Skip to main content
SpringerLink
Log in

Methods of Gait Recognition in Video

  • Published:
Programming and Computer Software Aims and scope Submit manuscript

Abstract

Human gait is an important biometric index that allows to identify a person at a great distance without direct contact. Due to these qualities, which other popular identifiers such as fingerprints or iris do not have, the recognition of a person by the manner of walking has become very common in various areas where video surveillance systems can be used. With the development of computer vision techniques, a variety of approaches for human identification by movements in a video appear. These approaches are based both on natural biometric characteristics (human skeleton, silhouette, and their change during walking) and abstract features trained automatically which do not have physical justification. Modern methods combine classical algorithms of video and image analysis and new approaches that show excellent results in related tasks of computer vision, such as human identification by face and appearance or action and gesture recognition. However, due to the large number of conditions that can affect the walking manner of a person itself and its representation in video, the problem of identifying a person by gait still does not have a sufficiently accurate solution. Many methods are overfitted by the conditions presented in the databases on which they are trained, which limits their applicability in real life. In this paper, we provide a survey of state-of-the-art methods of gait recognition, their analysis and comparison on several popular video collections and for different formulations of the problem of recognition. We additionally reveal the problems that prevent the final solution of gait identification challenge.

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

Access this article

Log in via an institution

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

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1.
Fig. 2.
Fig. 3.

Similar content being viewed by others

REFERENCES

  1. Arseev, S., Konushin, A., and Liutov, V., Human recognition by appearance and gait, Programming and Computer Software, 2018, pp. 258–265.

  2. Bashir, K., Xiang, T., and S, G., Gait recognition using gait entropy image, Proceedings of 3rd International Conference on Crime Detection and Prevention, 2009, pp. 1–6.

  3. Belhumeur, P.N., Hespanha, J.a.P., and Kriegman, D.J., Eigenfaces vs. fisherfaces: Recognition using class specific linear projection, IEEE Trans. Pattern Anal. Mach. Intell., 1997, vol. 19, no. 7, pp. 711–720.

    Article  Google Scholar 

  4. Chen, C., Liang, J., Zhao, H. Hu, H., and Tian, J., Frame difference energy image for gait recognition with incomplete silhouettes, Pattern Recognit. Lett., 2009, vol. 30, no. 11, pp 977–984.

    Article  Google Scholar 

  5. Castro, F.M., Marín-Jiménez, M.J., Guil, N., and Pérez de la Blanca, N., Automatic learning of gait signatures for people identification, Advances in Computational Intelligence, 2017. pp. 257–270.

    Book  Google Scholar 

  6. Castro, F.M., Marín-Jiménez, M., and Medina Carnicer, R., Pyramidal Fisher Motion for multiview gait recognition, 22nd International Conference on Pattern Recognition, 2014, pp. 692–1697.

  7. Dalal, N. and Triggs, B., Histograms of oriented gradients for human detection, Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), 2005, vol. 01, pp. 886–893.

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

    Article  Google Scholar 

  9. Feng, Y., Li, Y., and Luo, J., Learning effective gait features using LSTM, International Conference on Pattern Recognition, 2016, pp. 325–330.

  10. He, Y., Zhang, J., Shan, H., and Wang, L., Multi-task gans for view-specific feature learning in gait recognition, IEEE TIFS, 2019, vol. 14, no. 1, pp. 102–113.

    Google Scholar 

  11. Han, J. and Bhanu, B., Individual recognition using gait energy image, IEEE Trans. Pattern Anal. Mach. Intell., 2006, vol. 28, pp. 316–322.

    Article  Google Scholar 

  12. Hofmann, M., Geiger, J., Bachmann, S., Schuller, B., and Rigoll, G., The TUM Gait from Audio, Image and Depth (GAID) database: Multimodal recognition of subjects and traits, J. Visual Com. Image Repres., 2014, vol. 25, no. 1, pp.195–206.

    Article  Google Scholar 

  13. Iwama, H., Okumura, M., Makihara, Y., and Yagi, Y., The OU-ISIR gait database comprising the large population dataset and performance evaluation of gait recognition, IEEE Trans. on Information Forensics and Security, 2012, 7, Issue 5, pp. 1511–1521.

    Article  Google Scholar 

  14. Laptev, I., Marszalek, M., Schmid, C., and Rozenfeld, B., Learning realistic human actions from movies, IEEE Conference on Computer Vision & Pattern Recognition (CVPR 2008), 2008, pp. 1–8.

  15. Li, C., Sun, S., Chen, X., and Min, X., Cross-view gait recognition using joint Bayesian, Proc. SPIE 10420, Ninth International Conference on Digital Image Processing (ICDIP 2017), 2017.

  16. Liu, Y., Zhang, J., Wang, C., and Wang, L., Multiple HOG templates for gait recognition, Proceedings of the 21st International Conference on Pattern Recognition (ICPR2012), 2012. pp. 2930–2933.

  17. Makihara, Y., Sagawa, R., Mukaigawa, Y., Echigo, T., and Yagi, Y., Gait recognition using a view transformation model in the frequency domain, Computer Vision – ECCV 2006, 2006, pp. 151–163.

  18. Makihara, Y., Suzuki, A., Muramatsu, D., Li, X., and Yagi, Y., Joint intensity and spatial metric learning for robust gait recognition, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017, pp. 6786–6796.

  19. Mansur, A., Makihara, Y., Muramatsu, D., and Yagi, Y., Cross-view gait recognition using view-dependent discriminative analysis, 2014 IEEE/IAPR International Joint Conference on Biometrics (IJCB 2014), 2014.

  20. Marín-Jiménez, M., Castro, F., Guil, N., de la Torre, F., and Medina Carnicer, R., Deep multi-task learning for gait-based biometrics, IEEE International Conference on Image Processing (ICIP), 2017.

  21. Muramatsu, D., Makihara, Y., and Yagi, Y., View transformation model incorporating quality measures for cross-view gait recognition, IEEE Transactions on Cybernetics, 2015, vol. 46.

  22. Muramatsu, D., Makihara, Y., and Yagi, Y., Cross-view gait recognition by fusion of multiple transformation consistency measures, IET Biometrics, 2015, vol. 4.

    Book  Google Scholar 

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

  24. Simonyan, K. and Zisserman, A., Two-stream convolutional networks for action recognition in videos, Proceedings of the 27th International Conference on Neural Information Processing Systems (NIPS’14), 2014, vol. 1, pp. 568–576.

  25. Sokolova, A. and Konushin, A., Gait recognition based on convolutional neural networks, International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2017, vol. XLII-2/W4, pp. 207–212.

    Article  Google Scholar 

  26. Sokolova, A. and Konushin, A., Pose-based deep gait recognition, IET Biometrics, 2018.

    Google Scholar 

  27. Takemura, N., Makihara, Y., and Muramatsu, D., On input/output architectures for convolutional neural network-based cross-view gait recognition, IEEE Trans. Circuits Syst. Video Technol., 2017, vol. 1, p. 1.

    Article  Google Scholar 

  28. Thapar, D., Nigam, A., Aggarwal, D., and Agarwal, P., VGR-net: A view invariant gait recognition network, IEEE 4th International Conference on Identity, Security, and Behavior Analysis (ISBA), 2018, pp. 1–8.

  29. Tong, S., Fu, Y., Ling, H., and Zhang, E., Gait identification by joint spatial-temporal feature, Biometric Recognition, 2017, pp. 457–465.

    Book  Google Scholar 

  30. Whytock, T., Belyaev, A., and Robertson, N.M., Dynamic distance-based shape features for gait recognition, J. Math. Imaging and Vision, 2014, vol. 50, no. 3, pp. 314–326.

    Article  MATH  Google Scholar 

  31. Wu, Z., Huang, Y., Wang, L., Wang, X., and Tan, T., A comprehensive study on cross-view gait based human identification with deep cnns, IEEE Trans. on Pattern Analysis and Machine Intelligence, 2016, p. 39.

  32. Yang, Y., Tu, D., and Li, G., Gait recognition using flow histogram energy image, 22nd International Conference on Pattern Recognition, 2014, pp. 444–449.

  33. Yu, S., Chen, H., Reyes, E.B.G., and Poh, N., GaitGAN: Invariant Gait Feature Extraction Using Generative Adversarial Network, 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 2017, pp. 532–539.

  34. Yu, S., Chen, H., Wang, Q., Shen, L., and Huang, Y., Invariant feature extraction for gait recognition using only one uniform model, Neurocomputing, 2017, vol. 239, pp. 81–93.

    Article  Google Scholar 

  35. Yu, S., Tan, D., and Tan, T., A Framework for evaluating the Effect of view angle, clothing and carrying condition on gait recognition, Proc. of the 18’th International Conference on Pattern Recognition (ICPR), 2006, vol. 4, pp. 441–444.

  36. Zhang, C., Liu, W., Ma, H., and Fu, H., Siamese neural network based gait recognition for human identification, IEEE International Conference on Acoustics, Speech and Signal Processing, 2016, pp. 2832–2836.

  37. Zhang, X., Sun, S., Li, C., Zhao, X., and Hu, Y., Deepgait: A learning deep convolutional representation for gait recognition, Biometric Recognition, 2017, pp. 447–456.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. National Research University Higher School of Economics, 101000, Moscow, Russia

    A. Sokolova & A. Konushin

  2. Lomonosov Moscow State University, 119991, Moscow, Russia

    A. Konushin

Authors
  1. A. Sokolova
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. A. Konushin
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to A. Sokolova or A. Konushin.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Sokolova, A., Konushin, A. Methods of Gait Recognition in Video. Program Comput Soft 45, 213–220 (2019). https://doi.org/10.1134/S0361768819040091

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1134/S0361768819040091

Search

Navigation