Racial faces in the wild: Reducing racial bias by information maximization adaptation network

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 · see citing works

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 · see citing works

Very Deep Convolutional Networks for Large-Scale Image Recognition

Simonyan, K., & Zisserman, A. (2014) · arXiv · doi:10.48550/arxiv.1409.1556 · Cited by 74,248 · see citing works

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 · see citing works

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 · see citing works

FaceNet: 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 · see citing works

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 · see citing works

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 · see citing works

Deep Face Recognition

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 · see citing works

Unsupervised Domain Adaptation by BackpropagationPreprint

Ganin, Y., & Lempitsky, V. (2014) · arXiv (Cornell University) · doi:10.48550/arxiv.1409.7495 · Cited by 5,360 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

Untitled work

(2018) · Cited by 3,510 · see citing works

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 3,379 · see citing works

SphereFace: Deep Hypersphere Embedding for Face Recognition

Liu, W., Wen, Y., Yu, Z., Li, M., Raj, B., & Song, L. (2017) · 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.2017.713 · Cited by 3,128 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

Deep visual domain adaptation: A survey

Wang, M., & Deng, W. (2018) · Neurocomputing · doi:10.1016/j.neucom.2018.05.083 · Cited by 2,209 · see citing works

MS-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 · see citing works

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 · see citing works

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 · see citing works

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 1,629 · see citing works

Semi-supervised Learning by Entropy Minimization

Grandvalet, Y., & Bengio, Y. (2004) · Cited by 1,585 · see citing works

Deep face recognition: A survey

Wang, M., & Deng, W. (2020) · Neurocomputing · doi:10.1016/j.neucom.2020.10.081 · Cited by 1,093 · see citing works

Cross-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 · see citing works

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 · see citing works

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 595 · see citing works

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 · see citing works

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× · see citing works

Progressive 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 · see citing works

Asymmetric Tri-training for Unsupervised Domain AdaptationPreprint

Saito, K., Ushiku, Y., & Harada, T. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1702.08400 · Cited by 464 · see citing works

Learning Semantic Representations for Unsupervised Domain Adaptation

Xie, S., Zheng, Z., Chen, L., & Chen, C. (2018) · International Conference on Machine Learning · Cited by 419 · see citing works

Co-Training for Domain Adaptation

Chen, M., Weinberger, K. Q., & Blitzer, J. C. (2011) · Cited by 374 · see citing works

A Note on the Inception ScorePreprint

Barratt, S. T., & Sharma, R. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1801.01973 · Cited by 366 · see citing works

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 · see citing works

Discriminative Clustering by Regularized Information Maximization

Krause, A., Perona, P., & Gomes, R. G. (2010) · neural information processing systems · Cited by 273 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

Face recognition algorithms and the other-race effect: computational mechanisms for a developmental contact hypothesis

Nicholas Furl, A., Phillips, P. J., & O'Toole, A. J. (2002) · Cognitive Science · doi:10.1016/s0364-0213(02)00084-8 · Cited by 185 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

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 109 · see citing works

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 106 · see citing works

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 · see citing works

Face Recognition Vendor Test (FRVT)

Grother, P. J., & Ngan, M. L. (2014) · doi:10.6028/nist.ir.8009 · Cited by 103 · see citing works

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 · see citing works

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 · see citing works

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 66 · see citing works

Gender Privacy: An Ensemble of Semi Adversarial Networks for Confounding Arbitrary Gender Classifiers

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 61 · see citing works

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 · see citing works

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 · see citing works

Discriminative Clustering by Regularized Information Maximization

Gomes, R., Krause, A., & Perona, P. (2010) · CaltechAUTHORS (California Institute of Technology) · Cited by 54 · see citing works

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 · see citing works

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 · see citing works

Triplet Similarity Embedding for Face VerificationPreprint

Sankaranarayanan, S., Alavi, A., & Chellappa, R. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1602.03418 · Cited by 41 · see citing works

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 · see citing works

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 · see citing works

Deep face recognition: A survey

Wang, M., & Deng, W. (2020) · Neurocomputing · doi:10.1016/j.neucom.2020.10.081 · Cited by 1,093 · see citing works

Trustworthy 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 · see citing works

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 291 · see citing works

Towards Fairness in Visual Recognition: Effective Strategies for Bias Mitigation

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 284 · see citing works

A Call to Action on Assessing and Mitigating Bias in Artificial Intelligence Applications for Mental Health

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 · see citing works

Demographic Bias in Biometrics: A Survey on an Emerging Challenge

Drozdowski, P., Rathgeb, C., Dantcheva, A., Damer, N., & Busch, C. (2020) · IEEE Transactions on Technology and Society · doi:10.1109/tts.2020.2992344 · Cited by 263 · see citing works

Deep reinforcement learning in computer vision: a comprehensive survey

Le, N., Rathour, V. S., Yamazaki, K., Luu, K., & Savvides, M. (2021) · Artificial Intelligence Review · doi:10.1007/s10462-021-10061-9 · Cited by 265 · see citing works

EMOCA: Emotion Driven Monocular Face Capture and Animation

Daněček, R., Black, M. J., & Bolkart, T. (2022) · 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/cvpr52688.2022.01967 · Cited by 243 · see citing works

Mitigating Bias in Face Recognition Using Skewness-Aware Reinforcement Learning

Wang, M., & Deng, W. (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/cvpr42600.2020.00934 · Cited by 231 · see citing works

SensitiveNets: Learning Agnostic Representations with Application to Face Images

Morales, A., Fiérrez, J., Vera-Rodríguez, R., & Tolosana, R. (2020) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2020.3015420 · Cited by 185 · see citing works

CASIA-SURF CeFA: A Benchmark for Multi-modal Cross-ethnicity Face Anti-spoofing

Liu, A., Tan, Z., Wan, J., Escalera, S., Guo, G., & Li, S. Z. (2021) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv48630.2021.00122 · Cited by 185 · see citing works

Investigating Bias and Fairness in Facial Expression Recognition

Tian, X., White, J. C., Kalkan, S., & Güneş, H. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-65414-6_35 · Cited by 174 · see citing works

Issues Related to Face Recognition Accuracy Varying Based on Race and Skin Tone

Krishnapriya, K. S., Albiero, V., Vangara, K., King, M. C., & Bowyer, K. W. (2020) · IEEE Transactions on Technology and Society · doi:10.1109/tts.2020.2974996 · Cited by 151 · see citing works

The Elements of End-to-end Deep Face Recognition: A Survey of Recent Advances

Du, H., Shi, H., Zeng, D., Zhang, X.-P. S., & Mei, T. (2022) · ACM Computing Surveys · doi:10.1145/3507902 · Cited by 163 · see citing works

Biometrics: Trust, But Verify

Jain, A. K., Deb, D., & Engelsma, J. J. (2021) · IEEE Transactions on Biometrics Behavior and Identity Science · doi:10.1109/tbiom.2021.3115465 · Cited by 150 · see citing works

DigiFace-1M: 1 Million Digital Face Images for Face Recognition

Bae, G., Gorce, M. D. L., Baltrušaitis, T., Hewitt, C., Chen, D., Valentin, J., Cipolla, R., & Shen, J. (2023) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv56688.2023.00352 · Cited by 139 · see citing works

Human Reliance on Machine Learning Models When Performance Feedback is Limited: Heuristics and Risks

Lu, Z., & Yin, M. (2021) · Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3411764.3445562 · Cited by 131 · see citing works

Jointly De-Biasing Face Recognition and Demographic Attribute Estimation

Gong, S., Liu, X., & Jain, A. K. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-58526-6_20 · Cited by 126 · see citing works

Face Recognition: Too Bias, or Not Too Bias?

Robinson, J. P., Livitz, G., Henon, Y., Qin, C., Fu, Y., & Timoner, S. J. (2020) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw50498.2020.00008 · Cited by 125 · see citing works

Recent Advances in Deep Learning Techniques for Face Recognition

Fuad, M. T. H., Fime, A. A., Sikder, D., Iftee, M. A. R., Rabbi, J., Al-Rakhami, M. S., Gumaei, A., Sen, O., Fuad, M., & Islam, M. N. (2021) · IEEE Access · doi:10.1109/access.2021.3096136 · Cited by 122 · see citing works

Understanding and Mitigating Annotation Bias in Facial Expression Recognition

Chen, Y., & Joo, J. (2021) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.01471 · Cited by 120 · see citing works

Masked Face Recognition Challenge: The InsightFace Track Report

Deng, J., Guo, J., An, X., Zhu, Z., & Zafeiriou, S. P. (2021) · IEEE International Conference on Computer Vision Workshops · doi:10.1109/iccvw54120.2021.00165 · Cited by 117 · see citing works

Algorithmic fairness datasets: the story so far

Fabris, A., Messina, S., Silvello, G., & Susto, G. A. (2022) · Data Mining and Knowledge Discovery · doi:10.1007/s10618-022-00854-z · Cited by 128 · see citing works

Gait Recognition in the Wild: A Benchmark

(2021) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.01452 · Cited by 104 · see citing works

A Comprehensive Survey on Deep Gait Recognition: Algorithms, Datasets, and Challenges

Shen, C., Yu, S., Wang, J., Huang, G. Q., & Wang, L. (2024) · IEEE Transactions on Biometrics Behavior and Identity Science · doi:10.1109/tbiom.2024.3486345 · Cited by 112 · see citing works

Variational Prototype Learning for Deep Face Recognition

Deng, J., Guo, J., Yang, J., Lattas, A., & Zafeiriou, S. P. (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/cvpr46437.2021.01173 · Cited by 93 · see citing works

Mitigating face recognition bias via group adaptive classifier

Gong, S., Liu, X., & Jain, A. K. (2021) · 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr46437.2021.00342 · Cited by 92 · see citing works

Sensitive loss: Improving accuracy and fairness of face representations with discrimination-aware deep learning

Serna, I., Morales, A., Fiérrez, J., & Obradovich, N. (2022) · Artificial Intelligence · doi:10.1016/j.artint.2022.103682 · Cited by 88 · see citing works

Cross‐ethnicity face anti‐spoofing recognition challenge: A review

Liu, A., Li, X., Wan, J., Liang, Y., Escalera, S., Escalante, H. J., Madadi, M., Jin, Y., Wu, Z., Yu, X., et al. (2020) · IET Biometrics · doi:10.1049/bme2.12002 · Cited by 88 · see citing works

Intelligent deep learning based ethnicity recognition and classification using facial images

Sunitha, G., Geetha, K., Neelakandan, S., Pundir, A. K. S., Hemalatha, S., & Kumar, V. (2022) · Image and Vision Computing · doi:10.1016/j.imavis.2022.104404 · Cited by 84 · see citing works

Deep Polynomial Neural Networks

Chrysos, G. G., Moschoglou, S., Bouritsas, G., Deng, J., Panagakis, Y., & Zafeiriou, S. P. (2021) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2021.3058891 · Cited by 84 · see citing works

Fairness in Biometrics: A Figure of Merit to Assess Biometric Verification Systems

Pereira, T. D. F., & Marcel, S. (2021) · IEEE Transactions on Biometrics Behavior and Identity Science · doi:10.1109/tbiom.2021.3102862 · Cited by 82 · see citing works

Learning an animatable detailed 3D face model from in-the-wild images

Feng, Y., Feng, H., Black, M. J., & Bolkart, T. (2021) · ACM Transactions on Graphics · doi:10.1145/3476576.3476646 · Cited by 714 · see citing works

A causal perspective on dataset bias in machine learning for medical imaging

Jones, C., Castro, D. C., Ribeiro, F. D. S., Oktay, O., McCradden, M. D., & Glocker, B. (2024) · Nature Machine Intelligence · doi:10.1038/s42256-024-00797-8 · Cited by 87 · see citing works

Matching Algorithms: Fundamentals, Applications and Challenges

Ren, J. F., Xia, F., Chen, X., Liu, J., Hou, M., Shehzad, A. H., Sultanova, N., & Kong, X. (2021) · IEEE Transactions on Emerging Topics in Computational Intelligence · doi:10.1109/tetci.2021.3067655 · Cited by 80 · see citing works

A New Dataset for Facial Motion Analysis in Individuals With Neurological Disorders

Bandini, A., Rezaei, S., Guarín, D. L., Kulkarni, M., Lim, D., Boulos, M. I., Zinman, L. H., Yunusova, Y., & Taati, B. (2020) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2020.3019242 · Cited by 73 · see citing works

Exploring Racial Bias within Face Recognition via per-subject Adversarially-Enabled Data Augmentation

Yücer, Ş., Akçay, S., Al-Moubayed, N., & Breckon, T. P. (2020) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw50498.2020.00017 · Cited by 70 · see citing works

Meta Balanced Network for Fair Face Recognition

Wang, M., Zhang, Y., & Deng, W. (2021) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2021.3103191 · Cited by 69 · see citing works

Deep face recognition with clustering based domain adaptation

Wang, M., & Deng, W. (2020) · Neurocomputing · doi:10.1016/j.neucom.2020.02.005 · Cited by 69 · see citing works

Fair Feature Distillation for Visual Recognition

Jung, S., Lee, D.-G., Park, T., & Moon, T. (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/cvpr46437.2021.01194 · Cited by 63 · see citing works

Mitigating Demographic Bias in Facial Datasets with Style-Based Multi-attribute Transfer

Georgopoulos, M., Oldfield, J. P., Nicolaou, M. A., Panagakis, Y., & Pantić, M. (2021) · International Journal of Computer Vision · doi:10.1007/s11263-021-01448-w · Cited by 63 · see citing works

BlendFace: Re-designing Identity Encoders for Face-Swapping

Shiohara, K., Yang, X., & Taketomi, T. (2023) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.00702 · Cited by 63 · see citing works

Exclusivity-Consistency Regularized Knowledge Distillation for Face Recognition

Wang, X., Fu, T., Liao, S., Wang, S., Lei, Z., & Mei, T. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-58586-0_20 · Cited by 62 · see citing works

WebFace260M: A Benchmark for Million-Scale Deep Face Recognition

Zhu, Z. D., Huang, G., Deng, J., Ye, Y., Huang, J., Chen, X., Zhu, J., Yang, T., Du, D., Lu, J., et al. (2022) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2022.3169734 · Cited by 60 · see citing works

Fairness in Deep Learning: A Survey on Vision and Language Research

Parraga, O., Móre, M. D., Mattjie, C., Gavenski, N. S., Kupssinskü, L. S., Medronha, A., de Moura, L. V., Simões, G. S., & Barros, R. C. (2023) · ACM Computing Surveys · doi:10.1145/3637549 · Cited by 67 · see citing works

Consistent Instance False Positive Improves Fairness in Face Recognition

Xu, X., Huang, Y., Shen, P., Li, S., Li, J., Huang, F., Li, Y., & Cui, Z. (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/cvpr46437.2021.00064 · Cited by 56 · see citing works

Human-Centric Multimodal Machine Learning: Recent Advances and Testbed on AI-Based Recruitment

Peña, A., Serna, I., Morales, A., Fiérrez, J., Ortega, A., Sánchez, A. H., Saéz, M. A., & Ortega-García, J. (2023) · SN Computer Science · doi:10.1007/s42979-023-01733-0 · Cited by 62 · see citing works

You’d Better Stop! Understanding Human Reliance on Machine Learning Models under Covariate Shift

Chiang, C.-W., & Yin, M. (2021) · 13th ACM Web Science Conference 2021 · doi:10.1145/3447535.3462487 · Cited by 53 · see citing works

Quantifying Societal Bias Amplification in Image Captioning

Hirota, Y., Nakashima, Y., & García, N. (2022) · 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/cvpr52688.2022.01309 · Cited by 50 · see citing works

On Modality Bias Recognition and Reduction

Guo, Y., Nie, L., Cheng, H. H., Cheng, Z., Kankanhalli, M., & Bimbo, A. D. (2022) · ACM Transactions on Multimedia Computing Communications and Applications · doi:10.1145/3565266 · Cited by 48 · see citing works

Learning Disentangled Representation for Fair Facial Attribute Classification via Fairness-aware Information Alignment

Park, S., Hwang, S., Kim, D., & Byun, H. (2021) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v35i3.16341 · Cited by 46 · see citing works

GBDF: Gender Balanced DeepFake Dataset Towards Fair DeepFake Detection

Nadimpalli, A. V., & Rattani, A. (2023) · Lecture Notes in Computer Science · doi:10.1007/978-3-031-37742-6_25 · Cited by 46 · see citing works

One Label, One Billion Faces

Khan, Z., & Fu, Y. (2021) · ACM Conference on Fairness, Accountability, and Transparency (FAccT) · doi:10.1145/3442188.3445920 · Cited by 46 · see citing works

On responsible machine learning datasets emphasizing fairness, privacy and regulatory norms with examples in biometrics and healthcare

Mittal, S., Thakral, K., Singh, R., Mayank Vatsa, A., Glaser, T., Ferrer, C. C., & Hassner, T. (2024) · Nature Machine Intelligence · doi:10.1038/s42256-024-00874-y · Cited by 53 · see citing works

An Examination of Fairness of AI Models for Deepfake Detection

Trinh, L., & Liu, Y. (2021) · Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2021/79 · Cited by 44 · see citing works

Leveling Down in Computer Vision: Pareto Inefficiencies in Fair Deep Classifiers

Zietlow, D., Lohaus, M., Balakrishnan, G., Kleindeßner, M., Locatello, F., Schölkopf, B., & Russell, C. (2022) · 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/cvpr52688.2022.01016 · Cited by 45 · see citing works

The OARF Benchmark Suite: Characterization and Implications for Federated Learning Systems

Hu, S., Li, Y., Liu, X., Li, Q., Wu, Z., & He, B. (2022) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/3510540 · Cited by 47 · see citing works

Bias, awareness, and ignorance in deep-learning-based face recognition

Wehrli, S., Hertweck, C., Amirian, M., Glüge, S., & Stadelmann, T. (2021) · AI and Ethics · doi:10.1007/s43681-021-00108-6 · Cited by 43 · see citing works

Studying Bias in GANs Through the Lens of Race

Maluleke, V. H., Thakkar, N., Brooks, T., Weber, E., Darrell, T., Efros, A. A., Kanazawa, A., & Guillory, D. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-19778-9_20 · Cited by 42 · see citing works

Federated Learning in Computer Vision

Shenaj, D., Rizzoli, G., & Zanuttigh, P. (2023) · IEEE Access · doi:10.1109/access.2023.3310400 · Cited by 45 · see citing works

Exploring the Effects of Machine Learning Literacy Interventions on Laypeople’s Reliance on Machine Learning Models

Chiang, C.-W., & Yin, M. (2022) · 27th International Conference on Intelligent User Interfaces · doi:10.1145/3490099.3511121 · Cited by 42 · see citing works

Synthetic Generation of Face Videos with Plethysmograph Physiology

Wang, Z., Ba, Y., Chari, P., Bozkurt, O. D., Brown, G., Patwa, P., Vaddi, N., Jalilian, L., & Kadambi, A. (2022) · 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/cvpr52688.2022.01993 · Cited by 41 · see citing works

Biased Face Recognition Technology Used by Government: A Problem for Liberal Democracy

Gentzel, M. (2021) · Philosophy & Technology · doi:10.1007/s13347-021-00478-z · Cited by 38 · see citing works

FairFace Challenge at ECCV 2020: Analyzing Bias in Face Recognition

Sixta, T., Jacques, J. C. S., Buch-Cardona, P., Vazquez, E., & Escalera, S. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-65414-6_32 · Cited by 38 · see citing works

Rethinking Common Assumptions to Mitigate Racial Bias in Face Recognition Datasets

Gwilliam, M., Hegde, S., Tinubu, L., & Hanson, A. (2021) · 2021 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW) · doi:10.1109/iccvw54120.2021.00458 · Cited by 41 · see citing works

Face Recognition Depends on Specialized Mechanisms Tuned to View‐Invariant Facial Features: Insights from Deep Neural Networks Optimized for Face or Object Recognition

Abudarham, N., Grosbard, I. D., & Yovel, G. (2021) · Cognitive Science · doi:10.1111/cogs.13031 · Cited by 38 · see citing works

Gender and Racial Bias in Visual Question Answering Datasets

Hirota, Y., Nakashima, Y., & García, N. (2022) · ACM Conference on Fairness, Accountability, and Transparency (FAccT) · doi:10.1145/3531146.3533184 · Cited by 39 · see citing works

A Classification of Arab Ethnicity Based on Face Image Using Deep Learning Approach

Al-Humaidan, N. A., & Prince, M. (2021) · IEEE Access · doi:10.1109/access.2021.3069022 · Cited by 35 · see citing works

PASS: Protected Attribute Suppression System for Mitigating Bias in Face Recognition

Dhar, P., Gleason, J., Roy, A., Castillo, C. D., & Chellappa, R. (2021) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.01481 · Cited by 35 · see citing works

The Hitchhiker’s Guide to Bias and Fairness in Facial Affective Signal Processing: Overview and techniques

Cheong, J., Kalkan, S., & Güneş, H. (2021) · IEEE Signal Processing Magazine · doi:10.1109/msp.2021.3106619 · Cited by 35 · see citing works

FairGRAPE: Fairness-Aware GRAdient Pruning mEthod for Face Attribute Classification

Lin, X., Kim, S., & Joo, J. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-19778-9_24 · Cited by 35 · see citing works

CASIA-Face-Africa: A Large-Scale African Face Image Database

Muhammad, J., Wang, Y., Wang, C., Zhang, K., & Sun, Z. (2021) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2021.3080496 · Cited by 32 · see citing works

Accurate 3D Face Reconstruction with Facial Component Tokens

Zhang, T., Chu, X., Liu, Y., Lin, L., Yang, Z., Xu, Z., Cao, C., Yu, F. R., Zhou, C., Yuan, C., et al. (2023) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.00829 · Cited by 32 · see citing works

Mitigate Bias in Face Recognition using Skewness-Aware Reinforcement LearningPreprint

Wang, M., & Deng, W. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1911.10692 · Cited by 32 · see citing works

Balancing Biases and Preserving Privacy on Balanced Faces in the Wild

Robinson, J. P., Qin, C., Henon, Y., Timoner, S. J., & Fu, Y. (2023) · IEEE Transactions on Image Processing · doi:10.1109/tip.2023.3282837 · Cited by 31 · see citing works

Mitigating Dataset Harms Requires Stewardship: Lessons from 1000 Papers

Peng, K., Mathur, A., & Narayanan, A. (2021) · arXiv · doi:10.48550/arxiv.2108.02922 · Cited by 31 · see citing works

Model compression techniques in biometrics applications: A survey

Caldeira, E., Neto, P. C., Huber, M., Damer, N., & Sequeira, A. F. (2024) · Information Fusion · doi:10.1016/j.inffus.2024.102657 · Cited by 29 · see citing works

Class-Balanced Training for Deep Face Recognition

Zhang, Y., & Deng, W. (2020) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw50498.2020.00420 · Cited by 29 · see citing works

Open-Source Face Recognition Frameworks: A Review of the Landscape

Wanyonyi, D., & Çelik, T. (2022) · IEEE Access · doi:10.1109/access.2022.3170037 · Cited by 32 · see citing works

Unsupervised Learning of Debiased Representations with Pseudo-Attributes

Seo, S., Lee, J., & Han, B. (2022) · 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/cvpr52688.2022.01624 · Cited by 29 · see citing works

Measuring Hidden Bias within Face Recognition via Racial Phenotypes

Yücer, Ş., Tektas, F., Moubayed, N. A., & Breckon, T. P. (2022) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv51458.2022.00326 · Cited by 27 · see citing works

Anatomizing Bias in Facial Analysis

Singh, R., Majumdar, P., Mittal, S., & Mayank Vatsa, A. (2022) · AAAI Publications (The Association for the Advancement of Artificial Intelligence (AAAI)) · doi:10.1609/aaai.v36i11.21500 · Cited by 27 · see citing works

Survey on the Analysis and Modeling of Visual Kinship: A Decade in the Making

Robinson, J. P., Shao, M., & Fu, Y. (2021) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2021.3063078 · Cited by 26 · see citing works

Cross-Domain Similarity Learning for Face Recognition in Unseen Domains

Faraki, M., Yu, X., Tsai, Y., Suh, Y., & Chandraker, M. K. (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/cvpr46437.2021.01504 · Cited by 26 · see citing works

Boosting Semi-Supervised Face Recognition With Noise Robustness

Liu, Y., Shi, H., Du, H., Zhu, R., Wang, J., Zheng, L., & Mei, T. (2021) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2021.3068749 · Cited by 26 · see citing works

Learning Fair Classifiers with Partially Annotated Group Labels

Jung, S., Chun, S., & Moon, T. (2022) · 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/cvpr52688.2022.01010 · Cited by 26 · see citing works

Towards Racially Unbiased Skin Tone Estimation via Scene Disambiguation

Feng, H., Bolkart, T., Tesch, J., Black, M. J., & Abrevaya, V. F. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-19778-9_5 · Cited by 25 · see citing works

Recognizing Human Races through Machine Learning—A Multi-Network, Multi-Features Study

Darabant, A. S., Borza, D., & Danescu, R. (2021) · Mathematics · doi:10.3390/math9020195 · Cited by 24 · see citing works

Using Graph Neural Networks to Improve Generalization Capability of the Models for Deepfake Detection

She, H., Hu, Y., Liu, B., Li, J., & Li, C.-T. (2024) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2024.3451356 · Cited by 24 · see citing works

FRoundation: Are foundation models ready for face recognition?

Chettaoui, T., Damer, N., & Boutros, F. (2025) · Image and Vision Computing · doi:10.1016/j.imavis.2025.105453 · Cited by 24 · see citing works

Beyond Algorithmic Bias: A Socio-Computational Interrogation of the Google Search by Image Algorithm

Papakyriakopoulos, O., & Mboya, A. M. (2022) · Social Science Computer Review · doi:10.1177/08944393211073169 · Cited by 23 · see citing works

Learning to Learn across Diverse Data Biases in Deep Face Recognition

Liu, C., Yu, X., Tsai, Y., Faraki, M., Moslemi, R., Chandraker, M. K., & Fu, Y. (2022) · 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/cvpr52688.2022.00404 · Cited by 23 · see citing works

Child face recognition at scale: synthetic data generation and performance benchmark

Falkenberg, M., Ottsen, A. B., Ibsen, M., & Rathgeb, C. (2024) · Frontiers in Signal Processing · doi:10.3389/frsip.2024.1308505 · Cited by 23 · see citing works

DebFace: De-biasing Face RecognitionPreprint

Gong, S., Liu, X., & Jain, A. K. (2019) · arXiv (Cornell University) · Cited by 23 · see citing works

Race estimation with deep networks

Ahmed, M. A., Choudhury, R. D., & Kashyap, K. (2020) · Journal of King Saud University - Computer and Information Sciences · doi:10.1016/j.jksuci.2020.11.029 · Cited by 23 · see citing works

Federated Unsupervised Domain Adaptation for Face Recognition

Zhuang, W., Gan, X., Zhang, X., Wen, Y., Zhang, S., & Yi, S. (2022) · IEEE International Conference on Multimedia and Expo (ICME) · doi:10.1109/icme52920.2022.9859587 · Cited by 21 · see citing works

Synthetic Data for the Mitigation of Demographic Biases in Face Recognition

Melzi, P., Rathgeb, C., Tolosana, R., Vera-Rodríguez, R., Morales, A., Lawatsch, D., Domin, F., & Schaubert, M. (2023) · IEEE International Joint Conference on Biometrics (IJCB) · doi:10.1109/ijcb57857.2023.10449034 · Cited by 21 · see citing works

Feel the Bite: Robot-Assisted Inside-Mouth Bite Transfer using Robust Mouth Perception and Physical Interaction-Aware Control

Jenamani, R. K., Stabile, D., Liu, Z., Anwar, A., Dimitropoulou, K. A., & Bhattacharjee, T. (2024) · ACM/IEEE International Conference on Human-Robot Interaction (HRI) · doi:10.1145/3610977.3634975 · Cited by 21 · see citing works

Uncertainty-aware Cross-dataset Facial Expression Recognition via Regularized Conditional Alignment

Zhou, L., Fan, X., Ma, Y., Tjahjadi, T., & Ye, Q. (2020) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3394171.3413515 · Cited by 20 · see citing works

Deep Cross-Species Feature Learning for Animal Face Recognition via Residual Interspecies Equivariant Network

Shi, X. F., Yang, C., Xia, X., & Chai, X. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-58583-9_40 · Cited by 20 · see citing works

Racial Faces in-the-Wild: Reducing Racial Bias by Deep Unsupervised Domain Adaptation.Preprint

Wang, M., Deng, W., Hu, J., Peng, J., Tao, X., & Huang, Y. (2018) · arXiv (Cornell University) · Cited by 19 · see citing works

RamFace: Race Adaptive Margin Based Face Recognition for Racial Bias Mitigation

Yang, Z., Zhu, X., Jiang, C., Liu, W., & Shen, L. (2021) · IEEE International Joint Conference on Biometrics (IJCB) · doi:10.1109/ijcb52358.2021.9484352 · Cited by 17 · see citing works

Mitigating Bias in Face Recognition Using Skewness-Aware Reinforcement Learning

Wang, M., & Deng, W. (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/cvpr42600.2020.00934 · Cited by 231 · see citing works

Mitigate Bias in Face Recognition using Skewness-Aware Reinforcement LearningPreprint

Wang, M., & Deng, W. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1911.10692 · Cited by 32 · see citing works

Gradient Attention Balance Network: Mitigating Face Recognition Racial Bias via Gradient Attention

Huang, L., Wang, M., Liang, J., Deng, W., Shi, H., Wen, D., Zhang, Y., & Zhao, J. G. (2023) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw59228.2023.00009 · Cited by 14 · see citing works