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The volume of convolutional neural network (CNN) models proposed for face recognition has been continuously growing larger to better fit large amount of training data.
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2014
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H. Ng and S. Winkler, “A data-driven approach to cleaning large face datasets,” in
2014
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Z. Zhu, P. Luo, X. Wang, and X. Tang, “Multi-view perceptron: a deep model for learning face identity and view representations,” in
2014
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H. Li, G. Hua, X. Shen, Z. L. Lin, and J. Brandt, “Eigen-pep for video face recognition,” in
2014
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2015
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2015
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I. Kemelmacher-Shlizerman, S. M. Seitz, D. Miller, and E. Brossard, “The megaface benchmark: 1 million faces for recognition at scale,” in
2016
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2016
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2016
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A. Bansal, A. Nanduri, C. Castillo, R. Ranjan, and R. Chellappa, “Umdfaces: An annotated face dataset for training deep networks,” in
2017
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A. Bansal, C. Castillo, R. Ranjan, and R. Chellappa, “The do’s and don’ts for cnn-based face verification,” in
2017
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2017
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C. Whitelam, E. Taborsky, A. Blanton, B. Maze, J. Adams, T. Miller, N. Kalka, A. K. Jain, J. A. Duncan, K. Allen
2017
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2017
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K. Sohn, S. Liu, G. Zhong, X. Yu, M.-H. Yang, and M. Chandraker, “Unsupervised domain adaptation for face recognition in unlabeled videos,” in
2017
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N. Crosswhite, J. Byrne, C. Stauffer, O. Parkhi, Q. Cao, and A. Zisserman, “Template adaptation for face verification and identification,” in
2017
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J. Zhao, L. Xiong, P. K. Jayashree, J. Li, F. Zhao, Z. Wang, P. S. Pranata, P. S. Shen, S. Yan, and J. Feng, “Dual-agent gans for photorealistic and identity preserving profile face synthesis,” in
2017
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X. Yin and X. Liu, “Multi-task convolutional neural network for face recognition,”
2017
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2017
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R. He, X. Wu, Z. Sun, and T. Tan, “Learning invariant deep representation for nir-vis face recognition,” in
2017
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2017
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2017
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2018
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