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Despite the remarkable progress in face recognition related technologies, reliably recognizing faces across ages still remains a big challenge.
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Deep face recognition
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Dex: Deep expectation of apparent age from a single image
R. Rothe, R. Timofte, and L. V. Gool · 2015
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Dex: Deep expectation of apparent age from a single image
R. Rothe, R. Timofte, and L. Van Gool · 2015
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Facenet: A unified embedding for face recognition and clustering
F. Schroff, D. Kalenichenko, and J. Philbin · 2015
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Domain-adversarial training of neural networks
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Ms-celeb-1m: A dataset and benchmark for large-scale face recognition
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C. Xu, Q. Liu, and M. Ye · 2017
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Age progression/regression by conditional adversarial autoencoder
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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 · 2017
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T. Zheng, W. Deng, and J. Hu · 2017
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Vggface2: A dataset for recognising faces across pose and age
Q. Cao, L. Shen, W. Xie, O. M. Parkhi, and A. Zisserman · 2018
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Iarpa janus benchmark–c: Face dataset and protocol
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Dual conditional gans for face aging and rejuvenation
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Cosface: Large margin cosine loss for deep face recognition
H. Wang, Y. Wang, Z. Zhou, X. Ji, and W. Liu · 2018
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Orthogonal deep features decomposition for age-invariant face recognition
Y. Wang, D. Gong, Z. Zhou, X. Ji, H. Wang, Z. Li, W. Liu, and T. Zhang · 2018
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A light cnn for deep face representation with noisy labels
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Multicolumn networks for face recognition
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3d-aided deep pose-invariant face recognition
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Facial aging and rejuvenation by conditional multi-adversarial autoencoder with ordinal regression
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