22.2.1.2 Bias in Face Analysis, Evaluaions, Fairness

Chapter Contents (Back)
2012
Face Recognition. Bias in Recognition.

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

Zhao, Y.Y.[Yu-Ying], Deng, W.H.[Wei-Hong],
Dual Gaussian Modeling for Deep Face Embeddings,
PRL(161), 2022, pp. 74-81.
Elsevier DOI 2209
Model as a Gaussian distribution. Face recognition, Data uncertainty, Gaussian distribution BibRef

Wang, M.[Mei], Zhang, Y.[Yaobin], Deng, W.H.[Wei-Hong],
Meta Balanced Network for Fair Face Recognition,
PAMI(44), No. 11, November 2022, pp. 8433-8448.
IEEE DOI 2210
Skin, Face recognition, Training, Training data, Metadata, Databases, Adaptation models, Fairness with respect to skin tone, face recognition BibRef

Fabbrizzi, S.[Simone], Papadopoulos, S.[Symeon], Ntoutsi, E.[Eirini], Kompatsiaris, I.[Ioannis],
A survey on bias in visual datasets,
CVIU(223), 2022, pp. 103552.
Elsevier DOI 2210
Survey, Dataset Bias. Computer vision, Visual datasets, Bias, AI ethics BibRef

Shen, X.D.[Xu-Dong], Wong, Y.K.[Yong-Kang], Kankanhalli, M.S.[Mohan S.],
Fair Representation: Guaranteeing Approximate Multiple Group Fairness for Unknown Tasks,
PAMI(45), No. 1, January 2023, pp. 525-538.
IEEE DOI 2212
TV, Task analysis, Calibration, Statistics, Sociology, Taxonomy, Fair representation, group fairness, fair machine learning BibRef

Cui, S.[Sen], Pan, W.[Weishen], Zhang, C.S.[Chang-Shui], Wang, F.[Fei],
Bipartite Ranking Fairness Through a Model Agnostic Ordering Adjustment,
PAMI(45), No. 11, November 2023, pp. 13235-13249.
IEEE DOI 2310
BibRef

Majumdar, P.[Puspita], Vatsa, M.[Mayank], Singh, R.[Richa],
Uniform misclassification loss for unbiased model prediction,
PR(144), 2023, pp. 109689.
Elsevier DOI 2310
Bias, Fairness, Facial attribute prediction, Deep learning, Unbiased predictions BibRef

Gao, L.Y.[Li-Ying], Niu, K.[Kai], Jiao, B.L.[Bing-Liang], Wang, P.[Peng], Zhang, Y.N.[Yan-Ning],
Addressing Information Inequality for Text-Based Person Search via Pedestrian-Centric Visual Denoising and Bias-Aware Alignments,
CirSysVideo(33), No. 12, December 2023, pp. 7884-7899.
IEEE DOI 2312
BibRef


Meister, N.[Nicole], Zhao, D.[Dora], Wang, A.[Angelina], Ramaswamy, V.V.[Vikram V.], Fong, R.[Ruth], Russakovsky, O.[Olga],
Gender Artifacts in Visual Datasets,
ICCV23(4814-4825)
IEEE DOI 2401
BibRef

Aniraj, A.[Ananthu], Dantas, C.F.[Cassio F.], Ienco, D.[Dino], Marcos, D.[Diego],
Masking Strategies for Background Bias Removal in Computer Vision Models,
OutDistri23(4399-4407)
IEEE DOI 2401
BibRef

Rosales, R.[Rafael], Munoz, P.[Pablo], Paulitsch, M.[Michael],
Assessing the Impact of Diversity on the Resilience of Deep Learning Ensembles: A Comparative Study on Model Architecture, Output, Activation, and Attribution,
OutDistri23(4408-4418)
IEEE DOI 2401
BibRef

Shrivastava, S.[Shubham], Zhang, X.L.[Xian-Ling], Nagesh, S.[Sushruth], Parchami, A.[Armin],
DatasetEquity: Are All Samples Created Equal? In The Quest For Equity Within Datasets,
OutDistri23(4419-4428)
IEEE DOI Code:
WWW Link. 2401
BibRef

Choithwani, M.[Mohit], Almeida, S.[Sneha], Egger, B.[Bernhard],
PoseBias: On Dataset Bias and Task Difficulty - Is there an Optimal Camera Position for Facial Image Analysis?,
AMFG23(3088-3096)
IEEE DOI 2401
BibRef

Brinkmann, J.[Jannik], Swoboda, P.[Paul], Bartelt, C.[Christian],
A Multidimensional Analysis of Social Biases in Vision Transformers,
ICCV23(4891-4900)
IEEE DOI 2401
BibRef

Li, J.X.[Jia-Xuan], Vo, D.M.[Duc Minh], Nakayama, H.[Hideki],
Partition-and-Debias: Agnostic Biases Mitigation via A Mixture of Biases-Specific Experts,
ICCV23(4901-4911)
IEEE DOI Code:
WWW Link. 2401
BibRef

Liang, H.[Hao], Perona, P.[Pietro], Balakrishnan, G.[Guha],
Benchmarking Algorithmic Bias in Face Recognition: An Experimental Approach Using Synthetic Faces and Human Evaluation,
ICCV23(4954-4964)
IEEE DOI 2401
BibRef

Li, Z.X.[Ze-Xi], Shang, X.[Xinyi], He, R.[Rui], Lin, T.[Tao], Wu, C.[Chao],
No Fear of Classifier Biases: Neural Collapse Inspired Federated Learning with Synthetic and Fixed Classifier,
ICCV23(5296-5306)
IEEE DOI Code:
WWW Link. 2401
BibRef

Ou, F.Z.[Fu-Zhao], Chen, B.[Baoliang], Li, C.Y.[Chong-Yi], Wang, S.Q.[Shi-Qi], Kwong, S.[Sam],
Troubleshooting Ethnic Quality Bias with Curriculum Domain Adaptation for Face Image Quality Assessment,
ICCV23(20661-20672)
IEEE DOI 2401
BibRef

Abduh, L.[Latifah], Ivrissimtzis, I.[Ioannis],
Race Bias Analysis of Bona Fide Errors in Face Anti-spoofing,
CAIP23(II:23-32).
Springer DOI 2312
BibRef

Tang, P.W.[Peng-Wei], Yao, W.[Wei], Li, Z.[Zhicong], Liu, Y.[Yong],
Fair Scratch Tickets: Finding Fair Sparse Networks without Weight Training,
CVPR23(24406-24416)
IEEE DOI 2309
BibRef

Garcia, N.[Noa], Hirota, Y.[Yusuke], Wu, Y.[Yankun], Nakashima, Y.[Yuta],
Uncurated Image-Text Datasets: Shedding Light on Demographic Bias,
CVPR23(6957-6966)
IEEE DOI 2309
BibRef

Huang, L.[Linzhi], Wang, M.[Mei], Liang, J.H.[Jia-Hao], Deng, W.H.[Wei-Hong], Shi, H.Z.[Hong-Zhi], Wen, D.C.[Dong-Chao], Zhang, Y.J.[Ying-Jie], Zhao, J.[Jian],
Gradient Attention Balance Network: Mitigating Face Recognition Racial Bias via Gradient Attention,
FaDE-TCV23(38-47)
IEEE DOI 2309
BibRef

Wu, H.[Haiyu], Albiero, V.[Vítor], Krishnapriya, K.S., King, M.C.[Michael C.], Bowyer, K.W.[Kevin W.],
Face Recognition Accuracy Across Demographics: Shining a Light Into the Problem,
Biometrics23(1041-1050)
IEEE DOI 2309
BibRef

Mittal, S.[Surbhi], Thakral, K.[Kartik], Majumdar, P.[Puspita], Vatsa, M.[Mayank], Singh, R.[Richa],
Are Face Detection Models Biased?,
FG23(1-7)
IEEE DOI 2303
Location awareness, Analytical models, Annotations, Demography, Face recognition, Pipelines, Gesture recognition BibRef

Li, J.Z.[Jia-Zhi], Abd-Almageed, W.[Wael],
Information-Theoretic Bias Assessment Of Learned Representations Of Pretrained Face Recognition,
FG21(1-8)
IEEE DOI 2303
Measurement, Deep learning, Correlation, Face recognition, Neural networks, Gesture recognition BibRef

Cheong, J.[Jiaee], Kalkan, S.[Sinan], Gunes, H.[Hatice],
Causal Structure Learning of Bias for Fair Affect Recognition,
DVPBA23(340-349)
IEEE DOI 2302
Emotion recognition, Face recognition, Conferences, Closed box, Machine learning, Predictive models, Prediction algorithms BibRef

Bhatta, A.[Aman], Albiero, V.[Vítor], Bowyer, K.W.[Kevin W.], King, M.C.[Michael C.],
The Gender Gap in Face Recognition Accuracy Is a Hairy Problem,
DVPBA23(1-10)
IEEE DOI 2302
Hair, Face recognition BibRef

Kolla, M.[Manideep], Savadamuthu, A.[Aravinth],
The Impact of Racial Distribution in Training Data on Face Recognition Bias: A Closer Look,
DVPBA23(313-322)
IEEE DOI 2302
Measurement, Training, Image quality, Correlation, Face recognition, Training data, Clustering algorithms BibRef

Zhang, Y.F.[Yan-Fu], Gao, S.Q.[Shang-Qian], Huang, H.[Heng],
Recover Fair Deep Classification Models via Altering Pre-trained Structure,
ECCV22(XIII:481-498).
Springer DOI 2211
BibRef

Peychev, M.[Momchil], Ruoss, A.[Anian], Balunovic, M.[Mislav], Baader, M.[Maximilian], Vechev, M.[Martin],
Latent Space Smoothing for Individually Fair Representations,
ECCV22(XIII:535-554).
Springer DOI 2211
BibRef

Li, Z.H.[Zhi-Heng], Hoogs, A.[Anthony], Xu, C.L.[Chen-Liang],
Discover and Mitigate Unknown Biases with Debiasing Alternate Networks,
ECCV22(XIII:270-288).
Springer DOI 2211
BibRef

Chouldechova, A.[Alexandra], Deng, S.Q.[Si-Qi], Wang, Y.X.[Yong-Xin], Xia, W.[Wei], Perona, P.[Pietro],
Unsupervised and Semi-supervised Bias Benchmarking in Face Recognition,
ECCV22(XIII:289-306).
Springer DOI 2211
BibRef

Maluleke, V.H.[Vongani H.], Thakkar, N.[Neerja], Brooks, T.[Tim], Weber, E.[Ethan], Darrell, T.J.[Trevor J.], Efros, A.A.[Alexei A.], Kanazawa, A.[Angjoo], Guillory, D.[Devin],
Studying Bias in GANs Through the Lens of Race,
ECCV22(XIII:344-360).
Springer DOI 2211
BibRef

Shrestha, R.[Robik], Kafle, K.[Kushal], Kanan, C.[Christopher],
OccamNets: Mitigating Dataset Bias by Favoring Simpler Hypotheses,
ECCV22(XX:702-721).
Springer DOI 2211
BibRef

Jung, S.[Sangwon], Chun, S.[Sanghyuk], Moon, T.[Taesup],
Learning Fair Classifiers with Partially Annotated Group Labels,
CVPR22(10338-10347)
IEEE DOI 2210

WWW Link. Training, Measurement, Learning systems, Privacy, Codes, Annotations, Transparency, fairness, accountability, privacy and ethics in vision BibRef

Wang, Z.B.[Zhi-Bo], Dong, X.W.[Xiao-Wei], Xue, H.[Henry], Zhang, Z.F.[Zhi-Fei], Chiu, W.F.[Wei-Feng], Wei, T.[Tao], Ren, K.[Kui],
Fairness-aware Adversarial Perturbation Towards Bias Mitigation for Deployed Deep Models,
CVPR22(10369-10378)
IEEE DOI 2210
Training, Degradation, Adaptation models, Perturbation methods, Feature extraction, Generators, Pattern recognition, Transparency, privacy and ethics in vision BibRef

Wang, X.D.[Xu-Dong], Wu, Z.R.[Zhi-Rong], Lian, L.[Long], Yu, S.X.[Stella X.],
Debiased Learning from Naturally Imbalanced Pseudo-Labels,
CVPR22(14627-14637)
IEEE DOI 2210
Training, Representation learning, Adaptation models, Codes, Annotations, Semisupervised learning, Vision+language BibRef

Seo, S.[Seonguk], Lee, J.Y.[Joon-Young], Han, B.H.[Bo-Hyung],
Unsupervised Learning of Debiased Representations with Pseudo-Attributes,
CVPR22(16721-16730)
IEEE DOI 2210
Correlation, Codes, Annotations, Computational modeling, Machine learning, Benchmark testing, Representation learning, privacy and ethics in vision BibRef

Ardeshir, S.[Shervin], Segalin, C.[Cristina], Kallus, N.[Nathan],
Estimating Structural Disparities for Face Models,
CVPR22(10348-10357)
IEEE DOI 2210
Training, Analytical models, Computational modeling, Estimation, Machine learning, Predictive models, Transparency, fairness, Face and gestures BibRef

Agarwal, C.[Chirag], d'Souza, D.[Daniel], Hooker, S.[Sara],
Estimating Example Difficulty using Variance of Gradients,
CVPR22(10358-10368)
IEEE DOI 2210
Measurement, Ethics, Computational modeling, Machine learning, Inspection, Human in the loop, Transparency, fairness, privacy and ethics in vision BibRef

del Grosso, G.[Ganesh], Jalalzai, H.[Hamid], Pichler, G.[Georg], Palamidessi, C.[Catuscia], Piantanida, P.[Pablo],
Leveraging Adversarial Examples to Quantify Membership Information Leakage,
CVPR22(10389-10399)
IEEE DOI 2210
Training, Data privacy, Perturbation methods, Training data, Machine learning, Data models, Transparency, fairness, Machine learning BibRef

Sirotkin, K.[Kirill], Carballeira, P.[Pablo], Escudero-Viñolo, M.[Marcos],
A study on the distribution of social biases in self-supervised learning visual models,
CVPR22(10432-10441)
IEEE DOI 2210
Training, Visualization, Schedules, Computational modeling, Transfer learning, Supervised learning, Training data, Self- semi- meta- Transfer/low-shot/long-tail learning BibRef

Liu, R.[Ruyang], Liu, H.[Hao], Li, G.[Ge], Hou, H.[Haodi], Yu, T.[TingHao], Yang, T.[Tao],
Contextual Debiasing for Visual Recognition with Causal Mechanisms,
CVPR22(12745-12755)
IEEE DOI 2210
Charge coupled devices, Training, Adaptation models, Visualization, Target recognition, Predictive models, Visual reasoning, Scene analysis and understanding BibRef

Jeon, M.[Myeongho], Kim, D.[Daekyung], Lee, W.[Woochul], Kang, M.[Myungjoo], Lee, J.[Joonseok],
A Conservative Approach for Unbiased Learning on Unknown Biases,
CVPR22(16731-16739)
IEEE DOI 2210
Representation learning, Deep learning, Machine vision, Training data, Distributed databases, Data models, Robustness, Vision applications and systems BibRef

Teney, D.[Damien], Abbasnejad, E.[Ehsan], Lucey, S.[Simon], van den Hengel, A.J.[Anton J.],
Evading the Simplicity Bias: Training a Diverse Set of Models Discovers Solutions with Superior OOD Generalization,
CVPR22(16740-16751)
IEEE DOI 2210
Training, Representation learning, Deep learning, Visualization, Correlation, Neural networks, Robustness, Representation learning BibRef

Lee, S.[Seongmin], Hoffman, J.[Judy], Wang, Z.J.J.[Zi-Jie J.], Chau, D.H.[Duen Horng],
VIsCUIT: Visual Auditor for Bias in CNN Image Classifier,
CVPR22(21443-21451)
IEEE DOI 2210
Visualization, Computational modeling, Neurons, Data visualization, Computer architecture, Browsers BibRef

Stone, R.S.[Rebecca S], Ravikumar, N.[Nishant], Bulpitt, A.J.[Andrew J], Hogg, D.C.[David C],
Epistemic Uncertainty-Weighted Loss for Visual Bias Mitigation,
FaDE-TCV22(2897-2904)
IEEE DOI 2210
Training, Visualization, Uncertainty, Correlation, Neural networks, Training data, Mathematical models BibRef

Siddiqui, H.[Hera], Rattani, A.[Ajita], Ricanek, K.[Karl], Hill, T.[Twyla],
An Examination of Bias of Facial Analysis based BMI Prediction Models,
FaDE-TCV22(2925-2934)
IEEE DOI 2210
Obesity, Error analysis, Face recognition, Psychology, Predictive models, Market research, Public healthcare BibRef

Jaipuria, N.[Nikita], Stevo, K.[Katherine], Zhang, X.L.[Xian-Ling], Gaopande, M.L.[Meghana L.], Garcia, I.C.[Ian Calle], Jain, J.[Jinesh], Murali, V.N.[Vidya N.],
deepPIC: Deep Perceptual Image Clustering For Identifying Bias In Vision Datasets,
VDU22(4792-4801)
IEEE DOI 2210
Analytical models, Visualization, Annotations, Pipelines, Buildings, Data visualization BibRef

Li, Z.H.[Zhi-Heng], Xu, C.L.[Chen-Liang],
Discover the Unknown Biased Attribute of an Image Classifier,
ICCV21(14950-14959)
IEEE DOI 2203
Pipelines, Predictive models, Prediction algorithms, Linear programming, Classification algorithms, Explainable AI 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 .


Last update:Mar 16, 2024 at 20:36:19