Skip to main content
SpringerLink
Log in

Gait Recognition: Databases, Representations, and Applications

  • Living reference work entry
  • First Online:
Computer Vision

Synonyms

Automatic gait recognition; Gait analysis; Gait biometrics

Related Concepts

Definition

The way a person walks (or runs) combined with their posture is known as gait. Recognizing individuals by their particular gait using automated vision-based algorithms is known as gait recognition.

Background

Gait is increasingly used when a person cannot be identified by more conventional means: in the recent high-profile Hatton Gardens robbery in the UK, “Basil” covered his face and other identifying marks, Fig. 1, only to be convicted in part by identification by his gait. Gait has some important advantages over other biometrics. Gait can be observed at a distance when other biometrics are obscured or the resolution is insufficient. It does not require subject cooperation and can be acquired in a noninvasive manner. It is easy to observe and hard to disguise as...

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

Access this chapter

Log in via an institution

Institutional subscriptions

Notes

  1. 1.

    The actual permission will be obtained if this version is accepted

References

  1. Bouchrika I, Goffredo M, Carter J, Nixon M (2011) On using gait in forensic biometrics. J Forensic Sci 56(4):882–889

    Article  Google Scholar 

  2. 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 

  3. Cunado D, Nixon MS, Carter JN (2003) Automatic extraction and description of human gait models for recognition purposes. Comput Vis Image Underst 90(1):1–41

    Article  Google Scholar 

  4. Cutting JE, Kozlowski LT (1977) Recognizing friends by their walk: Gait perception without familiarity cues. Bull Psychon Soc 9(5):353–356

    Article  Google Scholar 

  5. Guoying Z, Guoyi L, Hua L, Pietikainen M (2006) 3D gait recognition using multiple cameras. In: Proceedings of AFGR, pp 529–534

    Google Scholar 

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

    Article  Google Scholar 

  7. Iwama H, Muramatsu D, Makihara Y, Yagi Y (2013) Gait verification system for criminal investigation. IPSJ Trans Comput Vis Appl 5:163–175

    Article  Google Scholar 

  8. Larsen PK, Simonsen EB, Lynnerup N (2008) Gait analysis in forensic medicine. J Forensic Sci 53(5):1149–1153

    Article  Google Scholar 

  9. Makihara Y, Matovski DS, Nixon MS, Carter JN, Yagi Y (2015) Gait recognition: databases, representations, and applications. Wiley, pp. 1–15

    Google Scholar 

  10. Makihara Y, Sagawa R, Mukaigawa Y, Echigo T, Yagi Y (2006) Gait recognition using a view transformation model in the frequency domain. Proc ECCV 3:151–163

    Google Scholar 

  11. Murray M, Drought A, Kory R (1964) Walking patterns of normal men. J Bone Joint Surg 46(2):335

    Article  Google Scholar 

  12. Sarkar S, Phillips PJ, Zongyi L, Isidro Robledo V, Grother PW, Bowyer K (2005) The HumanID gait challenge problem: data sets, performance, and analysis. IEEE Trans Pattern Anal Mach Intell 27(2):162–177

    Article  Google Scholar 

  13. Seely R, Samangooei S, Lee M, Carter J, Nixon M (2008) The university of Southampton multi-biometric tunnel and introducing a novel 3D gait dataset. In: Proceedings of IEEE BTAS, pp 1–6

    Google Scholar 

  14. Shiraga K, Makihara Y, Muramatsu D, Echigo, T, Yagi Y (2016) GEINet: view-invariant gait recognition using a convolutional neural network. In: Proceedings of 8th IAPR ICB

    Google Scholar 

  15. Stevenage S, Nixon M, Vince K (1999) Visual analysis of gait as a cue to identity. Appl Cogn Psychol 13:513–526

    Article  Google Scholar 

  16. Takemura N, Makihara Y, Muramatsu D, Echigo T, Yagi Y (2017) On input/output architectures for convolutional neural network-based cross-view gait recognition. IEEE Trans Circuits Syst Video Technol, https://doi.org/10.1109/TCSVT.2017.2760835

    Google Scholar 

  17. Winter DA (2009) Biomechanics and motor control of human movement. Wiley, Ottawa

    Book  Google Scholar 

  18. 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 

  19. Yam C, Nixon MS, Carter JN (2004) Automated person recognition by walking and running via model-based approaches. Pattern Recogn 37(5):1057–1072

    Article  Google Scholar 

  20. Yu S, Liao R, An W, Chen H, Garcia E, Huang Y, Norman P (2019) GaitGANv2: invariant gait feature extraction using generative adversarial networks. Pattern Recogn 87:179–189

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. The Institute of Scientific and Industrial Research, Osaka University, Osaka, Japan

    Yasushi Makihara & Yasushi Yagi

  2. Department of Electronics and Computer Science, University of Southampton, Southampton, UK

    Mark S. Nixon

Authors
  1. Yasushi Makihara
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Mark S. Nixon
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Yasushi Yagi
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Yasushi Makihara .

Section Editor information

  1. No affiliation provided

    Larry Davis

Rights and permissions

Reprints and permissions

Copyright information

© 2021 Springer Nature Switzerland AG

About this entry

Check for updates. Verify currency and authenticity via CrossMark

Cite this entry

Makihara, Y., Nixon, M.S., Yagi, Y. (2021). Gait Recognition: Databases, Representations, and Applications. In: Computer Vision. Springer, Cham. https://doi.org/10.1007/978-3-030-03243-2_883-1

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-03243-2_883-1

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-03243-2

  • Online ISBN: 978-3-030-03243-2

  • eBook Packages: Springer Reference Computer SciencesReference Module Computer Science and Engineering

Publish with us

Policies and ethics

Search

Navigation