Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 31903
MarginDistillation: Distillation for Face Recognition Neural Networks with Margin-Based Softmax

Authors: Svitov David, Alyamkin Sergey

Abstract:

The usage of convolutional neural networks (CNNs) in conjunction with the margin-based softmax approach demonstrates the state-of-the-art performance for the face recognition problem. Recently, lightweight neural network models trained with the margin-based softmax have been introduced for the face identification task for edge devices. In this paper, we propose a distillation method for lightweight neural network architectures that outperforms other known methods for the face recognition task on LFW, AgeDB-30 and Megaface datasets. The idea of the proposed method is to use class centers from the teacher network for the student network. Then the student network is trained to get the same angles between the class centers and face embeddings predicted by the teacher network.

Keywords: ArcFace, distillation, face recognition, margin-based softmax.

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 319

References:


[1] S. Chen, Y. Liu, X. Gao, and Z. Han, “Mobilefacenets: Efficient cnns for accurate real-time face verification on mobile devices,” in Chinese Conference on Biometric Recognition. Springer, 2018, pp. 428–438.
[2] J. Deng, J. Guo, N. Xue, and S. Zafeiriou, “Arcface: Additive angular margin loss for deep face recognition,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2019, pp. 4690–4699.
[3] W. Liu, Y. Wen, Z. Yu, M. Li, B. Raj, and L. Song, “Sphereface: Deep hypersphere embedding for face recognition,” in Proceedings of the IEEE conference on computer vision and pattern recognition, 2017, pp. 212–220.
[4] H. Wang, Y. Wang, Z. Zhou, X. Ji, D. Gong, J. Zhou, Z. Li, and W. Liu, “Cosface: Large margin cosine loss for deep face recognition,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2018, pp. 5265–5274.
[5] G. B. Huang, M. Mattar, T. Berg, and E. Learned-Miller, “Labeled faces in the wild: A database forstudying face recognition in unconstrained environments,” 2008.
[6] S. Moschoglou, A. Papaioannou, C. Sagonas, J. Deng, I. Kotsia, and S. Zafeiriou, “Agedb: the first manually collected, in-the-wild age database,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, 2017, pp. 51–59.
[7] I. Kemelmacher-Shlizerman, S. M. Seitz, D. Miller, and E. Brossard, “The megaface benchmark: 1 million faces for recognition at scale,” in Proceedings of the IEEE conference on computer vision and pattern recognition, 2016, pp. 4873–4882.
[8] G. Hinton, O. Vinyals, and J. Dean, “Distilling the knowledge in a neural network,” arXiv preprint arXiv:1503.02531, 2015.
[9] T. Fukuda, M. Suzuki, G. Kurata, S. Thomas, J. Cui, and B. Ramabhadran, “Efficient knowledge distillation from an ensemble of teachers.” in Interspeech, 2017, pp. 3697–3701.
[10] B. B. Sau and V. N. Balasubramanian, “Deep model compression: Distilling knowledge from noisy teachers,” arXiv preprint arXiv:1610.09650, 2016.
[11] T. Furlanello, Z. C. Lipton, M. Tschannen, L. Itti, and A. Anandkumar, “Born again neural networks,” arXiv preprint arXiv:1805.04770, 2018.
[12] Z. Huang and N. Wang, “Like what you like: Knowledge distill via neuron selectivity transfer,” arXiv preprint arXiv:1707.01219, 2017.
[13] A. Romero, N. Ballas, S. E. Kahou, A. Chassang, C. Gatta, and Y. Bengio, “Fitnets: Hints for thin deep nets,” arXiv preprint arXiv:1412.6550, 2014.
[14] H. Chen, Y. Wang, C. Xu, C. Xu, and D. Tao, “Learning student networks via feature embedding,” arXiv preprint arXiv:1812.06597, 2018.
[15] W. Park, D. Kim, Y. Lu, and M. Cho, “Relational knowledge distillation,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2019, pp. 3967–3976.
[16] Y. Feng, H. Wang, R. Hu, and D. T. Yi, “Triplet distillation for deep face recognition,” arXiv preprint arXiv:1905.04457, 2019.
[17] C. N. Duong, K. Luu, K. G. Quach, and N. Le, “Shrinkteanet: Million-scale lightweight face recognition via shrinking teacher-student networks,” arXiv preprint arXiv:1905.10620, 2019.
[18] D. Nekhaev, S. Milyaev, and I. Laptev, “Margin based knowledge distillation for mobile face recognition,” in Twelfth International Conference on Machine Vision (ICMV 2019), vol. 11433. International Society for Optics and Photonics, 2020, p. 114330O.
[19] K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in Proceedings of the IEEE conference on computer vision and pattern recognition, 2016, pp. 770–778.
[20] K. Zhang, Z. Zhang, Z. Li, and Y. Qiao, “Joint face detection and alignment using multitask cascaded convolutional networks,” IEEE Signal Processing Letters, vol. 23, no. 10, pp. 1499–1503, 2016.
[21] Y. Guo, L. Zhang, Y. Hu, X. He, and J. Gao, “Ms-celeb-1m: A dataset and benchmark for large-scale face recognition,” in European conference on computer vision. Springer, 2016, pp. 87–102.
[22] H.-W. Ng and S. Winkler, “A data-driven approach to cleaning large face datasets,” in 2014 IEEE international conference on image processing (ICIP). IEEE, 2014, pp. 343–347.