theory, Applications and Systems (BTAS) · doi:10.1109/btas.2018.8698605 · Cited by 62
The Good, the Bad, and the Ugly Face Challenge Problem
Phillips, P. J., Beveridge, J. R., Draper, B. A., Givens, G. H., O’Toole, A. J., Bolme, D. S., Dunlop, J. P., Lui, Y. M., Sahibzada, H., & Weimer, S. (2012) · Image and Vision Computing · doi:10.1016/j.imavis.2012.01.004 · Cited by 59
Focus on quality, predicting FRVT 2006 performance
Beveridge, J. R., Givens, G. H., Phillips, P. J., Draper, B. A., & Lui, Y. M. (2008) · International Conference on Automatic Face and Gesture Recognition · doi:10.1109/afgr.2008.4813375 · Cited by 55
Discriminative Clustering by Regularized Information Maximization
Gomes, R., Krause, A., & Perona, P. (2010) · CaltechAUTHORS (California Institute of Technology) · Cited by 54
Face Recognition Vendor Test (FRVT)
Grother, P., & Ngan, M. (2014) · doi:10.6028/nist.ir.8009 · Cited by 50
A compact local binary pattern using maximization of mutual information for face analysis
Jun, B., Kim, T., & Kim, D. (2010) · Pattern Recognition · doi:10.1016/j.patcog.2010.10.008 · Cited by 46
Deep Unsupervised Domain Adaptation for Face Recognition
Luo, Z., Hu, J., Deng, W., & Shen, H. (2018) · International Conference on Automatic Face and Gesture Recognition · doi:10.1109/fg.2018.00073 · Cited by 42
Self-citationTriplet Similarity Embedding for Face VerificationPreprint
Sankaranarayanan, S., Alavi, A., & Chellappa, R. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1602.03418 · Cited by 41
A Cross Benchmark Assessment of a Deep Convolutional Neural Network for Face Recognition
Phillips, P. J. (2017) · International Conference on Automatic Face and Gesture Recognition · doi:10.1109/fg.2017.89 · Cited by 24
Supervised COSMOS Autoencoder: Learning Beyond the Euclidean Loss!Preprint
Singh, M., Nagpal, S., Mayank Vatsa, A., Singh, R., & Afzel Noore, B. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1810.06221 · Cited by 4
Flagged first, then by citations
4 of the 68 references couldn’t be matched to a known paper.
Research works printed but not linked: 8
- 2015, Freebase Data Dumps
- 0, Are face recognition systems accurate? depends on your race
- 0, Amazon’s rekognition tool
- garvie, 2016, The Perpetual Line-Up Unregulated Police Face Recognition in America
- cafiero, 2006, Integrating structured biological data by kernel maximum mean discrepancy, Bioinformatics, 22, 49e, 10.1093/bioinformatics/btl242 · DOI · Paper page
- 0, Face++ Research Toolkit
- 0, Ms-celeb-1m challenge 3 Face feature test/trillion pairs
- 0, Microsoft Azure
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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
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Wang, Z., Qinami, K., Karakozis, I. C., Genova, K., Nair, P., Hata, K., & Russakovsky, O. (2020) · 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/cvpr42600.2020.00894 · Cited by 296
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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