21.2.1.2 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


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

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 .


Last update:Oct 24, 2021 at 16:35:58