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
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
Adeli, E.[Ehsan],
Zhao, Q.[Qingyu],
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
Wang, A.[Angelina],
Narayanan, A.[Arvind],
Russakovsky, O.[Olga],
Revise: A Tool for Measuring and Mitigating Bias in Visual Datasets,
ECCV20(III:733-751).
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.,
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
Wang, M.,
Deng, W.,
Hu, J.,
Tao, X.,
Huang, Y.,
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
Wang, M.[Mei],
Deng, W.H.[Wei-Hong],
Mitigating Bias in Face Recognition Using Skewness-Aware
Reinforcement Learning,
CVPR20(9319-9328)
IEEE DOI
2008
Training, Face recognition, Face,
Learning (artificial intelligence), Training data, Databases, Computer vision
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
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 .