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Racial bias is an important issue in biometric, but has not been thoroughly studied in deep face recognition.
Face recognition algorithms and the other-race effect: computational mechanisms for a developmental contact hypothesis
N. Furl, P. J. Phillips, and A. J. O’Toole · 2002
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Face recognition vendor test 2002
P. J. Phillips, P. Grother, R. Micheals, D. M. Blackburn, E. Tabassi, and M. Bone · 2003
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Semi-supervised learning by entropy minimization
Y. Grandvalet and Y. Bengio · 2005
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Integrating structured biological data by kernel maximum mean discrepancy
K. M. Borgwardt, A. Gretton, M. J. Rasch, H.-P. Kriegel, B. Schölkopf, and A. J. Smola · 2006
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Integrating structured biological data by kernel maximum mean discrepancy
R. Cafiero, A. Gabrielli, M. A. MuÑ, and oz · 2006
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Labeled faces in the wild: A database for studying face recognition in unconstrained environments
G. B. Huang, M. Ramesh, T. Berg, and E. Learned-Miller · 2007
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Focus on quality, predicting frvt 2006 performance
J. R. Beveridge, G. H. Givens, P. J. Phillips, B. A. Draper, and Y. M. Lui · 2008
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Discriminative clustering by regularized information maximization
R. Gomes, A. Krause, and P. Perona · 2010
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Report on the evaluation of 2d still-image face recognition algorithms
P. J. Grother, G. W. Quinn, and P. J. Phillips · 2010
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Cross-domain sentiment classification via spectral feature alignment
S. J. Pan, X. Ni, J.-T. Sun, Q. Yang, and Z. Chen · 2010
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Co-training for domain adaptation
M. Chen, K. Q. Weinberger, and J. Blitzer · 2011
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A compact local binary pattern using maximization of mutual information for face analysis
B. Jun, T. Kim, and D. Kim · 2011
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An other-race effect for face recognition algorithms
P. J. Phillips, F. Jiang, A. Narvekar, J. Ayyad, and A. J. O’Toole · 2011
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Face recognition performance: Role of demographic information
B. F. Klare, M. J. Burge, J. C. Klontz, R. W. V. Bruegge, and A. K. Jain · 2012
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Imagenet classification with deep convolutional neural networks
A. Krizhevsky, I. Sutskever, and G. E. Hinton · 2012
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The good, the bad, and the ugly face challenge problem
P. J. Phillips, J. R. Beveridge, B. A. Draper, G. Givens, A. J. O’Toole, D. Bolme, J. Dunlop, Y. M. Lui, H. Sahibzada, and S. Weimer · 2012
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Information-theoretical learning of discriminative clusters for unsupervised domain adaptation
S. Yuan and S. Fei · 2012
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Decaf: A deep convolutional activation feature for generic visual recognition
J. Donahue, Y. Jia, O. Vinyals, J. Hoffman, N. Zhang, E. Tzeng, and T. Darrell · 2014
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Privacy of facial soft biometrics: Suppressing gender but retaining identity
A. Othman and A. Ross · 2014
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Very deep convolutional networks for large-scale image recognition
K. Simonyan and A. Zisserman · 2014
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Deep learning face representation by joint identification-verification
Y. Sun, Y. Chen, X. Wang, and X. Tang · 2014
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Deep domain confusion: Maximizing for domain invariance
E. Tzeng, J. Hoffman, N. Zhang, K. Saenko, and T. Darrell · 2014
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Learning face representation from scratch
D. Yi, Z. Lei, S. Liao, and S. Z. Li · 2014
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Unsupervised domain adaptation by backpropagation
Y. Ganin · 2015
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Freebase data dumps
Google · 2015
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Bi-shifting auto-encoder for unsupervised domain adaptation
M. Kan, S. Shan, and X. Chen · 2015
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Pushing the frontiers of unconstrained face detection and recognition: Iarpa janus benchmark a
B. F. Klare, B. Klein, E. Taborsky, A. Blanton, J. Cheney, K. Allen, P. Grother, A. Mah, and A. K. Jain · 2015
Cited alongside, same era.
Learning transferable features with deep adaptation networks
M. Long, Y. Cao, J. Wang, and M. I. Jordan · 2015
Vggface2: A dataset for recognising faces across pose and age
Q. Cao, L. Shen, W. Xie, O. M. Parkhi, and A. Zisserman · 2017
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Squeeze-and-excitation networks
J. Hu, L. Shen, and G. Sun · 2017
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Sphereface: Deep hypersphere embedding for face recognition
W. Liu, Y. Wen, Z. Yu, M. Li, B. Raj, and L. Song · 2017
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A cross benchmark assessment of a deep convolutional neural network for face recognition
P. J. Phillips · 2017
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Asymmetric tri-training for unsupervised domain adaptation
K. Saito, Y. Ushiku, and T. Harada · 2017
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Cited alongside, same era.
Deep face recognition
O. M. Parkhi, A. Vedaldi, A. Zisserman, et al · 2015
Cited alongside, same era.
Facenet: A unified embedding for face recognition and clustering
F. Schroff, D. Kalenichenko, and J. Philbin · 2015
Cited alongside, same era.
Going deeper with convolutions
C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, A. Rabinovich, et al · 2015
Cited alongside, same era.
Face search at scale: 80 million gallery
D. Wang, C. Otto, and A. K. Jain · 2015
Cited alongside, same era.
Face recognition using deep multi-pose representations
W. AbdAlmageed, Y. Wu, S. Rawls, S. Harel, T. Hassner, I. Masi, J. Choi, J. Lekust, J. Kim, P. Natarajan, et al · 2016
Cited alongside, same era.
Unconstrained face verification using deep cnn features
J.-C. Chen, V. M. Patel, and R. Chellappa · 2016
Cited alongside, same era.
Unsupervised domain adaptation for face recognition in unlabeled videos
K. Sohn, S. Liu, G. Zhong, X. Yu, M.-H. Yang, and M. Chandraker · 2017
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Adversarial discriminative domain adaptation
E. Tzeng, J. Hoffman, K. Saenko, and T. Darrell · 2017
Later among the works it cites.
Turning a blind eye: Explicit removal of biases and variation from deep neural network embeddings
M. Alvi, A. Zisserman, and C. Nellaker · 2018
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S. Barratt and R. Sharma · 2018
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Gender shades: Intersectional accuracy disparities in commercial gender classification
J. Buolamwini and T. Gebru · 2018
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Progressive feature alignment for unsupervised domain adaptation
C. Chen, W. Xie, T. Xu, W. Huang, Y. Rong, X. Ding, Y. Huang, and J. Huang · 2018
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Arcface: Additive angular margin loss for deep face recognition
J. Deng, J. Guo, and S. Zafeiriou · 2018
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Semi-adversarial networks: Convolutional autoencoders for imparting privacy to face images
V. Mirjalili, S. Raschka, A. Namboodiri, and A. Ross · 2018
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V. Mirjalili, S. Raschka, and A. Ross · 2018
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Supervised cosmos autoencoder: Learning beyond the euclidean loss!
M. Singh, S. Nagpal, M. Vatsa, R. Singh, and A. Noore · 2018
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Deep face recognition: A survey
M. Wang and W. Deng · 2018
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Deep visual domain adaptation: A survey
M. Wang and W. Deng · 2018
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Learning semantic representations for unsupervised domain adaptation
S. Xie, Z. Zheng, L. Chen, and C. Chen · 2018
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Collaborative and adversarial network for unsupervised domain adaptation
W. Zhang, W. Ouyang, W. Li, and D. Xu · 2018
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Deep unsupervised domain adaptation for face recognition
W. D. H. S. Zimeng Luo, Jiani Hu · 2018
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Uncovering and mitigating algorithmic bias through learned latent structure
A. Amini, A. Soleimany, W. Schwarting, S. Bhatia, and D. Rus · 2019
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