21.2.1.2 Bias in Face Analysis, Evaluaions, Fairness

López-López, E.[Eric], Pardo, X.M.[Xosé M.], Regueiro, C.V.[Carlos V.], Iglesias, R.[Roberto], Casado, F.E.[Fernando E.],
Dataset bias exposed in face verification,
IET-Bio(8), No. 4, July 2019, pp. 249-258.
DOI Link 1906
BibRef

Georgopoulos, M.[Markos], Panagakis, Y.[Yannis], Pantic, M.[Maja],
Investigating bias in deep face analysis: The KANFace dataset and empirical study,
IVC(102), 2020, pp. 103954.
Elsevier DOI 2010
Dataset bias, Face recognition, Age estimation, Gender recognition, Kinship verification BibRef

Terhörst, P.[Philipp], Kolf, J.N.[Jan Niklas], Damer, N.[Naser], Kirchbuchner, F.[Florian], Kuijper, A.[Arjan],
Post-comparison mitigation of demographic bias in face recognition using fair score normalization,
PRL(140), 2020, pp. 332-338.
Elsevier DOI 2012
Bias, Face recognition, Biometrics, 41A05, 41A10, 65D05, 65D17 BibRef

Marks, P.[Paul],
Can the Biases in Facial Recognition Be Fixed; Also, Should They?,
CACM(64), No. 1, January 2021, pp. 20-22.
DOI Link 2103
Many facial recognition systems used by law enforcement are shot through with biases. Can anything be done to make them fair and trustworthy? BibRef

Georgopoulos, M.[Markos], Oldfield, J.[James], Nicolaou, M.A.[Mihalis A.], Panagakis, Y.[Yannis], Pantic, M.[Maja],
Mitigating Demographic Bias in Facial Datasets with Style-Based Multi-attribute Transfer,
IJCV(129), No. 7, July 2021, pp. 2288-2307. 2106
BibRef

Cheong, J.[Jiaee], Kalkan, S.[Sinan], Gunes, H.[Hatice],
The Hitchhiker's Guide to Bias and Fairness in Facial Affective Signal Processing: Overview and techniques,
SPMag(38), No. 6, November 2021, pp. 39-49.
IEEE DOI 2112
Current measurement, Signal processing algorithms, Signal analysis, Face recognition, Facial features BibRef

Booth, B.M.[Brandon M.], Hickman, L.[Louis], Subburaj, S.K.[Shree Krishna], Tay, L.[Louis], Woo, S.E.[Sang Eun], d'Mello, S.K.[Sidney K.],
Integrating Psychometrics and Computing Perspectives on Bias and Fairness in Affective Computing: A case study of automated video interviews,
SPMag(38), No. 6, November 2021, pp. 84-95.
IEEE DOI 2112
Measurement, Affective computing, Law, Psychology, Machine learning, Behavioral sciences BibRef

Jain, N.[Niharika], Olmo, A.[Alberto], Sengupta, S.[Sailik], Manikonda, L.[Lydia], Kambhampati, S.[Subbarao],
Imperfect ImaGANation: Implications of GANs exacerbating biases on facial data augmentation and snapchat face lenses,
AI(304), 2022, pp. 103652.
Elsevier DOI 2202
Generative adversarial networks (GANs), Societal impacts, Algorithmic bias, Data augmentation, Social media BibRef

Nápoles, G.[Gonzalo], Koutsoviti-Koumeri, L.[Lisa],
A fuzzy-rough uncertainty measure to discover bias encoded explicitly or implicitly in features of structured pattern classification datasets,
PRL(154), 2022, pp. 29-36.
Elsevier DOI 2202
Bias, Fairness, Explainable machine learning, Fuzzy-rough sets BibRef

Serna, I.[Ignacio], Morales, A.[Aythami], Fierrez, J.[Julian], Obradovich, N.[Nick],
Sensitive loss: Improving accuracy and fairness of face representations with discrimination-aware deep learning,
AI(305), 2022, pp. 103682.
Elsevier DOI 2203
Machine behavior, Bias, Fairness, Discrimination, Machine learning, Learning representations, Face, Biometrics BibRef

Wang, A.[Angelina], Liu, A.[Alexander], Zhang, R.[Ryan], Kleiman, A.[Anat], Kim, L.[Leslie], Zhao, D.[Dora], Shirai, I.[Iroha], Narayanan, A.[Arvind], Russakovsky, O.[Olga],
REVISE: A Tool for Measuring and Mitigating Bias in Visual Datasets,
IJCV(130), No. 7, July 2022, pp. 1790-1810.
Springer DOI 2207
BibRef
Earlier: A1, A8, A9, Only: ECCV20(III:733-751).
Springer DOI 2012
BibRef

Wang, M.[Mei], Deng, W.H.[Wei-Hong],
Adaptive Face Recognition Using Adversarial Information Network,
IP(31), 2022, pp. 4909-4921.
IEEE DOI 2208
BibRef
Earlier:
Mitigating Bias in Face Recognition Using Skewness-Aware Reinforcement Learning,
CVPR20(9319-9328)
IEEE DOI 2008
Face recognition, Prototypes, Adaptation models, Feature extraction, Reliability, Convolution, Training, graph convolution network. Training, Face recognition, Face, Learning (artificial intelligence), Training data, Databases BibRef

Wang, M.[Mei], Deng, W.H.[Wei-Hong], Hu, J.N.[Jia-Ni], Tao, X.Q.[Xun-Qiang], Huang, Y.H.[Yao-Hai],
Racial Faces in the Wild: Reducing Racial Bias by Information Maximization Adaptation Network,
ICCV19(692-702)
IEEE DOI 2004
face recognition, feature extraction, image representation, unsupervised learning, visual databases, Testing BibRef

Liu, B.Y.[Bing-Yu], Deng, W.H.[Wei-Hong], Zhong, Y.Y.[Yao-Yao], Wang, M.[Mei], Hu, J.N.[Jia-Ni], Tao, X.Q.[Xun-Qiang], Huang, Y.O.[Ya-Ohai],
Fair Loss: Margin-Aware Reinforcement Learning for Deep Face Recognition,
ICCV19(10051-10060)
IEEE DOI 2004
face recognition, learning (artificial intelligence), deep Q-learning, margin adaptive strategy, large-margin loss, Learning (artificial intelligence) BibRef

Dhar, P.[Prithviraj], Gleason, J.[Joshua], Roy, A.[Aniket], Castillo, C.D.[Carlos D.], Chellappa, R.[Rama],
PASS: Protected Attribute Suppression System for Mitigating Bias in Face Recognition,
ICCV21(15067-15076)
IEEE DOI 2203
Training, Privacy, Face recognition, Encoding, Open area test sites, Fairness, accountability, transparency, and ethics in vision, Faces BibRef

Zhao, D.[Dora], Wang, A.[Angelina], Russakovsky, O.[Olga],
Understanding and Evaluating Racial Biases in Image Captioning,
ICCV21(14810-14820)
IEEE DOI 2203
Visualization, Annotations, Image color analysis, Focusing, Manuals, Machine learning, Fairness, accountability, transparency, Vision + language BibRef

Chen, Y.[Yunliang], Joo, J.[Jungseock],
Understanding and Mitigating Annotation Bias in Facial Expression Recognition,
ICCV21(14960-14971)
IEEE DOI 2203
Annotations, Face recognition, Computational modeling, Training data, Linear programming, Data models, Fairness, Faces BibRef

Kim, E.[Eungyeup], Lee, J.[Jihyeon], Choo, J.[Jaegul],
BiaSwap: Removing Dataset Bias with Bias-Tailored Swapping Augmentation,
ICCV21(14972-14981)
IEEE DOI 2203
Training, Deep learning, Correlation, Computational modeling, Neural networks, Fairness, accountability, transparency, Image and video synthesis BibRef

Zhu, W.[Wei], Zheng, H.[Haitian], Liao, H.[Haofu], Li, W.J.[Wei-Jian], Luo, J.B.[Jie-Bo],
Learning Bias-Invariant Representation by Cross-Sample Mutual Information Minimization,
ICCV21(14982-14992)
IEEE DOI 2203
Training, Representation learning, Correlation, Training data, Estimation, Feature extraction, Minimization, Fairness, Representation learning BibRef

Shrestha, R.[Robik], Kafle, K.[Kushal], Kanan, C.[Christopher],
An Investigation of Critical Issues in Bias Mitigation Techniques,
WACV22(2512-2523)
IEEE DOI 2202
Measurement, Deep learning, Visualization, Protocols, Codes, Benchmark testing, Analysis and Understanding BibRef

Agarwal, S.[Sharat], Muku, S.[Sumanyu], Anand, S.[Saket], Arora, C.[Chetan],
Does Data Repair Lead to Fair Models? Curating Contextually Fair Data To Reduce Model Bias,
WACV22(3898-3907)
IEEE DOI 2202
Training, Neural networks, Object detection, Predictive models, Maintenance engineering, Prediction algorithms, Data models, Privacy and Ethics in Vision BibRef

Dash, S.[Saloni], Balasubramanian, V.N.[Vineeth N], Sharma, A.[Amit],
Evaluating and Mitigating Bias in Image Classifiers: A Causal Perspective Using Counterfactuals,
WACV22(3879-3888)
IEEE DOI 2202
Hair, Image color analysis, Perturbation methods, Computational modeling, Prototypes, Machine learning, GANs BibRef

Majumdar, P.[Puspita], Singh, R.[Richa], Vatsa, M.[Mayank],
Attention Aware Debiasing for Unbiased Model Prediction,
HTCV21(4116-4124)
IEEE DOI 2112
Computational modeling, Predictive models, Task analysis, Artificial intelligence BibRef

Gwilliam, M.[Matthew], Hegde, S.[Srinidhi], Tinubu, L.[Lade], Hanson, A.[Alex],
Rethinking Common Assumptions to Mitigate Racial Bias in Face Recognition Datasets,
HTCV21(4106-4115)
IEEE DOI 2112
Training, Codes, Face recognition, Buildings, Data models BibRef

Majumdar, P.[Puspita], Mittal, S.[Surbhi], Singh, R.[Richa], Vatsa, M.[Mayank],
Unravelling the Effect of Image Distortions for Biased Prediction of Pre-trained Face Recognition Models,
RPRMI21(3779-3788)
IEEE DOI 2112
Deep learning, Degradation, Analytical models, Systematics, Face recognition, Computational modeling, Nose BibRef

Barbano, C.A.[Carlo Alberto], Tartaglione, E.[Enzo], Grangetto, M.[Marco],
Bridging the gap between debiasing and privacy for deep learning,
RPRMI21(3799-3808)
IEEE DOI 2112
Deep learning, Privacy, Data privacy, Sufficient conditions, Task analysis BibRef

Ramaswamy, V.V.[Vikram V.], Kim, S.S.Y.[Sunnie S. Y.], Russakovsky, O.[Olga],
Fair Attribute Classification through Latent Space De-biasing,
CVPR21(9297-9306)
IEEE DOI 2111

WWW Link. Training, Measurement, Visualization, Correlation, Codes, Training data BibRef

Nuriel, O.[Oren], Benaim, S.[Sagie], Wolf, L.B.[Lior B.],
Permuted AdaIN: Reducing the Bias Towards Global Statistics in Image Classification,
CVPR21(9477-9486)
IEEE DOI 2111
Training, Shape, Face recognition, Transfer learning, Semantics, Benchmark testing, Robustness BibRef

Gong, S.[Sixue], Liu, X.M.[Xiao-Ming], Jain, A.K.[Anil K.],
Mitigating Face Recognition Bias via Group Adaptive Classifier,
CVPR21(3413-3423)
IEEE DOI 2111
Automation, Convolution, Face recognition, Performance gain, Benchmark testing, Robustness BibRef

Ragonesi, R.[Ruggero], Volpi, R.[Riccardo], Cavazza, J.[Jacopo], Murino, V.[Vittorio],
Learning Unbiased Representations via Mutual Information Backpropagation,
LLID21(2723-2732)
IEEE DOI 2109
Training, Face recognition, Estimation, Benchmark testing, Mutual information, Tuning BibRef

Hazirbas, C.[Caner], Bitton, J.[Joanna], Dolhansky, B.[Brian], Pan, J.[Jacqueline], Gordo, A.[Albert], Ferrer, C.C.[Cristian Canton],
Casual Conversations: A dataset for measuring fairness in AI,
RCV21(2289-2293)
IEEE DOI 2109
Analytical models, Biometrics (access control), Atmospheric measurements, Annotations, Lighting, Skin BibRef

Lu, M.[Mandy], Zhao, Q.Y.[Qing-Yu], Zhang, J.[Jiequan], Pohl, K.M.[Kilian M.], Fei-Fei, L.[Li], Niebles, J.C.[Juan Carlos], Adeli, E.[Ehsan],
Metadata Normalization,
CVPR21(10912-10922)
IEEE DOI 2111
Deep learning, Training, Measurement, Computational modeling, Face recognition, Computer architecture BibRef

Adeli, E.[Ehsan], Zhao, Q.Y.[Qing-Yu], Pfefferbaum, A.[Adolf], Sullivan, E.V.[Edith V.], Fei-Fei, L.[Li], Niebles, J.C.[Juan Carlos], Pohl, K.M.[Kilian M.],
Representation Learning with Statistical Independence to Mitigate Bias,
WACV21(2512-2522)
IEEE DOI 2106
Training, Correlation, Face recognition, Neural networks, Control systems, Data models BibRef

Kärkkäinen, K.[Kimmo], Joo, J.[Jungseock],
FairFace: Face Attribute Dataset for Balanced Race, Gender, and Age for Bias Measurement and Mitigation,
WACV21(1547-1557)
IEEE DOI 2106
Dataset, Face Recognition.
WWW Link. Training, Social networking (online), Computational modeling, Multimedia Web sites, Decision making, Media BibRef

Hwang, S.[Sunhee], Park, S.[Sungho], Lee, P.[Pilhyeon], Jeon, S.[Seogkyu], Kim, D.[Dohyung], Byun, H.R.[Hye-Ran],
Exploiting Transferable Knowledge for Fairness-aware Image Classification,
ACCV20(IV:19-35).
Springer DOI 2103
BibRef

Balakrishnan, G.[Guha], Xiong, Y.J.[Yuan-Jun], Xia, W.[Wei], Perona, P.[Pietro],
Towards Causal Benchmarking of Bias in Face Analysis Algorithms,
ECCV20(XVIII:547-563).
Springer DOI 2012
BibRef

Gong, S.[Sixue], Liu, X.M.[Xiao-Ming], Jain, A.K.[Anil K.],
Jointly De-biasing Face Recognition and Demographic Attribute Estimation,
ECCV20(XXIX: 330-347).
Springer DOI 2010
BibRef

Nagpal, S.[Shruti], Singh, M.[Maneet], Singh, R.[Richa], Vatsa, M.[Mayank],
Attribute Aware Filter-Drop for Bias-Invariant Classification,
TCV20(147-153)
IEEE DOI 2008
Deal with Bias. Task analysis, Training, Prediction algorithms, Predictive models, Face, Machine learning, Training data BibRef

Wang, Z.[Zeyu], Qinami, K.[Klint], Karakozis, I.C.[Ioannis Christos], Genova, K.[Kyle], Nair, P.[Prem], Hata, K.[Kenji], Russakovsky, O.[Olga],
Towards Fairness in Visual Recognition: Effective Strategies for Bias Mitigation,
CVPR20(8916-8925)
IEEE DOI 2008
Deal with Bias. spurious age, gender, and race correlations. Training, Task analysis, Image color analysis, Benchmark testing, Gray-scale, Correlation, Data models BibRef

Robinson, J.P., Livitz, G., Henon, Y., Qin, C., Fu, Y., Timoner, S.,
Face Recognition: Too Bias, or Not Too Bias?,
TCV20(1-10)
IEEE DOI 2008
Face, Databases, Face recognition, Sensitivity, Graphics, Benchmark testing, Rats BibRef

Peña, A., Serna, I., Morales, A., Fierrez, J.,
Bias in Multimodal AI: Testbed for Fair Automatic Recruitment,
TCV20(129-137)
IEEE DOI 2008
Recruitment, Face, Tools, Machine learning, Resumes, Training BibRef

Yucer, S.[Seyma], Tektas, F.[Furkan], Al-Moubayed, N.[Noura], Breckon, T.P.[Toby P.],
Measuring Hidden Bias within Face Recognition via Racial Phenotypes,
WACV22(3202-3211)
IEEE DOI 2202
Training, Solution design, Face recognition, Computational modeling, Skin, Task analysis, Explainable AI, Evaluation and Comparison of Vision Algorithms BibRef

Yucer, S., Akçay, S., Al-Moubayed, N., Breckon, T.P.,
Exploring Racial Bias within Face Recognition via per-subject Adversarially-Enabled Data Augmentation,
TCV20(83-92)
IEEE DOI 2008
Face recognition, Face, Training, Transforms, Machine learning, Machine learning algorithms, Mutual information BibRef

Das, A.[Abhijit], Dantcheva, A.[Antitza], Bremond, F.[Francois],
Mitigating Bias in Gender, Age and Ethnicity Classification: A Multi-task Convolution Neural Network Approach,
BEFace18(I:573-585).
Springer DOI 1905
BibRef

Sinha, S.[Sanchit], Agarwal, M.[Mohit], Vatsa, M.[Mayank], Singh, R.[Richa], Anand, S.[Saket],
Exploring Bias in Primate Face Detection and Recognition,
BEFace18(I:541-555).
Springer DOI 1905
BibRef

Nejati, H.[Hossein], Zhang, L.[Li], Sim, T.[Terence],
Eyewitness Face Sketch Recognition Based on Two-Step Bias Modeling,
CAIP13(II:26-33).
Springer DOI 1311
BibRef

Chapter on Face Recognition, Detection, Tracking, Gesture Recognition, Fingerprints, Biometrics continues in
Face Analysis, General Papers, Surveys .