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Face recognition has witnessed significant progresses due to the advances of deep convolutional neural networks (CNNs), the central challenge of which, is feature discrimination.
Support-vector networks
C. Cortes and V. Vapnik · 1995
Earlier work this paper cites.
Labeled faces in the wild: A database for studying face recognition in unconstrained enviroments
G. Huang, M. Ramesh, T. Berg, and E. Miller · 2007
Earlier work this paper cites.
Robust face recognition via sparse representation
J. Wright, A. Y. Yang, A. Ganesh, S. S. Sastry, and Y. Ma · 2009
Earlier work this paper cites.
http://www.fgnet.rsunit.com/
Fg-net aging database · 2010
Earlier work this paper cites.
Support vector guided dictionary learning
S. Cai, W. Zuo, L. Zhang, X. Feng, and P. Wang · 2014
Earlier work this paper cites.
A benchmark study of large-scale unconstrained face recognition
S. Liao, Z. Lei, D. Yi, and S. Z. Li · 2014
Earlier work this paper cites.
A data-driven approach to cleaning large face datasets
H.-W. Ng and S. Winkler · 2014
Earlier work this paper cites.
Very deep convolutional networks for large-scale image recognition
K. Simonyan and Z. Andrew · 2014
Earlier work this paper cites.
Deep learning face representation by joint identification-verification
Y. Sun, Y. Chen, and X. Wang · 2014
Earlier work this paper cites.
Deep learning face representation from predicting 10,000 classes
Y. Sun, X. Wang, and X. Tang · 2014
Earlier work this paper cites.
Deepface: Closing the gap to human-level performance in face verification
Y. Taigman, M. Yang, and M. Ranzato · 2014
Earlier work this paper cites.
Deep face recognition
O. Parkhi, A. Vedaldi, and A. Zisserman · 2015
Earlier work this paper cites.
Facenet: A unified embedding for face recognition and clustering
F. Schroff, D. Kalenichenko, and J. Philbin · 2015
Earlier work this paper cites.
Deeply learned face representations are sparse, selective, and robust
Y. Sun, X. Wang, and X. Tang · 2015
Earlier work this paper cites.
Adaptively unified semi-supervised dictionary learning with active points
X. Wang, X. Guo, and S. Z. Li · 2015
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Ms-celeb-1m: A dataset and benchmark for large-scale face recognition
Y. Guo, L. Zhang, Y. Hu, X. He, and J. Gao · 2016
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Deep residual learning for image recognition
K. He, X. Zhang, and S. Ren · 2016
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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
W. Liu, Y. Wen, and Z. Yu · 2016
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Training region-based object detectors with online hard example mining
A. Shrivastava, A. Gupta, and R. Girshick · 2016
Cited alongside, same era.
L2-constrained softmax loss for discriminative face verification
R. Ranjan, C. Castillo, and R. Chellappa · 2017
Later among the works it cites.
Cross-modality face recognition via heterogeneous joint bayesian
H. Shi, X. Wang, D. Yi, Z. Lei, X. Zhu, and S. Z. Li · 2017
Later among the works it cites.
Residual attention network for image classification
F. Wang, M. Jiang, C. Qian, S. Yang, C. Li, H. Zhang, X. Wang, and X. Tang · 2017
Later among the works it cites.
Normface:
F. Wang, X. Xiang, J. Chen, and A. Yuille · 2017
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Deep metric learning with angular loss
J. Wang, F. Zhou, and S. Wen · 2017
Later among the works it cites.
Hard-aware deeply cascaded embedding
Y. Yuan, K. Yang, and C. Zhang · 2017
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Deep metric learning via lifted structured feature embedding
H. Song, Y. Xiang, S. Jegelka, and S. Savarese · 2016
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A discriminative feature learning approach for deep face recognition
Y. Wen, K. Zhang, and Z. Li · 2016
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Wing loss for robust facial landmark localisation with convolutional neural networks
Z.-H. Feng, J. Kittler, M. Awais, P. Huber, and X.-J. Wu · 2017
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Soft-margin softmax for deep classification
X. Liang, X. Wang, Z. Lei, S. Liao, and S. Li · 2017
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Focal loss for dense object detection
Y. Lin, P. Goyal, and R. Girshick · 2017
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Sphereface: Deep hypersphere embedding for face recognition
W. Liu, Y. Wen, Z. Yu, M. Li, and L. Song · 2017
Cited alongside, same era.
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Faceboxes: A cpu real-time face detector with high accuracy
S. Zhang, X. Zhu, Z. Lei, H. Shi, X. Wang, and S. Z. Li · 2017
Later among the works it cites.
Arcface: Additive angular margin loss for deep face recognition
J. Deng, J. Guo, and S. Zafeiriou · 2018
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The devil of face recognition is in the noise
F. Wang, L. Chen, C. Li, S. Huang, Y. Chen, C. Qian, and C. C. Loy · 2018
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Additive margin softmax for face verification
F. Wang, J. Cheng, W. Liu, and H. Liu · 2018
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Cosface: Large margin cosine loss for deep face recognition
H. Wang, Y. Wang, Z. Zhou, X. Ji, Z. Li, D. Gong, J. Zhou, and W. Liu · 2018
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Ensemble soft-margin softmax loss for image classification
X. Wang, S. Zhang, Z. Lei, S. Liu, X. Guo, and S. Z. Li · 2018
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Ring loss: Convex feature normalization for face recognition
Y. Zheng, D. K. Pal, and M. Savvides · 2018
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