Skip to main content

Causality-Inspired Discriminative Feature Learning in Triple Domains for Gait Recognition

  • Conference paper
  • First Online:
Computer Vision – ECCV 2024 (ECCV 2024)

Abstract

Gait recognition is a biometric technology that distinguishes individuals by their walking patterns. However, previous methods face challenges when accurately extracting identity features because they often become entangled with non-identity clues. To address this challenge, we propose CLTD, a causality-inspired discriminative feature learning module designed to effectively eliminate the influence of confounders in triple domains, i.e., spatial, temporal, and spectral. Specifically, we utilize the Cross Pixel-wise Attention Generator (CPAG) to generate attention distributions for factual and counterfactual features in spatial and temporal domains. Then, we introduce the Fourier Projection Head (FPH) to project spatial features into the spectral space, which preserves essential information while reducing computational costs. Additionally, we employ an optimization method with contrastive learning to enforce semantic consistency constraints across sequences from the same subject. Our approach has demonstrated significant performance improvements on challenging datasets, proving its effectiveness. Moreover, it can be seamlessly integrated into existing gait recognition methods.

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

Access this chapter

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Abraham, E.D., et al.: CEBaB: estimating the causal effects of real-world concepts on NLP model behavior. In: Advances in Neural Information Processing Systems, vol. 35, pp. 17582–17596 (2022)

    Google Scholar 

  2. Chai, T., Li, A., Zhang, S., Li, Z., Wang, Y.: Lagrange motion analysis and view embeddings for improved gait recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 20249–20258 (2022)

    Google Scholar 

  3. Chao, H., He, Y., Zhang, J., Feng, J.: GaitSet: regarding gait as a set for cross-view gait recognition. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp. 8126–8133 (2019)

    Google Scholar 

  4. Chen, J., Gao, Z., Wu, X., Luo, J.: Meta-causal learning for single domain generalization. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 7683–7692 (2023)

    Google Scholar 

  5. Ding, S., Feng, F., He, X., Liao, Y., Shi, J., Zhang, Y.: Causal incremental graph convolution for recommender system retraining. IEEE Trans. Neural Netw. Learn. Syst. (2022)

    Google Scholar 

  6. Dosovitskiy, A., et al.: An image is worth \(16\times 16\) words: transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020)

  7. Dou, H., Zhang, P., Su, W., Yu, Y., Li, X.: MetaGait: learning to learn an omni sample adaptive representation for gait recognition. In: Avidan, S., Brostow, G., Cissé, M., Farinella, G.M., Hassner, T. (eds.) ECCV 2022. LNCS, vol. 13665, pp. 357–374. Springer, Cham (2022). https://doi.org/10.1007/978-3-031-20065-6_21

    Chapter  Google Scholar 

  8. Dou, H., Zhang, P., Su, W., Yu, Y., Lin, Y., Li, X.: GaitGCI: generative counterfactual intervention for gait recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5578–5588 (2023)

    Google Scholar 

  9. Fan, C., Liang, J., Shen, C., Hou, S., Huang, Y., Yu, S.: OpenGait: revisiting gait recognition towards better practicality. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9707–9716 (2023)

    Google Scholar 

  10. Fan, C., et al.: GaitPart: temporal part-based model for gait recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 14225–14233 (2020)

    Google Scholar 

  11. Fu, Y., Meng, S., Hou, S., Hu, X., Huang, Y.: GPGait: generalized pose-based gait recognition. In: Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), pp. 19595–19604 (2023)

    Google Scholar 

  12. Guo, H., Ji, Q.: Physics-augmented autoencoder for 3D skeleton-based gait recognition. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 19627–19638 (2023)

    Google Scholar 

  13. Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive learning from extremely augmented skeleton sequences for self-supervised action recognition. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp. 762–770 (2022)

    Google Scholar 

  14. Gutmann, M., Hyvärinen, A.: Noise-contrastive estimation: a new estimation principle for unnormalized statistical models. In: Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, pp. 297–304. JMLR Workshop and Conference Proceedings (2010)

    Google Scholar 

  15. Han, J., Bhanu, B.: Individual recognition using gait energy image. IEEE Trans. Pattern Anal. Mach. Intell. 28(2), 316–322 (2005)

    Article  Google Scholar 

  16. Hermans, A., Beyer, L., Leibe, B.: In defense of the triplet loss for person re-identification. arXiv preprint arXiv:1703.07737 (2017)

  17. Hou, R., Chang, H., Ma, B., Huang, R., Shan, S.: BiCnet-TKS: learning efficient spatial-temporal representation for video person re-identification. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2014–2023 (2021)

    Google Scholar 

  18. Hou, S., Cao, C., Liu, X., Huang, Y.: Gait lateral network: learning discriminative and compact representations for gait recognition. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12354, pp. 382–398. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-58545-7_22

    Chapter  Google Scholar 

  19. Huang, X., Wang, X., He, B., He, S., Liu, W., Feng, B.: Star: spatio-temporal augmented relation network for gait recognition. IEEE Trans. Biometrics Behav. Identity Sci. 5(1), 115–125 (2022)

    Article  Google Scholar 

  20. Huang, X., et al.: Condition-adaptive graph convolution learning for skeleton-based gait recognition. IEEE Trans. Image Process. (2023)

    Google Scholar 

  21. Huang, X., et al.: Context-sensitive temporal feature learning for gait recognition. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 12909–12918 (2021)

    Google Scholar 

  22. Huang, Z., et al.: 3D local convolutional neural networks for gait recognition. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14920–14929 (2021)

    Google Scholar 

  23. Huang, Z., Wang, X., Huang, L., Huang, C., Wei, Y., Liu, W.: CCNet: criss-cross attention for semantic segmentation. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 603–612 (2019)

    Google Scholar 

  24. Lee, B.K., Kim, J., Ro, Y.M.: Mitigating adversarial vulnerability through causal parameter estimation by adversarial double machine learning. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 4499–4509 (2023)

    Google Scholar 

  25. Lee, S., Bae, J., Kim, H.Y.: Decompose, adjust, compose: effective normalization by playing with frequency for domain generalization. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11776–11785 (2023)

    Google Scholar 

  26. Li, C., et al.: Embedding Fourier for ultra-high-definition low-light image enhancement. In: ICLR (2023)

    Google Scholar 

  27. Li, X., Makihara, Y., Xu, C., Yagi, Y., Ren, M.: Gait recognition via semi-supervised disentangled representation learning to identity and covariate features. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 13309–13319 (2020)

    Google Scholar 

  28. Li, X., et al.: Causally-aware intraoperative imputation for overall survival time prediction. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 15681–15690 (2023)

    Google Scholar 

  29. Lin, B., Zhang, S., Yu, X.: Gait recognition via effective global-local feature representation and local temporal aggregation. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14648–14656 (2021)

    Google Scholar 

  30. Loper, M., Mahmood, N., Romero, J., Pons-Moll, G., Black, M.J.: SMPL: a skinned multi-person linear model. ACM Trans. Graph. 34(6) (2015)

    Google Scholar 

  31. Ma, K., Fu, Y., Zheng, D., Cao, C., Hu, X., Huang, Y.: Dynamic aggregated network for gait recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 22076–22085 (2023)

    Google Scholar 

  32. Van der Maaten, L., Hinton, G.: Visualizing data using t-SNE. J. Mach. Learn. Res. 9(11) (2008)

    Google Scholar 

  33. Mao, X., Liu, Y., Liu, F., Li, Q., Shen, W., Wang, Y.: Intriguing findings of frequency selection for image deblurring. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp. 1905–1913 (2023)

    Google Scholar 

  34. Miao, J., Chen, C., Liu, F., Wei, H., Heng, P.A.: CauSSL: causality-inspired semi-supervised learning for medical image segmentation. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 21426–21437 (2023)

    Google Scholar 

  35. van den Oord, A., Li, Y., Vinyals, O.: Representation learning with contrastive predictive coding. arXiv preprint arXiv:1807.03748 (2018)

  36. Paszke, A., et al.: PyTorch: an imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems, vol. 32 (2019)

    Google Scholar 

  37. Pearl, J., Glymour, M., Jewell, N.P.: Causal Inference in Statistics: A Primer. Wiley, Hoboken (2016)

    Google Scholar 

  38. Quan, S., Hirano, M., Yamakawa, Y.: Semantic information in contrastive learning. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 5686–5696 (2023)

    Google Scholar 

  39. Rao, H., Miao, C.: TranSG: transformer-based skeleton graph prototype contrastive learning with structure-trajectory prompted reconstruction for person re-identification. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 22118–22128 (2023)

    Google Scholar 

  40. Sarkar, S., Phillips, P.J., Liu, Z., Vega, I.R., Grother, P., Bowyer, K.W.: The humanid gait challenge problem: data sets, performance, and analysis. IEEE Trans. Pattern Anal. Mach. Intell. 27(2), 162–177 (2005)

    Article  Google Scholar 

  41. Shen, C., Yu, S., Wang, J., Huang, G.Q., Wang, L.: A comprehensive survey on deep gait recognition: algorithms, datasets and challenges. arXiv preprint arXiv:2206.13732 (2022)

  42. Shiraga, K., Makihara, Y., Muramatsu, D., Echigo, T., Yagi, Y.: GEINet: view-invariant gait recognition using a convolutional neural network. In: 2016 International Conference on Biometrics (ICB), pp. 1–8. IEEE (2016)

    Google Scholar 

  43. Takemura, N., Makihara, Y., Muramatsu, D., Echigo, T., Yagi, Y.: Multi-view large population gait dataset and its performance evaluation for cross-view gait recognition. IPSJ Trans. Comput. Vis. Appl. 10, 1–14 (2018)

    Google Scholar 

  44. Tang, K., Niu, Y., Huang, J., Shi, J., Zhang, H.: Unbiased scene graph generation from biased training. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 3716–3725 (2020)

    Google Scholar 

  45. Teepe, T., Gilg, J., Herzog, F., Hörmann, S., Rigoll, G.: Towards a deeper understanding of skeleton-based gait recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 1569–1577 (2022)

    Google Scholar 

  46. Teepe, T., Khan, A., Gilg, J., Herzog, F., Hörmann, S., Rigoll, G.: GaitGraph: graph convolutional network for skeleton-based gait recognition. In: 2021 IEEE International Conference on Image Processing (ICIP), pp. 2314–2318. IEEE (2021)

    Google Scholar 

  47. Vaswani, A., et al.: Attention is all you need. In: Advances in Neural Information Processing Systems, vol. 30 (2017)

    Google Scholar 

  48. Wang, J., et al.: Causal intervention for sparse-view gait recognition. In: Proceedings of the 31st ACM International Conference on Multimedia, pp. 77–85 (2023)

    Google Scholar 

  49. Wang, L., Liu, B., Liang, F., Wang, B.: Hierarchical spatio-temporal representation learning for gait recognition. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 19639–19649 (2023)

    Google Scholar 

  50. Wang, M., et al.: DyGait: exploiting dynamic representations for high-performance gait recognition. In: Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), pp. 13424–13433 (2023)

    Google Scholar 

  51. Yang, X., Zhang, H., Qi, G., Cai, J.: Causal attention for vision-language tasks. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9847–9857 (2021)

    Google Scholar 

  52. Yang, Z., Lin, M., Zhong, X., Wu, Y., Wang, Z.: Good is bad: causality inspired cloth-debiasing for cloth-changing person re-identification. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 1472–1481 (2023)

    Google Scholar 

  53. Yu, S., Tan, D., Tan, T.: A framework for evaluating the effect of view angle, clothing and carrying condition on gait recognition. In: 18th International Conference on Pattern Recognition (ICPR 2006), vol. 4, pp. 441–444. IEEE (2006)

    Google Scholar 

  54. Zhang, C., Chen, X.P., Han, G.Q., Liu, X.J.: Spatial transformer network on skeleton-based gait recognition. Expert Syst. e13244 (2023)

    Google Scholar 

  55. Zhang, Q.: Probabilistic reasoning based on dynamic causality trees/diagrams. Reliab. Eng. Syst. Saf. 46(3), 209–220 (1994)

    Article  Google Scholar 

  56. Zhao, Z., Wang, D., Zhao, X.: Movement enhancement toward multi-scale video feature representation for temporal action detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 13555–13564 (2023)

    Google Scholar 

  57. Zheng, J., et al.: Gait recognition in the wild with multi-hop temporal switch. In: Proceedings of the 30th ACM International Conference on Multimedia, pp. 6136–6145 (2022)

    Google Scholar 

  58. Zheng, J., Liu, X., Liu, W., He, L., Yan, C., Mei, T.: Gait recognition in the wild with dense 3D representations and a benchmark. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 20228–20237 (2022)

    Google Scholar 

  59. Zhou, M., Huang, J., Guo, C.L., Li, C.: Fourmer: an efficient global modeling paradigm for image restoration. In: International Conference on Machine Learning, pp. 42589–42601. PMLR (2023)

    Google Scholar 

  60. Zhu, Z., et al.: Gait recognition in the wild: a benchmark. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14789–14799 (2021)

    Google Scholar 

Download references

Acknowledgements

This work was supported by the National Key R&D Program of China under project 2023YFF0905401.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Bin Feng .

Editor information

Editors and Affiliations

1 Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary material 1 (pdf 150 KB)

Rights and permissions

Reprints and permissions

Copyright information

© 2025 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Xiong, H., Feng, B., Wang, X., Liu, W. (2025). Causality-Inspired Discriminative Feature Learning in Triple Domains for Gait Recognition. In: Leonardis, A., Ricci, E., Roth, S., Russakovsky, O., Sattler, T., Varol, G. (eds) Computer Vision – ECCV 2024. ECCV 2024. Lecture Notes in Computer Science, vol 15112. Springer, Cham. https://doi.org/10.1007/978-3-031-72949-2_15

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-72949-2_15

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-72948-5

  • Online ISBN: 978-3-031-72949-2

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics