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Learning discriminative face features plays a major role in building high-performing face recognition models.
Probability, random variables, and random signal principles
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Ms-celeb-1m: A dataset and benchmark for large-scale face recognition
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The megaface benchmark: 1 million faces for recognition at scale
I. Kemelmacher-Shlizerman, S. M. Seitz, D. Miller, and E. Brossard · 2016
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Large-margin softmax loss for convolutional neural networks
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Frontal to profile face verification in the wild
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Improved deep metric learning with multi-class n-pair loss objective
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Joint face detection and alignment using multitask cascaded convolutional networks
K. Zhang, Z. Zhang, Z. Li, and Y. Qiao · 2016
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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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Agedb: The first manually collected, in-the-wild age database
S. Moschoglou, A. Papaioannou, C. Sagonas, J. Deng, I. Kotsia, and S. Zafeiriou · 2017
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IARPA janus benchmark-b face dataset
C. Whitelam, E. Taborsky, A. Blanton, B. Maze, J. C. Adams, T. Miller, N. D. Kalka, A. K. Jain, J. A. Duncan, K. Allen, J. Cheney, and P. Grother · 2017
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Cross-age LFW: A database for studying cross-age face recognition in unconstrained environments
T. Zheng, W. Deng, and J. Hu · 2017
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Uniformface: Learning deep equidistributed representation for face recognition
Y. Duan, J. Lu, and J. Zhou · 2019
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Adaptiveface: Adaptive margin and sampling for face recognition
H. Liu, X. Zhu, Z. Lei, and S. Z. Li · 2019
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Pytorch: An imperative style, high-performance deep learning library
A. Paszke, S. Gross, F. Massa, A. Lerer, J. Bradbury, G. Chanan, T. Killeen, Z. Lin, N. Gimelshein, L. Antiga, A. Desmaison, A. Kopf, E. Yang, Z. DeVito, M. Raison, A. Tejani, S. Chilamkurthy, B. Steiner, L. Fang, J. Bai, and S. Chintala · 2019
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Adacos: Adaptively scaling cosine logits for effectively learning deep face representations
X. Zhang, R. Zhao, Y. Qiao, X. Wang, and H. Li · 2019
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Curricularface: Adaptive curriculum learning loss for deep face recognition
Y. Huang, Y. Wang, Y. Tai, X. Liu, P. Shen, S. Li, J. Li, and F. Huang · 2020
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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
B. Maze, J. C. Adams, J. A. Duncan, N. D. Kalka, T. Miller, C. Otto, A. K. Jain, W. T. Niggel, J. Anderson, J. Cheney, and P. Grother · 2018
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Cosface: Large margin cosine loss for deep face recognition
H. Wang, Y. Wang, Z. Zhou, X. Ji, D. Gong, J. Zhou, Z. Li, and W. Liu · 2018
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A light CNN for deep face representation with noisy labels
X. Wu, R. He, Z. Sun, and T. Tan · 2018
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Cross-pose lfw: A database for studying cross-pose face recognition in unconstrained environments
T. Zheng and W. Deng · 2018
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Arcface: Additive angular margin loss for deep face recognition
J. Deng, J. Guo, N. Xue, and S. Zafeiriou · 2019
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Groupface: Learning latent groups and constructing group-based representations for face recognition
Y. Kim, W. Park, M. Roh, and J. Shin · 2020
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Circle loss: A unified perspective of pair similarity optimization
Y. Sun, C. Cheng, Y. Zhang, C. Zhang, L. Zheng, Z. Wang, and Y. Wei · 2020
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Partial fc: Training 10 million identities on a single machine
X. An, X. Zhu, Y. Gao, Y. Xiao, Y. Zhao, Z. Feng, L. Wu, B. Qin, M. Zhang, D. Zhang, and Y. Fu · 2021
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ISO/IEC 19795-1:2021 Information technology — Biometric performance testing and reporting — Part 1: Principles and framework
ISO/IEC JTC1 SC37 Biometrics · 2021
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Dyn-arcface: dynamic additive angular margin loss for deep face recognition
J. Jiao, W. Liu, Y. M. J. Jiao, Z. Deng, and X. Chen · 2021
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Magface: A universal representation for face recognition and quality assessment
Q. Meng, S. Zhao, Z. Huang, and F. Zhou · 2021
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