深度人脸识别方法综述
A Survey of Deep Face Recognition
DOI: 10.12677/SEA.2023.124059, PDF,    国家自然科学基金支持
作者: 王海勇, 潘海涛*：南京邮电大学计算机学院，江苏 南京
关键词: 人脸识别；卷积神经网络；人脸表示；损失函数；Face Recognition； Convolutional Neural Networks； Face Representation； Loss Function
摘要: 深度人脸识别通过大规模数据集训练卷积神经网络获取更鲁棒的人脸表示,极大的提升了人脸识别性能。文中总结了深度人脸识别方法的发展脉络，首先根据卷积神经网络的不同发展阶段回顾了现有的深度人脸识别方法，其次对基于欧几里得距离以及基于角余弦裕度的损失函数进行了回顾，同时总结了一些针对特定任务的人脸识别方法。然后总结了现有的人脸识别数据集以及人脸识别性能的评价指标，并对主流深度人脸识别方法进行了比较。最后总结了人脸识别当前面临的挑战和未来的发展趋势。
Abstract: Deep face recognition greatly improves the performance of face recognition by training convolutional neural networks on large-scale data sets to obtain more robust face representation. This paper summarizes the development of depth face recognition methods. First, the existing depth face recognition methods are reviewed according to the different development stages of convolutional neural networks. Secondly, the loss functions based on Euclidean distance and angular cosine margin are reviewed, and some task-specific face recognition methods are summarized. Then, the existing data sets and the evaluation indicators of face recognition performance are summarized, and the mainstream depth face recognition methods are compared. Finally, the current challenges and future trends of face recognition are summarized.
文章引用：王海勇, 潘海涛. 深度人脸识别方法综述[J]. 软件工程与应用, 2023, 12(4): 609-619. https://doi.org/10.12677/SEA.2023.124059

参考文献

[1] Chen, S., Liu, Y., Gao, X. and Han, Z. (2018) MobileFaceNets: Efficient CNNs for Accurate Real-Time Face Verification on Mobile Devices. In: Zhou, J., et al. Eds., CCBR 2018: Biometric Recognition, Springer, Cham, 428-438. [Google Scholar] [CrossRef] 
[2] Duong, C.N., Quach, K.G., Jalata, I., Le, N. and Luu, K. (2019) MobiFace: A Lightweight Deep Learning Face recOgnition on Mobile Devices. 2019 IEEE 10th International Conference on Biometrics Theory, Applications and Systems (BTAS), Tampa, 23-26 September 2019, 1-6. [Google Scholar] [CrossRef] 
[3] Zhang, J. (2019) Seesawfacenets: Sparse and Robust Face Verification Model for Mobile Platform.
https://arxiv.org/abs/1908.09124
[4] Boutros, F., Damer, N., Fang, M., Kirchbuchner, F. and Kuijper, A. (2021) MixFaceNets: Extremely Efficient Face Recognition Networks. 2021 IEEE International Joint Conference on Biometrics (IJCB), Shenzhen, 4-7 August 2021, 1-8. [Google Scholar] [CrossRef] 
[5] Liu, W., Wen, Y., Yu, Z., et al. (2017) SphereFace: Deep Hypersphere Embedding for Face Recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, 21-26 July 2017, 6738-6746. [Google Scholar] [CrossRef] 
[6] Wang, H., Wang, Y., Zhou, Z., et al. (2018) CosFace: Large Margin Cosine Loss for Deep Face Recognition. 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, 18-23 June 2018, 5265-5274. [Google Scholar] [CrossRef] 
[7] Deng, J., Guo, J., Xue, N. and Zafeiriou, S. (2019) ArcFace: Additive Angular Margin Loss for Deep Face recOgnition. 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, 15-20 June 2019, 4 4685-4694. [Google Scholar] [CrossRef] 
[8] Zhang, X., Zhao, R., Qiao, Y., Wang, X.G. and Li, H.S. (2019) AdaCos: Adaptively Scaling Cosine Logits for Effectively Learning Deep Face Representations. 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, 15-20 June 2019, 10815-10824. [Google Scholar] [CrossRef] 
[9] Wang, X., Zhang, S., Wang, S., et al. (2020) Mis-Classified Vector Guided Softmax Loss for Face Recognition. Proceedings of the AAAI Conference on Artificial Intelligence, 34, 12241-12248. [Google Scholar] [CrossRef] 
[10] Huang, Y., Wang, Y., Tai, Y., et al. (2020) Curricularface: Adaptive Curriculum Learning Loss for Deep Face reCognition. 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, 13-19 June 2020, 5900-5909. [Google Scholar] [CrossRef] 
[11] Kim, M., Jain, A.K. and Liu, X. (2022) AdaFace: Quality Adaptive Margin for Face Recognition. 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), New Orleans, 18-24 June 2022, 18729-18738. [Google Scholar] [CrossRef] 
[12] Meng, Q., Zhao, S., Huang, Z. and Zhou, F. (2021) MagFace: A Universal Representation for Face Recognition and Quality Assessment. 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Nashville, 20-25 June 2021, 14220-14229. [Google Scholar] [CrossRef] 
[13] Liu, H., Zhu, X., Lei, Z. and Li, S.Z. (2019) AdaptiveFace: Adaptive Margin and Sampling for Face Recognition. 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, 15-20 June 2019, 11939- 11948. [Google Scholar] [CrossRef] 
[14] Shi, Y. and Jain, A.K. (2019) Probabilistic Face Embeddings. 2019 IEEE/CVF International Conference on Computer Vision (ICCV), Seoul, 27 October 2019-2 November 2019, 6901-6910. [Google Scholar] [CrossRef] 
[15] Chang, J., Lan, Z., Cheng, C. and Wei, Y.C. (2020) Data Uncertainty Learning in Face Recognition. 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, 13-19 June 2020, 5709-5718. [Google Scholar] [CrossRef] 
[16] Li, S., Xu, J., Xu, X., et al. (2021) Spherical Confidence Learning for Face Recognition. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, Nashville, 20-25 June 2021, 15629-15637.

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