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SphereFace: Deep Hypersphere Embedding for Face Recognition
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Current face recognition systems already reach very high levels of accuracy in public non-forensic benchmarks.
“Current face recognition systems [8,24,43], already reach very high levels of accuracy in public non-forensic benchmarks, and it is expected that in the coming years they will keep improving.”
From Calibration of score based likelihood ratio estimation in automated forensic facial image comparison (2022) · cites this paper as [24]
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Deep Residual Learning for Image Recognition
He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.90 · Cited by 235,474
ImageNet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017) · Communications of the ACM · doi:10.1145/3065386 · Cited by 112,155
FaceNet: A unified embedding for face recognition and clustering
Schroff, F., Kalenichenko, D., & Philbin, J. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298682 · Cited by 15,267
A Discriminative Feature Learning Approach for Deep Face Recognition
Wen, Y., Zhang, K., Li, Z., & Qiao, Y. (2016) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-46478-7_31 · Cited by 4,077
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Research works printed but not linked: 35
- zhang, 2016, Joint Face Detection and Alignment using Multi-task Cascaded Convolutional Networks
- ying, 2012, Distance metric learning with eigenvalue optimization, JMLR, 13, 1
- weinberger, 2009, Distance metric learning for large margin nearest neighbor classification, Journal of Machine Learning Research, 10, 207
- 10.1109/CVPR.2014.180 · DOI
- 10.1109/CVPR.2008.4587670 · DOI
- 10.1109/CVPR.2014.220 · DOI · Paper page
- yi, 2014, Learning face representation from scratch
- xing, 2003, Distance metric learning with application to clustering with side-information, NIPS
- 10.1109/CVPR.2011.5995566 · DOI
- jia, 2014, Caffe Convolutional Architecture for Fast Feature Embedding
- 10.1109/CVPR.2016.527 · DOI · Paper page
- 10.1109/CVPR.2012.6247939 · DOI
- lee, 2003, Video-based face recognition using probabilistic appearance manifolds, CVPR
- liu, 2015, Targeting ultimate accuracy Face recognition via deep embedding
- liu, 2016, Large-margin softmax loss for convolutional neural networks, ICML
- 10.1109/CVPR.2015.7298717 · DOI
- miller, 2015, Megaface A million faces for recognition at scale
- 10.1109/ICIP.2014.7025068 · DOI
- 10.1109/CVPR.2016.525 · DOI
- 10.1109/CVPR.2015.7298907 · DOI
- 10.1109/CVPR.2006.100 · DOI · Paper page
- hoffer, 2014, Deep metric learning using triplet network
- 10.1109/CVPR.2015.7298594 · DOI · Paper page
- he, 2005, Face recognition using laplacianfaces, TPAMI, 27, 328, 10.1109/TPAMI.2005.55 · DOI
- huang, 2014, Labeled faces in the wild: Updates and new reporting procedures, Tech Rep, 14
- 10.1109/CVPR.2014.242 · DOI
- 10.1109/TMM.2015.2477042 · DOI · Paper page
- huang, 2007, Labeled faces in the wild: A database for studying face recognition in unconstrained environments, Technical Report Technical Report
- 10.1109/CVPR.2005.202 · DOI · Paper page
- parkhi, 2015, Deep face recognition, BMVC
- arun ross, 2004, Multimodal Biometrics: An Overview, Proc 2004 Eur Signal Process Conf, 1221
- 10.1109/CVPR.2016.434 · DOI
- simonyan, 2014, Very Deep Convolutional Networks for Large-scale Image Recognition
- 10.1109/CVPR.2014.244 · DOI
- sun, 2014, Deep learning face representation by joint identification-verification, NIPS
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Bagher Zadeh, A., Liang, P. P., Poria, S., Cambria, E., & Morency, L.-P. (2018) · Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.18653/v1/p18-1208 · Cited by 1,433
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Wang, K., Peng, X., Yang, J., Lu, S., & Qiao, Y. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr42600.2020.00693 · Cited by 772
Face Recognition Systems: A Survey
Kortli, Y., Jridi, M., Al Falou, A., & Atri, M. (2020) · Sensors · doi:10.3390/s20020342 · Cited by 623
ResNet and its application to medical image processing: Research progress and challenges
Xu, W., Fu, Y.-L., & Zhu, D. (2023) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2023.107660 · Cited by 416
Speaker recognition based on deep learning: An overview
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Feature Transfer Learning for Face Recognition With Under-Represented Data
Yin, X., Yu, X., Sohn, K., Liu, X., & Chandraker, M. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2019.00585 · Cited by 346
Racial faces in the wild: Reducing racial bias by information maximization adaptation network
Wang, M., Deng, W., Hu, J., Tao, X., & Huang, Y. (2019) · 2019 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00078 · Cited by 353
Data Uncertainty Learning in Face Recognition
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Nautsch, A., Wang, X., Evans, N., Kinnunen, T. H., Vestman, V., Todisco, M., Delgado, H., Sahidullah, M., Yamagishi, J., & Lee, K. A. (2021) · IEEE Transactions on Biometrics, Behavior, and Identity Science · doi:10.1109/tbiom.2021.3059479 · Cited by 185
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Topology-Preserving Class-Incremental Learning
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On the Intrinsic Dimensionality of Image Representations
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Wang, X., Qin, X., Zhu, T., Wang, C., Zhang, S., & Li, M. (2021) · 2021 Edition of the Automatic Speaker Verification and Spoofing Countermeasures Challenge · doi:10.21437/asvspoof.2021-3 · Cited by 27
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Do Fine-tuned Commonsense Language Models Really Generalize?
Kejriwal, M., & Shen, K. (2020) · arXiv · doi:10.48550/arxiv.2011.09159 · Cited by 6
Child Face Age-Progression via Deep Feature Aging
Deb, D., Aggarwal, D., & Jain, A. K. (2020) · arXiv · doi:10.48550/arxiv.2003.08788 · Cited by 4
Metadata
- Authors
- Liu, W., Wen, Y., Yu, Z., Li, M., Raj, B., & Song, L.
- Venue
- 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
- Publisher
- IEEE
- Published
- 2017-07
- Pages
- 6738-6746
- Cited by
- 2,280
- References
- 4
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