Skip to main content

Advertisement

Log in

GaitLRDF: gait recognition via local relevant feature representation and discriminative feature learning

  • Published:
Applied Intelligence Aims and scope Submit manuscript

Abstract

As an emerging biometric recognition technology, gait recognition has the advantages of non-contact long distance and difficult to imitate. Existing gait recognition methods perform gait recognition by using features extracted from the overall appearance or local regions of humans. However, the detailed features extracted by current gait recognition methods based on human local region lose the overall relevance of the image and the edge information of human local region. Secondly, the method based on the local area of the human body does not focus on the local parts of the human body that are less affected by clothing occlusion. To solve the above problems, this paper proposes a new gait recognition network framework GaitLRDF, which improves the accuracy and robustness of gait recognition by Local Relation Convolutional layers (LRConv) and Human Body Focusing module(HBF). LRConv can simultaneously use the global and local information of the human body, and the local detail features extracted in the module can retain the edge information of the human body. HBF can focuse on the gait parts that are less affected by clothing occlusion, and obtain more discriminative gait detail features. The experimental results show that in the three gait environments of NM, BG and CL set by CASIA-B dataset, GaitLRDF is 0.40%, 0.10% and 1.10% higher than the current most advanced method respectively. The recognition accuracy on OU-MVLP dataset reaches 91.40%.

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

Access this article

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

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

Data Availability and Access

We evaluate our proposed method on public datasets CASIA-B and OU-MVLP.The CASIA-B dataset is available at http://www.cbsr.ia.ac.cn/english/Gait%20Databases.asp. The OU-MVLP dataset is available at http://www.am.sanken.osaka-u.ac.jp/BiometricDB/GaitMVLP.html.

References

  1. Connor P, Ross A (2018) Biometric recognition by gait: a survey of modalities and features. Comput Vis Image Underst 167:1–27

    Article  Google Scholar 

  2. Liao R, Cao C, Garcia EB, Yu S, Huang Y (2017) Pose-based temporal-spatial network (ptsn) for gait recognition with carrying and clothing variations. In: Zhou J, Wang Y, Sun Z, Xu Y, Shen L, Feng J, Shan S, Qiao Y, Guo Z, Yu S (eds) Biometric Recognition, vol 10568. Springer, Cham, pp 474–483

    Chapter  Google Scholar 

  3. Yu S, Tan D, Tan T (2006) A framework for evaluating the effect of view angle, clothing and carrying condition on gait recognition. In: 18th International conference on pattern recognition (ICPR’06), vol 4, pp 441–444

  4. Shiraga K, Makihara Y, Muramatsu D, Echigo T, Yagi Y (2016) Geinet: view-invariant gait recognition using a convolutional neural network. In: 2016 International conference on biometrics (ICB), pp 1–8

  5. Sepas-Moghaddam A, Etemad A (2023) Deep gait recognition: a survey. IEEE Trans Pattern Anal Mach Intell 45(1):264–284

    Article  Google Scholar 

  6. Chao H, Wang K, He Y, Zhang J, Feng J (2022) Gaitset: cross-view gait recognition through utilizing gait as a deep set. IEEE Trans Pattern Anal Mach Intell 44(7):3467–3478

    Google Scholar 

  7. Huang X, Zhu D, Wang H, Wang X, Yang B, He B, Liu W, Feng B (2021) Context-sensitive temporal feature learning for gait recognition. In: Proceedings of the IEEE/CVF international conference on computer vision (ICCV), pp 12909–12918

  8. Zhang Y, Huang Y, Yu S, Wang L (2020) Cross-view gait recognition by discriminative feature learning. IEEE Trans Image Process 29:1001–1015

    Article  MathSciNet  Google Scholar 

  9. Fan C, Peng Y, Cao C, Liu X, Hou S, Chi J, Huang Y, Li Q, He Z (2020) Gaitpart: temporal part-based model for gait recognition. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition (CVPR), pp 14225–14233

  10. Lin B, Zhang S, Yu X (2021) Gait recognition via effective global-local feature representation and local temporal aggregation. In: Proceedings of the IEEE/CVF international conference on computer vision (ICCV), pp 14648–14656

  11. Deng M, Wang C, Cheng F, Zeng W (2017) Fusion of spatial-temporal and kinematic features for gait recognition with deterministic learning. Pattern Recognit 67:186–200

  12. Tang J, Luo J, Tjahjadi T, Guo F (2017) Robust arbitrary-view gait recognition based on 3d partial similarity matching. IEEE Trans Image Process 26(1):7–22

    Article  MathSciNet  Google Scholar 

  13. Li X, Makihara Y, Xu C, Yagi Y, Yu S, Ren M (2020) End-to-end model-based gait recognition. In: Proceedings of the Asian conference on computer vision (ACCV)

  14. Teepe T, Khan A, Gilg J, Herzog F, Hörmann S, Rigoll G (2021) Gaitgraph: graph convolutional network for skeleton-based gait recognition. In: 2021 IEEE International conference on image processing (ICIP), pp 2314–2318

  15. Han J, Bhanu B (2006) Individual recognition using gait energy image. IEEE Trans Pattern Anal Mach Intell 28(2):316–322

    Article  Google Scholar 

  16. Wang C, Zhang J, Wang L, Pu J, Yuan X (2012) Human identification using temporal information preserving gait template. IEEE Trans Pattern Anal Mach Intell 34(11):2164–2176

    Article  Google Scholar 

  17. Kusakunniran W, Wu Q, Li H, Zhang J (2009) Multiple views gait recognition using view transformation model based on optimized gait energy image. In: 2009 IEEE 12th International conference on computer vision workshops, ICCV Workshops, pp 1058–1064

  18. Zheng S, Zhang J, Huang K, He R, Tan T (2011) Robust view transformation model for gait recognition. In: 2011 18th IEEE International conference on image processing, pp 2073–2076

  19. Wu Z, Huang Y, Wang L, Wang X, Tan T (2017) A comprehensive study on cross-view gait based human identification with deep cnns. IEEE Trans Pattern Anal Mach Intell 39(2):209–226

    Article  Google Scholar 

  20. Yu S, Chen H, Garcia Reyes EB, Poh N (2017) Gaitgan: invariant gait feature extraction using generative adversarial networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR) Workshops, pp 30–37

  21. Yu S, Liao R, An W, Chen H, García EB, Huang Y, Poh N (2019) Gaitganv2: invariant gait feature extraction using generative adversarial networks. Pattern Recognit 87:179–189

  22. Liao R, An W, Li Z, Bhattacharyya SS (2021) A novel view synthesis approach based on view space covering for gait recognition. Neurocomputing 453:13–25

  23. Zhang Z, Tran L, Liu F, Liu X (2020) On learning disentangled representations for gait recognition. IEEE Trans Pattern Anal Mach Intell 44(1):345–360

    Article  Google Scholar 

  24. Li G, Guo L, Zhang R, Qian J, Gao S (2023) Transgait: multimodal-based gait recognition with set transformer. Appl Intell 53(2):1535–1547

    Article  Google Scholar 

  25. Li H, Qiu Y, Zhao H, Zhan J, Chen R, Wei T, Huang Z (2022) Gaitslice: a gait recognition model based on spatio-temporal slice features. Pattern Recognit 124:108453

  26. Zhao L, Guo L, Zhang R, Xie X, Ye X (2022) mmgaitset: multimodal based gait recognition for countering carrying and clothing changes. Appl Intell 52(2):2023–2036

    Article  Google Scholar 

  27. Liang J, Fan C, Hou S, Shen C, Huang Y, Yu S (2022) Gaitedge: beyond plain end-to-end gait recognition for better practicality. In: Avidan S, Brostow G, Cissé M, Farinella GM, Hassner T (eds) Computer Vision - ECCV 2022. Springer, Cham, pp 375–390

  28. Fu Y, Wei Y, Zhou Y, Shi H, Huang G, Wang X, Yao Z, Huang T (2019) Horizontal pyramid matching for person re-identification. In: Proceedings of the AAAI conference on artificial intelligence, vol 33, pp 8295–8302

  29. Luo H, Gu Y, Liao X, Lai S, Jiang W (2019) Bag of tricks and a strong baseline for deep person re-identification. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition (CVPR) workshops

  30. Sun Y, Zheng L, Yang Y, Tian Q, Wang S (2018) Beyond part models: person retrieval with refined part pooling (and a strong convolutional baseline). In: Proceedings of the European conference on computer vision (ECCV), pp 480–496

  31. Wang Q, Wu B, Zhu P, Li P, Zuo W, Hu Q (2020) Eca-net: efficient channel attention for deep convolutional neural networks. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp 11534–11542

  32. Takemura N, Makihara Y, Muramatsu D, Echigo T, Yagi Y (2018) Multi-view large population gait dataset and its performance evaluation for cross-view gait recognition. IPSJ transactions on Computer Vision and Applications. 10:1–14

    Article  Google Scholar 

  33. Liao R, Li Z, Bhattacharyya SS, York G (2022) Posemapgait: a model-based gait recognition method with pose estimation maps and graph convolutional networks. Neurocomputing 501:514–528

  34. Chen J, Wang Z, Yi P, Zeng K, He Z, Zou Q (2022) Gait pyramid attention network: toward silhouette semantic relation learning for gait recognition. IEEE Transactions on biometrics, behavior, and identity science. 4(4):582–595

    Article  Google Scholar 

  35. Dou H, Zhang P, Zhao Y, Dong L, Qin Z, Li X (2024) Gaitmpl: gait recognition with memory-augmented progressive learnin. IEEE Trans Image Process 33:1464–1475

    Article  Google Scholar 

  36. Chen J, Wang Z, Zheng C, Zeng K, Zou Q, Cui L (2023) Gaitamr: cross-view gait recognition via aggregated multi-feature representation. Inf Sci 636:118920

    Article  Google Scholar 

  37. Wei T, Liu M, Zhao H, Li H (2024) Gmsn: an efficient multi-scale feature extraction network for gait recognition. Expert Syst Appl 252:124250

    Article  Google Scholar 

  38. Li N, Zhao X (2023) A strong and robust skeleton-based gait recognition method with gait periodicity priors. IEEE Trans Multimedia 25:3046–3058

    Article  Google Scholar 

  39. Hou S, Cao C, Liu X, Huang Y (2020) Gait lateral network: learning discriminative and compact representations for gait recognition. In: Vedaldi A, Bischof H, Brox T, Frahm J-M (eds) Computer Vision - ECCV 2020. Springer, Cham, pp 382–398

    Chapter  Google Scholar 

  40. Woo S, Park J, Lee J-Y, Kweon IS (2018) Cbam: convolutional block attention module. In: Proceedings of the European Conference on Computer Vision (ECCV), pp 3–19

  41. Hu J, Shen L, Sun G (2018) Squeeze-and-excitation networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 7132–7141

Download references

Acknowledgements

The research work was supported by the Visual Perception and Data Cognitive Artificial Intelligence Laboratory of Xi ’an University of Posts and Telecommunications in Xi ’an, Shaanxi Province

Author information

Authors and Affiliations

Authors

Contributions

Xiao Ying Pan (First Author):Supervision,Conceptulization,WritingReview & Editing,Methodology,Formal Analysis,Project Administration; He Wei Xie:Conceptulization,Methodology, Data Curation, WritingReview & Editing,Writing - Original Draft, Visualization, Validation, software, Formal Analysis; Ni Juan Zhang:Data Curation; Shou Kun Li:Data Curation;

Corresponding author

Correspondence to Hewei Xie.

Ethics declarations

Ethical and Informed Consent for Data Used

I guarantee that the data used is from the official source and authorized by the official. Otherwise, I will bear the responsibility.

Competing Interests

No potential conflict of interest was reported by the authors.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Pan, X., Xie, H., Zhang, N. et al. GaitLRDF: gait recognition via local relevant feature representation and discriminative feature learning. Appl Intell 54, 12476–12491 (2024). https://doi.org/10.1007/s10489-024-05837-9

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10489-024-05837-9

Keywords

Navigation