Skip to main content
SpringerLink
Log in

Improving Face Recognition from Hard Samples via Distribution Distillation Loss

  • Conference paper
  • First Online:
Computer Vision – ECCV 2020 (ECCV 2020)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 12375))

Included in the following conference series:

Abstract

Large facial variations are the main challenge in face recognition. To this end, previous variation-specific methods make full use of task-related prior to design special network losses, which are typically not general among different tasks and scenarios. In contrast, the existing generic methods focus on improving the feature discriminability to minimize the intra-class distance while maximizing the inter-class distance, which perform well on easy samples but fail on hard samples. To improve the performance on hard samples, we propose a novel Distribution Distillation Loss to narrow the performance gap between easy and hard samples, which is simple, effective and generic for various types of facial variations. Specifically, we first adopt state-of-the-art classifiers such as Arcface to construct two similarity distributions: a teacher distribution from easy samples and a student distribution from hard samples. Then, we propose a novel distribution-driven loss to constrain the student distribution to approximate the teacher distribution, which thus leads to smaller overlap between the positive and negative pairs in the student distribution. We have conducted extensive experiments on both generic large-scale face benchmarks and benchmarks with diverse variations on race, resolution and pose. The quantitative results demonstrate the superiority of our method over strong baselines, e.g., Arcface and Cosface. Code will be available at https://github.com/HuangYG123/DDL.

Y. Huang and P. Shen—Equal contribution.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Abadi, M., et al.: TensorFlow: large-scale machine learning on heterogeneous systems (2015). http://tensorflow.org/. software available from tensorflow.org

  2. Cao, K., Rong, Y., Li, C., Tang, X., Change Loy, C.: Pose-robust face recognition via deep residual equivariant mapping. In: CVPR, pp. 5187–5196 (2018)

    Google Scholar 

  3. Cao, Q., Shen, L., Xie, W., Parkhi, O.M., Zisserman, A.: Vggface2: a dataset for recognising faces across pose and age. In: FG, pp. 67–74. IEEE (2018)

    Google Scholar 

  4. Chen, Y., Tai, Y., Liu, X., Shen, C., Yang, J.: FSRNet: end-to-end learning face super-resolution with facial priors. In: CVPR, pp. 2492–2501 (2018)

    Google Scholar 

  5. Deng, J., Cheng, S., Xue, N., Zhou, Y., Zafeiriou, S.: UV-GAN: adversarial facial UV map completion for pose-invariant face recognition. In: CVPR, pp. 7093–7102 (2018)

    Google Scholar 

  6. Deng, J., Guo, J., Xue, N., Zafeiriou, S.: Arcface: additive angular margin loss for deep face recognition. In: CVPR, pp. 4690–4699 (2019)

    Google Scholar 

  7. Deng, J., Zhou, Y., Zafeiriou, S.: Marginal loss for deep face recognition. In: CVPR Workshops, pp. 60–68 (2017)

    Google Scholar 

  8. Gong, S., Liu, X., Jain, A.: Jointly de-biasing face recognition and demographic attribute estimation. In: ECCV (2020)

    Google Scholar 

  9. Goodfellow, I., et al.: Generative adversarial nets. In: NIPS, pp. 2672–2680 (2014)

    Google Scholar 

  10. Grgic, M., Delac, K., Grgic, S.: SCface-surveillance cameras face database. Multimedia Tools Appli. 51(3), 863–879 (2011). https://doi.org/10.1007/s11042-009-0417-2

    Article  Google Scholar 

  11. Hennings-Yeomans, P.H., Baker, S., Kumar, B.V.: Simultaneous super-resolution and feature extraction for recognition of low-resolution faces. In: CVPR, pp. 1–8. IEEE (2008)

    Google Scholar 

  12. Hinton, G., Vinyals, O., Dean, J.: Distilling the knowledge in a neural network. In: NIPS Workshop (2014)

    Google Scholar 

  13. Huang, Y., et al.: CurricularFace: adaptive curriculum learning loss for deep face recognition. In: CVPR (2020)

    Google Scholar 

  14. Huang, Z., Wang, N.: Like what you like: knowledge distill via neuron selectivity transfer (2017). arXiv:1707.01219v2

  15. Huang, Z., et al.: A benchmark and comparative study of video-based face recognition on cox face database. IEEE Trans. Image Process. 24(12), 5967–5981 (2015)

    Article  MathSciNet  MATH  Google Scholar 

  16. Lei, Z., Ahonen, T., Pietikäinen, M., Li, S.Z.: Local frequency descriptor for low-resolution face recognition. In: FG, pp. 161–166. IEEE (2011)

    Google Scholar 

  17. Lin, T.Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: ICCV, pp. 2980–2988 (2017)

    Google Scholar 

  18. Liu, W., Wen, Y., Yu, Z., Li, M., Raj, B., Song, L.: Sphereface: deep hypersphere embedding for face recognition. In: CVPR, pp. 212–220 (2017)

    Google Scholar 

  19. 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 

  20. Lu, Z., Jiang, X., Kot, A.: Deep coupled resnet for low-resolution face recognition. IEEE Sig. Process. Lett. 25(4), 526–530 (2018)

    Article  Google Scholar 

  21. Maaten, L.V.D., Hinton, G.: Visualizing data using t-SNE. J. Mach. Learn. Res. 9(Nov), 2579–2605 (2008)

    MATH  Google Scholar 

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

    Google Scholar 

  23. Parkhi, O.M., Vedaldi, A., Zisserman, A., et al.: Deep face recognition. In: BMVC, vol. 1, p. 6 (2015)

    Google Scholar 

  24. Peng, X., Yu, X., Sohn, K., Metaxas, D.N., Chandraker, M.: Reconstruction-based disentanglement for pose-invariant face recognition. In: ICCV, pp. 1623–1632 (2017)

    Google Scholar 

  25. Ruiz, N., Chong, E., Rehg, J.M.: Fine-grained head pose estimation without keypoints. In: CVPR Workshops, pp. 2074–2083 (2018)

    Google Scholar 

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

    Google Scholar 

  27. Shekhar, S., Patel, V.M., Chellappa, R.: Synthesis-based recognition of low resolution faces. In: IJCB, pp. 1–6. IEEE (2011)

    Google Scholar 

  28. Shrivastava, A., Gupta, A., Girshick, R.: Training region-based object detectors with online hard example mining. In: CVPR, pp. 761–769 (2016)

    Google Scholar 

  29. Sun, Y., Chen, Y., Wang, X., Tang, X.: Deep learning face representation by joint identification-verification. In: NIPS, pp. 1988–1996 (2014)

    Google Scholar 

  30. Sun, Y., Wang, X., Tang, X.: Deep learning face representation from predicting 10,000 classes. In: CVPR, pp. 1891–1898 (2014)

    Google Scholar 

  31. Tai, Y., Yang, J., Liu, X.: Image super-resolution via deep recursive residual network. In: CVPR, pp. 3147–3155 (2017)

    Google Scholar 

  32. Tai, Y., Yang, J., Liu, X., Xu, C.: Memnet: a persistent memory network for image restoration. In: ICCV, pp. 4539–4547 (2017)

    Google Scholar 

  33. Tai, Y., Yang, J., Zhang, Y., Luo, L., Qian, J., Chen, Y.: Face recognition with pose variations and misalignment via orthogonal procrustes regression. IEEE Trans. Image Process. 25(6), 2673–2683 (2016)

    Article  MathSciNet  MATH  Google Scholar 

  34. Taigman, Y., Yang, M., Ranzato, M., Wolf, L.: Deepface: closing the gap to human-level performance in face verification. In: CVPR, pp. 1701–1708 (2014)

    Google Scholar 

  35. Tran, L., Yin, X., Liu, X.: Disentangled representation learning GAN for pose-invariant face recognition. In: CVPR, pp. 1415–1424 (2017)

    Google Scholar 

  36. Tung, F., Mori, G.: Similarity-preserving knowledge distillation. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1365–1374 (2019)

    Google Scholar 

  37. Ustinova, E., Lempitsky, V.: Learning deep embeddings with histogram loss. In: NIPS, pp. 4170–4178 (2016)

    Google Scholar 

  38. Wang, F., Cheng, J., Liu, W., Liu, H.: Additive margin softmax for face verification. IEEE Sig. Process. Lett. 25(7), 926–930 (2018)

    Article  Google Scholar 

  39. Wang, F., Xiang, X., Cheng, J., Yuille, A.L.: Normface: L2 hypersphere embedding for face verification. In: ACMMM, pp. 1041–1049. ACM (2017)

    Google Scholar 

  40. Wang, H., et al.: Cosface: large margin cosine loss for deep face recognition. In: CVPR, pp. 5265–5274 (2018)

    Google Scholar 

  41. Wang, X., Wang, S., Wang, J., Shi, H., Mei, T.: Co-mining: deep face recognition with noisy labels. In: ICCV, pp. 9358–9367 (2019)

    Google Scholar 

  42. Wang, X., Wang, S., Zhang, S., Fu, T., Shi, H., Mei, T.: Support vector guided softmax loss for face recognition (2018). arXiv:1812.11317

  43. Wen, Y., Zhang, K., Li, Z., Qiao, Yu.: A discriminative feature learning approach for deep face recognition. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9911, pp. 499–515. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46478-7_31

    Chapter  Google Scholar 

  44. Xie, W., Shen, L., Zisserman, A.: Comparator networks. In: ECCV, pp. 782–797 (2018)

    Google Scholar 

  45. Xie, W., Zisserman, A.: Multicolumn networks for face recognition. In: BMVC (2018)

    Google Scholar 

  46. Yang, F., Yang, W., Gao, R., Liao, Q.: Discriminative multidimensional scaling for low-resolution face recognition. IEEE Sig. Process. Lett. 25(3), 388–392 (2017)

    Article  Google Scholar 

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

  48. Yin, X., Liu, X.: Multi-task convolutional neural network for pose-invariant face recognition. IEEE Trans. Image Process. 27(2), 964–975 (2017)

    Article  MathSciNet  MATH  Google Scholar 

  49. Yin, X., Yu, X., Sohn, K., Liu, X., Chandraker, M.: Towards large-pose face frontalization in the wild. In: ICCV, pp. 3990–3999 (2017)

    Google Scholar 

  50. Zhang, K., et al.: Super-identity convolutional neural network for face hallucination. In: ECCV, pp. 183–198 (2018)

    Google Scholar 

  51. Zhang, K., Zhang, Z., Li, Z., Qiao, Y.: Joint face detection and alignment using multitask cascaded convolutional networks. IEEE Sig. Process. Lett. 23(10), 1499–1503 (2016)

    Article  Google Scholar 

  52. Zhang, X., Fang, Z., Wen, Y., Li, Z., Qiao, Y.: Range loss for deep face recognition with long-tailed training data. In: ICCV, pp. 5409–5418 (2017)

    Google Scholar 

  53. Zhang, Y., Xiang, T., Hospedales, T.M., Lu, H.: Deep mutual learning. In: CVPR, pp. 4320–4328 (2018)

    Google Scholar 

  54. Zhao, J., et al.: 3D-aided deep pose-invariant face recognition. In: IJCAI, vol. 2, p. 11 (2018)

    Google Scholar 

  55. Zou, W.W., Yuen, P.C.: Very low resolution face recognition problem. IEEE Trans. Image Process. 21(1), 327–340 (2011)

    Article  MathSciNet  MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Youtu Lab, Tencent, Shanghai, China

    Yuge Huang, Pengcheng Shen, Ying Tai, Shaoxin Li, Jilin Li & Feiyue Huang

  2. Michigan State University, East Lansing, USA

    Xiaoming Liu

  3. Xiamen University, Xiamen, China

    Rongrong Ji

Authors
  1. Yuge Huang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Pengcheng Shen
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ying Tai
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Shaoxin Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Xiaoming Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Jilin Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Feiyue Huang
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Rongrong Ji
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to Ying Tai or Shaoxin Li .

Editor information

Editors and Affiliations

  1. University of Oxford, Oxford, UK

    Andrea Vedaldi

  2. Graz University of Technology, Graz, Austria

    Horst Bischof

  3. University of Freiburg, Freiburg im Breisgau, Germany

    Thomas Brox

  4. University of North Carolina at Chapel Hill, Chapel Hill, NC, USA

    Jan-Michael Frahm

1 Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary material 1 (pdf 1347 KB)

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Huang, Y. et al. (2020). Improving Face Recognition from Hard Samples via Distribution Distillation Loss. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, JM. (eds) Computer Vision – ECCV 2020. ECCV 2020. Lecture Notes in Computer Science(), vol 12375. Springer, Cham. https://doi.org/10.1007/978-3-030-58577-8_9

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-58577-8_9

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-58576-1

  • Online ISBN: 978-3-030-58577-8

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation