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Dataset bias, Face recognition, Age estimation,
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Bias, Face recognition, Biometrics, 41A05, 41A10, 65D05, 65D17
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IEEE DOI
2112
Current measurement, Signal processing algorithms,
Signal analysis, Face recognition, Facial features
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Subburaj, S.K.[Shree Krishna],
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Integrating Psychometrics and Computing Perspectives on Bias and
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IEEE DOI
2112
Measurement, Affective computing, Law, Psychology, Machine learning,
Behavioral sciences
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Elsevier DOI
2202
Generative adversarial networks (GANs), Societal impacts,
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2202
Bias, Fairness, Explainable machine learning, Fuzzy-rough sets
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Elsevier DOI
2203
Machine behavior, Bias, Fairness, Discrimination,
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Springer DOI
2207
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Earlier: A1, A8, A9, Only:
ECCV20(III:733-751).
Springer DOI
2012
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Wang, M.[Mei],
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Adaptive Face Recognition Using Adversarial Information Network,
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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
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Tao, X.Q.[Xun-Qiang],
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Racial Faces in the Wild: Reducing Racial Bias by Information
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ICCV19(692-702)
IEEE DOI
2004
face recognition, feature extraction, image representation,
unsupervised learning, visual databases, Testing
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Fair Loss: Margin-Aware Reinforcement Learning for Deep Face
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ICCV19(10051-10060)
IEEE DOI
2004
face recognition, learning (artificial intelligence),
deep Q-learning, margin adaptive strategy, large-margin loss,
Learning (artificial intelligence)
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Model as a Gaussian distribution.
Face recognition, Data uncertainty, Gaussian distribution
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Meta Balanced Network for Fair Face Recognition,
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IEEE DOI
2210
Skin, Face recognition, Training, Training data, Metadata, Databases,
Adaptation models, Fairness with respect to skin tone, face recognition
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Elsevier DOI
2210
Survey, Dataset Bias. Computer vision, Visual datasets, Bias, AI ethics
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IEEE DOI
2212
TV, Task analysis, Calibration, Statistics, Sociology, Taxonomy,
Fair representation, group fairness, fair machine learning
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Bipartite Ranking Fairness Through a Model Agnostic Ordering
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IEEE DOI
2310
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Elsevier DOI
2310
Bias, Fairness, Facial attribute prediction, Deep learning, Unbiased predictions
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Gao, L.Y.[Li-Ying],
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Jiao, B.L.[Bing-Liang],
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Addressing Information Inequality for Text-Based Person Search via
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IEEE DOI
2312
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2406
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Chen, Y.Y.[Yi-Yang],
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Elsevier DOI
2408
Neural network, Class activate map, Ethnicity identification,
Face perception, Cognitive science
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Sukumar, A.[Aadith],
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Training Against Disguises: Addressing and Mitigating Bias in Facial
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FG24(1-6)
IEEE DOI
2408
Access control, Training, Emotion recognition, Face recognition,
Prevention and mitigation, Benchmark testing, security
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Aminou, L.[Loubna],
Daaif, A.[Abdelaziz],
Soulami, M.[Maha],
Chalfaouat, A.[Abderrahim],
Youssfi, M.[Mohamed],
Converging human and algorithmic biases in the hiring decision-making
process,
ISCV24(1-5)
IEEE DOI
2408
Ethics, Sensitivity, Reviews, Decision making,
Knowledge based systems, Training data, Human factors, algorithmic biases
BibRef
Elobaid, A.[Alaa],
Ramoly, N.[Nathan],
Younes, L.[Lara],
Papadopoulos, S.[Symeon],
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FG24(1-9)
IEEE DOI Code:
WWW Link.
2408
Codes, Accuracy, Error analysis, Face recognition,
Current measurement, Measurement uncertainty, Focusing
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Bhatta, A.[Aman],
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Impact of Blur and Resolution on Demographic Disparities in 1-to-Many
Facial Identification,
VAQuality24(422-430)
IEEE DOI
2404
Measurement, Image resolution, Image recognition, Surveillance,
Face recognition, Government
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D'Incŕ, M.[Moreno],
Tzelepis, C.[Christos],
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Improving Fairness using Vision-Language Driven Image Augmentation,
WACV24(4683-4692)
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WWW Link.
2404
Training, Measurement, Correlation, Image color analysis, Semantics,
Natural languages, Skin, Algorithms, Explainable, fair, accountable.
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Liang, H.[Hao],
Caro, J.O.[Josue Ortega],
Maheshri, V.[Vikram],
Patel, A.B.[Ankit B.],
Balakrishnan, G.[Guha],
Linking convolutional kernel size to generalization bias in face
analysis CNNs,
WACV24(4693-4703)
IEEE DOI
2404
Training, Perturbation methods, Neural networks,
Computer architecture, Size measurement,
Social good
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Arora, P.[Piyush],
Mazumder, P.[Pratik],
Hybrid Sample Synthesis-based Debiasing of Classifier in Limited Data
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WACV24(i-ix)
IEEE DOI
2404
Deep learning, Training data, Predictive models, Benchmark testing,
Data models, Algorithms, Explainable, fair, accountable,
ethical computer vision
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Capitani, G.[Giacomo],
Bolelli, F.[Federico],
Porrello, A.[Angelo],
Calderara, S.[Simone],
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ClusterFix: A Cluster-Based Debiasing Approach without
Protected-Group Supervision,
WACV24(4858-4867)
IEEE DOI
2404
Ethics, Risk minimization, Clustering algorithms,
Benchmark testing, Encoding, Object recognition, Algorithms,
Biomedical / healthcare / medicine
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Huber, M.[Marco],
Luu, A.T.[Anh Thi],
Boutros, F.[Fadi],
Kuijper, A.[Arjan],
Damer, N.[Naser],
Bias and Diversity in Synthetic-based Face Recognition,
WACV24(6203-6214)
IEEE DOI
2404
Training, Ethics, Head, Law, Face recognition, Computational modeling,
Training data, Algorithms, Biometrics, face, gesture, body pose
BibRef
Clemmer, C.[Colton],
Ding, J.H.[Jun-Hua],
Feng, Y.H.[Yun-He],
PreciseDebias: An Automatic Prompt Engineering Approach for
Generative AI to Mitigate Image Demographic Biases,
WACV24(8581-8590)
IEEE DOI
2404
Training, Codes, Image synthesis, Computational modeling, Transforms,
Search engines, Applications, Social good
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Kotwal, K.[Ketan],
Marcel, S.[Sébastien],
Mitigating Demographic Bias in Face Recognition via Regularized Score
Calibration,
DVPBA24(1150-1159)
IEEE DOI
2404
Training, Costs, Face recognition, Pipelines, Computer architecture
BibRef
Tiwari, R.[Rishabh],
Sivasubramanian, D.[Durga],
Mekala, A.[Anmol],
Ramakrishnan, G.[Ganesh],
Shenoy, P.[Pradeep],
Using Early Readouts to Mediate Featural Bias in Distillation,
WACV24(2626-2635)
IEEE DOI
2404
Representation learning, Adaptation models, Correlation,
Computational modeling, Supervised learning, Benchmark testing,
ethical computer vision
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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
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OutDistri23(4419-4428)
IEEE DOI Code:
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2401
BibRef
Choithwani, M.[Mohit],
Almeida, S.[Sneha],
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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
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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
Abduh, L.[Latifah],
Ivrissimtzis, I.[Ioannis],
Race Bias Analysis of Bona Fide Errors in Face Anti-spoofing,
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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-Vińolo, 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
Peńa, 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 .