1 Deepface

[1] Taigman Y, Yang M, Ranzato M A, et al. Deepface: Closing the gap to human-level performance in face verification[C]//Proceedings of the IEEE conference on computer vision and pattern recognition. 2014: 1701-1708.

2 DeepID系列

[2] Sun Y, Wang X, Tang X. Deep learning face representation from predicting 10,000 classes[C]//Proceedings of the IEEE conference on computer vision and pattern recognition. 2014: 1891-1898.

[3] Sun Y, Chen Y, Wang X, et al. Deep learning face representation by joint identification-verification[C]//Advances in neural information processing systems. 2014: 1988-1996.

[4] Sun Y, Wang X, Tang X. Deeply learned face representations are sparse, selective, and robust[C]//Proceedings of the IEEE conference on computer vision and pattern recognition. 2015: 2892-2900.

3 FaceNet

[5] Schroff F, Kalenichenko D, Philbin J. Facenet: A unified embedding for face recognition and clustering[C]//Proceedings of the IEEE conference on computer vision and pattern recognition. 2015: 815-823.

[6] Parkhi, Omkar M., Andrea Vedaldi, and Andrew Zisserman. "Deep face recognition." bmvc. Vol. 1. No. 3. 2015.

4 Center face

[7] Wen Y, Zhang K, Li Z, et al. A discriminative feature learning approach for deep face recognition[C]//European conference on computer vision. Springer, Cham, 2016: 499-515.

5 Range loss

[8] Zhang X, Fang Z, Wen Y, et al. Range loss for deep face recognition with long-tailed training data[C]//Proceedings of the IEEE International Conference on Computer Vision. 2017: 5409-5418.

6 angular loss与coco loss

[9] Wang J, Zhou F, Wen S, et al. Deep metric learning with angular loss[C]//Proceedings of the IEEE International Conference on Computer Vision. 2017: 2593-2601.

[10] Liu Y, Li H, Wang X. Rethinking feature discrimination and polymerization for large-scale recognition[J]. arXiv preprint arXiv:1710.00870, 2017.

7 softmax loss及其变种

8 如何获取文章与交流

https://github.com/longpeng2008/yousan.ai

人脸识别是人脸算法中最广泛的应用，仍然有研究空间，往后我们将推荐跨年龄，抗遮挡与装饰，3D人脸识别相关的文章。