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CoupleFace: Relation Matters for Face Recognition Distillation

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Computer Vision – ECCV 2022 (ECCV 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13672))

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Abstract

Knowledge distillation is an effective method to improve the performance of a lightweight neural network (i.e., student model) by transferring the knowledge of a well-performed neural network (i.e., teacher model), which has been widely applied in many computer vision tasks, including face recognition (FR). Nevertheless, the current FR distillation methods usually utilize the Feature Consistency Distillation (FCD) (e.g., \(L_2\) distance) on the learned embeddings extracted by the teacher and student models for each sample, which is not able to fully transfer the knowledge from the teacher to the student for FR. In this work, we observe that mutual relation knowledge between samples is also important to improve the discriminative ability of the learned representation of the student model, and propose an effective FR distillation method called CoupleFace by additionally introducing the Mutual Relation Distillation (MRD) into existing distillation framework. Specifically, in MRD, we first propose to mine the informative mutual relations, and then introduce the Relation-Aware Distillation (RAD) loss to transfer the mutual relation knowledge of the teacher model to the student model. Extensive experimental results on multiple benchmark datasets demonstrate the effectiveness of our proposed CoupleFace for FR. Moreover, based on our proposed CoupleFace, we have won the first place in the ICCV21 Masked Face Recognition Challenge (MS1M track).

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References

  1. An, X., et al.: Partial FC: training 10 million identities on a single machine. In: Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) Workshops, pp. 1445–1449, October 2021

    Google Scholar 

  2. David, S., Sergey, A.: MarginDistillation: distillation for face recognition neural networks with margin-based Softmax. Int. J. Comput. Inf. Eng. 15(3), 206–210 (2021)

    Google Scholar 

  3. Deng, J., Guo, J., Liu, T., Gong, M., Zafeiriou, S.: Sub-center ArcFace: boosting face recognition by large-scale noisy web faces. In: Proceedings of the IEEE Conference on European Conference on Computer Vision (2020)

    Google Scholar 

  4. Deng, J., Guo, J., Xue, N., Zafeiriou, S.: ArcFace: additive angular margin loss for deep face recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4690–4699 (2019)

    Google Scholar 

  5. Deng, J., Guo, J., Yang, J., Lattas, A., Zafeiriou, S.: Variational prototype learning for deep face recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). pp. 11906–11915, June 2021

    Google Scholar 

  6. Fang, Z., Wang, J., Wang, L., Zhang, L., Yang, Y., Liu, Z.: (SEED): self-supervised distillation for visual representation. In: International Conference on Learning Representations (2021)

    Google Scholar 

  7. Feng, Y., Wang, H., Hu, H.R., Yu, L., Wang, W., Wang, S.: Triplet distillation for deep face recognition. In: 2020 IEEE International Conference on Image Processing (ICIP), pp. 808–812. IEEE (2020)

    Google Scholar 

  8. Gentile, C., Warmuth, M.K.: Linear hinge loss and average margin. In: Advances In Neural Information Processing Systems 11, pp. 225–231 (1998)

    Google Scholar 

  9. Guo, J., Liu, J., Xu, D.: JointPruning: pruning networks along multiple dimensions for efficient point cloud processing. IEEE Trans. Circuits Syst. Video Technol. 32(6), 3659–3672 (2021)

    Article  Google Scholar 

  10. Guo, J., Ouyang, W., Xu, D.: Multi-dimensional pruning: a unified framework for model compression. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 1508–1517 (2020)

    Google Scholar 

  11. Harwood, B., Vijay Kumar, B.G., Carneiro, G., Reid, I., Drummond, T.: Smart mining for deep metric learning. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2821–2829 (2017)

    Google Scholar 

  12. He, K., Fan, H., Wu, Y., Xie, S., Girshick, R.: Momentum contrast for unsupervised visual representation learning. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9729–9738 (2020)

    Google Scholar 

  13. He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016)

    Google Scholar 

  14. Hinton, G., Vinyals, O., Dean, J.: Distilling the knowledge in a neural network. arXiv preprint arXiv:1503.02531 (2015)

  15. Huang, Y., Shen, P., Tai, Y., Li, S., Liu, X., Li, J., Huang, F., Ji, R.: Improving face recognition from hard samples via distribution distillation loss. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12375, pp. 138–154. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-58577-8_9

    Chapter  Google Scholar 

  16. Huang, Y., et al.: CurricularFace: adaptive curriculum learning loss for deep face recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5901–5910 (2020)

    Google Scholar 

  17. Huang, Y., Wu, J., Xu, X., Ding, S.: Evaluation-oriented knowledge distillation for deep face recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 18740–18749, June 2022

    Google Scholar 

  18. InsightFace: Glint-mini face recognition dataset (2021). https://github.com/deepinsight/insightface/tree/master/recognition/_datasets_

  19. Kemelmacher-Shlizerman, I., Seitz, S.M., Miller, D., Brossard, E.: The MegaFace benchmark: 1 million faces for recognition at scale. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4873–4882 (2016)

    Google Scholar 

  20. Kim, Y., Park, W., Shin, J.: BroadFace: looking at tens of thousands of people at once for face recognition. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12354, pp. 536–552. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-58545-7_31

    Chapter  Google Scholar 

  21. Li, Z., Wu, Y., Chen, K., Wu, Y., Zhou, S., Liu, J., Yan, J.: Learning to auto weight: entirely data-driven and highly efficient weighting framework. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp. 4788–4795 (2020)

    Google Scholar 

  22. Liu, J., Guo, J., Xu, D.: APSNet: towards adaptive point sampling for efficient 3D action recognition. IEEE Trans. Image Process. 31, 5287–5302 (2022)

    Article  Google Scholar 

  23. Liu, J., Qin, H., Wu, Y., Liang, D.: AnchorFace: Boosting TAR@FAR for practical face recognition. In: Proceedings of the AAAI Conference on Artificial Intelligence (2022)

    Google Scholar 

  24. Liu, J., Wu, Y., Wu, Y., Li, C., Hu, X., Liang, D., Wang, M.: Dam: Discrepancy alignment metric for face recognition. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 3814–3823 (2021)

    Google Scholar 

  25. Liu, J., Yu, T., Peng, H., Sun, M., Li, P.: Cross-lingual cross-modal consolidation for effective multilingual video corpus moment retrieval. In: NAACL-HLT (2022)

    Google Scholar 

  26. Liu, J., et al.: OneFace: one threshold for all. In: Farinella, T. (ed.) ECCV 2022. LNCS, vol. 13672, pp. 545–561. Springer, Cham (2022)

    Google Scholar 

  27. Liu, J., Zhou, S., Wu, Y., Chen, K., Ouyang, W., Xu, D.: Block proposal neural architecture search. IEEE Trans. Image Process. 30, 15–25 (2020)

    Article  Google Scholar 

  28. Liu, W., Wen, Y., Yu, Z., Li, M., Raj, B., Song, L.: SphereFace: deep hypersphere embedding for face recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 212–220 (2017)

    Google Scholar 

  29. Liu, W., Wen, Y., Yu, Z., Yang, M.: Large-margin Softmax loss for convolutional neural networks. In: ICML, vol. 2, p. 7 (2016)

    Google Scholar 

  30. Maze, B., et al.: IARPA Janus benchmark-C: face dataset and protocol. In: 2018 International Conference on Biometrics (ICB), pp. 158–165. IEEE (2018)

    Google Scholar 

  31. Meng, Q., Zhao, S., Huang, Z., Zhou, F.: MagFace: a universal representation for face recognition and quality assessment. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 14225–14234 (2021)

    Google Scholar 

  32. Park, W., Kim, D., Lu, Y., Cho, M.: Relational knowledge distillation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 3967–3976 (2019)

    Google Scholar 

  33. Peng, B., et al.: Correlation congruence for knowledge distillation. In: ICCV, October 2019

    Google Scholar 

  34. Romero, A., Ballas, N., Kahou, S.E., Chassang, A., Gatta, C., Bengio, Y.: FitNets: hints for thin deep nets. arXiv preprint arXiv:1412.6550 (2014)

  35. Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., Chen, L.C.: MobileNetV2: inverted residuals and linear bottlenecks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4510–4520 (2018)

    Google Scholar 

  36. Schroff, F., Kalenichenko, D., Philbin, J.: FaceNet: a unified embedding for face recognition and clustering. In: CVPR, pp. 815–823 (2015)

    Google Scholar 

  37. Shi, W., Ren, G., Chen, Y., Yan, S.: ProxylessKD: direct knowledge distillation with inherited classifier for face recognition. arXiv preprint arXiv:2011.00265 (2020)

  38. Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014)

  39. Suh, Y., Han, B., Kim, W., Lee, K.M.: Stochastic class-based hard example mining for deep metric learning. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 7251–7259 (2019)

    Google Scholar 

  40. Sun, Y., Chen, Y., Wang, X., Tang, X.: Deep learning face representation by joint identification-verification. In: Advances in Neural Information Processing Systems, pp. 1988–1996 (2014)

    Google Scholar 

  41. Sun, Y., et al.: Circle loss: a unified perspective of pair similarity optimization. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6398–6407 (2020)

    Google Scholar 

  42. Szegedy, C., et al.: Going deeper with convolutions. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2015

    Google Scholar 

  43. Taigman, Y., Yang, M., Ranzato, M., Wolf, L.: DeepFace: closing the gap to human-level performance in face verification. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1701–1708 (2014)

    Google Scholar 

  44. Tian, Y., Krishnan, D., Isola, P.: Contrastive representation distillation. In: ICLR (2020)

    Google Scholar 

  45. Tung, F., Mori, G.: Similarity-preserving knowledge distillation. In: Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), October 2019

    Google Scholar 

  46. Wang, F., Cheng, J., Liu, W., Liu, H.: Additive margin Softmax for face verification. IEEE Signal Proc. Lett. 25(7), 926–930 (2018)

    Article  Google Scholar 

  47. Wang, F., Xiang, X., Cheng, J., Yuille, A.L.: NormFace: L2 hypersphere embedding for face verification. In: Proceedings of the 25th ACM International Conference on Multimedia, pp. 1041–1049 (2017)

    Google Scholar 

  48. Wang, H., et al.: CosFace: large margin cosine loss for deep face recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5265–5274 (2018)

    Google Scholar 

  49. Wang, X., Fu, T., Liao, S., Wang, S., Lei, Z., Mei, T.: Exclusivity-consistency regularized knowledge distillation for face recognition. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12369, pp. 325–342. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-58586-0_20

    Chapter  Google Scholar 

  50. Wang, X., Zhang, S., Wang, S., Fu, T., Shi, H., Mei, T.: Mis-classified vector guided softmax loss for face recognition. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, pp. 12241–12248 (2020)

    Google Scholar 

  51. Whitelam, C., et al.: IARPA Janus Benchmark-B face dataset. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp. 90–98 (2017)

    Google Scholar 

  52. Wolf, L., Hassner, T., Maoz, I.: Face recognition in unconstrained videos with matched background similarity. IEEE (2011)

    Google Scholar 

  53. Wu, C.Y., Manmatha, R., Smola, A.J., Krahenbuhl, P.: Sampling matters in deep embedding learning. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2840–2848 (2017)

    Google Scholar 

  54. Yang, C., An, Z., Cai, L., Xu, Y.: Hierarchical self-supervised augmented knowledge distillation. In: Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence, pp. 1217–1223 (2021)

    Google Scholar 

  55. Yang, C., An, Z., Cai, L., Xu, Y.: Knowledge distillation using hierarchical self-supervision augmented distribution. IEEE Tran. Neural Netw. Learn. Syst. (2022)

    Google Scholar 

  56. Yang, C., Zhou, H., An, Z., Jiang, X., Xu, Y., Zhang, Q.: Cross-image relational knowledge distillation for semantic segmentation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 12319–12328 (2022)

    Google Scholar 

  57. Yi, D., Lei, Z., Liao, S., Li, S.Z.: Learning face representation from scratch. arXiv preprint arXiv:1411.7923 (2014)

  58. Zhang, X., Fang, Z., Wen, Y., Li, Z., Qiao, Y.: Range loss for deep face recognition with long-tailed training data. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 5409–5418 (2017)

    Google Scholar 

  59. Zhu, Z., et al.: WebFace260M: a benchmark unveiling the power of million-scale deep face recognition. In: CVPR, pp. 10492–10502, June 2021

    Google Scholar 

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Acknowledgments

This research was supported by National Natural Science Foundation of China under Grant 61932002.

Author information

Authors and Affiliations

  1. State Key Lab of Software Development Environment, Beihang University, Beijing, China

    Jiaheng Liu & Ke Xu

  2. SenseTime Group Limited, Beijing, China

    Haoyu Qin, Yichao Wu & Ding Liang

  3. SKLSDE, Institute of Artificial Intelligence, Beihang University, Beijing, China

    Jinyang Guo

Authors
  1. Jiaheng Liu
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  2. Haoyu Qin
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  3. Yichao Wu
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  4. Jinyang Guo
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  5. Ding Liang
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  6. Ke Xu
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Corresponding author

Correspondence to Haoyu Qin .

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Editors and Affiliations

  1. Tel Aviv University, Tel Aviv, Israel

    Shai Avidan

  2. University College London, London, UK

    Gabriel Brostow

  3. Google AI, Accra, Ghana

    Moustapha Cissé

  4. University of Catania, Catania, Italy

    Giovanni Maria Farinella

  5. Facebook (United States), Menlo Park, CA, USA

    Tal Hassner

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Liu, J., Qin, H., Wu, Y., Guo, J., Liang, D., Xu, K. (2022). CoupleFace: Relation Matters for Face Recognition Distillation. In: Avidan, S., Brostow, G., Cissé, M., Farinella, G.M., Hassner, T. (eds) Computer Vision – ECCV 2022. ECCV 2022. Lecture Notes in Computer Science, vol 13672. Springer, Cham. https://doi.org/10.1007/978-3-031-19775-8_40

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  • DOI: https://doi.org/10.1007/978-3-031-19775-8_40

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