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K. Zhang, Y.-L. Chang, and W. Hsu, “Deep disguised faces recognition,” in
2018
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N. Kohli, D. Yadav, and A. Noore, “Face verification with disguise variations via deep disguise recognizer,” in
2018
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E. Smirnov, A. Melnikov, A. Oleinik, E. Ivanova, I. Kalinovskiy, and E. Luckyanets, “Hard example mining with auxiliary embeddings,” in
2018
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S. Suri, A. Sankaran, M. Vatsa, and R. Singh, “On matching faces with alterations due to plastic surgery and disguise,” in
2018
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2018
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L. Song, M. Zhang, X. Wu, and R. He, “Adversarial discriminative heterogeneous face recognition,” in
2018
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Z. Shen, W.-S. Lai, T. Xu, J. Kautz, and M.-H. Yang, “Deep semantic face deblurring,” in
2018
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X. Liu, B. Vijaya Kumar, C. Yang, Q. Tang, and J. You, “Dependency-aware attention control for unconstrained face recognition with image sets,” in
2018
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L. He, H. Li, Q. Zhang, and Z. Sun, “Dynamic feature learning for partial face recognition,” in
2018
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Y. Wei, X. Pan, H. Qin, W. Ouyang, and J. Yan, “Quantization mimic: Towards very tiny cnn for object detection,” in
2018
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A. Jourabloo, Y. Liu, and X. Liu, “Face de-spoofing: Anti-spoofing via noise modeling,” in
2018
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2018
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A. Goel, A. Singh, A. Agarwal, M. Vatsa, and R. Singh, “Smartbox: Benchmarking adversarial detection and mitigation algorithms for face recognition,”
2018
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G. Mai, K. Cao, P. C. Yuen, and A. K. Jain, “On the reconstruction of face images from deep face templates,”
2018
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2018
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V. Mirjalili, S. Raschka, and A. Ross, “Gender privacy: An ensemble of semi adversarial networks for confounding arbitrary gender classifiers,” in
2018
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V. Mirjalili, S. Raschka, A. Namboodiri, and A. Ross, “Semi-adversarial networks: Convolutional autoencoders for imparting privacy to face images,” in
2018
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P. J. Phillips, A. N. Yates, Y. Hu, C. A. Hahn, E. Noyes, K. Jackson, J. G. Cavazos, G. Jeckeln, R. Ranjan, S. Sankaranarayanan
2018
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J. Deng, J. Guo, N. Xue, and S. Zafeiriou, “Arcface: Additive angular margin loss for deep face recognition,” in
2019
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B. Liu, W. Deng, Y. Zhong, M. Wang, J. Hu, X. Tao, and Y. Huang, “Fair loss: Margin-aware reinforcement learning for deep face recognition,” in
2019
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H. Liu, X. Zhu, Z. Lei, and S. Z. Li, “Adaptiveface: Adaptive margin and sampling for face recognition,” in
2019
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E. Real, A. Aggarwal, Y. Huang, and Q. V. Le, “Aging evolution for image classifier architecture search,” in
2019
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I. Hupont and C. Fernández, “Demogpairs: Quantifying the impact of demographic imbalance in deep face recognition,” in
2019
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M. Wang, W. Deng, J. Hu, X. Tao, and Y. Huang, “Racial faces in the wild: Reducing racial bias by information maximization adaptation network,” in
2019
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M. Singh, M. Chawla, R. Singh, M. Vatsa, and R. Chellappa, “Disguised faces in the wild 2019,” in
2019
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M. Singh, R. Singh, M. Vatsa, N. K. Ratha, and R. Chellappa, “Recognizing disguised faces in the wild,”
2019
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X. Yin, X. Yu, K. Sohn, X. Liu, and M. Chandraker, “Feature transfer learning for face recognition with under-represented data,” in
2019
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R. Shao, X. Lan, and P. C. Yuen, “Joint discriminative learning of deep dynamic textures for 3d mask face anti-spoofing,”
2019
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A. Rossler, D. Cozzolino, L. Verdoliva, C. Riess, J. Thies, and M. Niessner, “Faceforensics++: Learning to detect manipulated facial images,” in
2019
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Y. Gurovich, Y. Hanani, and e. a. Bar, Omri, “Identifying facial phenotypes of genetic disorders using deep learning,”
2019
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N. Zhu, Z. Yu, and C. Kou, “A new deep neural architecture search pipeline for face recognition,”
2020
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Y. Zhang, W. Deng, M. Wang, J. Hu, X. Li, D. Zhao, and D. Wen, “Global-local gcn: Large-scale label noise cleansing for face recognition,” in
2020
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2020
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Y. Kim, W. Park, M.-C. Roh, and J. Shin, “Groupface: Learning latent groups and constructing group-based representations for face recognition,” in
2020
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E. Zangeneh, M. Rahmati, and Y. Mohsenzadeh, “Low resolution face recognition using a two-branch deep convolutional neural network architecture,”
2020
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M. Wang and W. Deng, “Mitigating bias in face recognition using skewness-aware reinforcement learning,” in
2020
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J. Guo, X. Zhu, C. Zhao, D. Cao, Z. Lei, and S. Z. Li, “Learning meta face recognition in unseen domains,” in
2020
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Y. Zhong and W. Deng, “Towards transferable adversarial attack against deep face recognition,”
2020
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H. Dang, F. Liu, J. Stehouwer, X. Liu, and A. K. Jain, “On the detection of digital face manipulation,” in
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M. Jaderberg, K. Simonyan, A. Zisserman
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G. Antipov, M. Baccouche, and J.-L. Dugelay, “Face aging with conditional generative adversarial networks,” in
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