Skip to main content

Advertisement

Springer Nature Link
Log in

Frame–based classification for cross-speed gait recognition

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

The use of human gait as the means of biometric identification has gained a lot of attention in the past few years, mostly due to its enormous potential. Such biometrics can be captured at public places from a distance without subjects collaboration, awareness and even consent. However, there are still numerous challenges caused by influence of covariate factors like changes of walking speed, view, clothing, footwear etc., that have negative impact on recognition performance. In this paper we tackle walking speed changes with a skeleton model-based gait recognition system focusing on improving algorithm robustness and improving the performance at higher walking speed changes. We achieve these by proposing frame based classification method, which overcomes the main shortcoming of distance based classification methods, which are very sensitive to gait cycle starting point detection. The proposed technique is starting point invariant with respect to gait cycle starts and as such ensures independence of classification from gait cycle start positions. Additionally, we propose wavelet transform based signal approximation, which enables the analysis of feature signals on different frequency space resolutions and diminishes the need for using feature transformation that require training. With the evaluation on OU-ISIR gait dataset we demonstrate state of the art performance of proposed methods.

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

Access this article

Log in via an institution

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.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13

Similar content being viewed by others

References

  1. Alotaibi M, Mahmood A (2015) Improved gait recognition based on specialized deep convolutional neural networks. In: Applied Imagery Pattern Recognition Workshop. IEEE, pp 1–7. https://doi.org/10.1109/AIPR.2015.7444550

  2. Ariyanto G, Nixon MS (2012) Marionette mass-spring model for 3d gait biometrics. In: Biometrics. IEEE, New Delhi, pp 354–359. https://doi.org/10.1109/ICB.2012.6199832

  3. Aung M, Thies S, Kenney L, Howard D, Selles R, Findlow A, Goulermas J Automated detection of instantaneous gait events using time frequency analysis and manifold embedding. Trans Neural Syst Rehabil Eng 21(6). https://doi.org/10.1109/TNSRE.2013.2239313

    Article  Google Scholar 

  4. Castro F, Marin-Jimenez M, Guil N, de la Blanca NP Automatic learning of gait signatures for people identification. arXiv:1603.01006

  5. Iwashita Y, Sakano H, Kurazume R (2015) Gait recognition robust to speed transition using mutual subspace method. In: Image Analysis and Processing, Vol. 9279 of Lecture Notes in Computer Science. Springer, Berlin, pp 141–149. https://doi.org/10.1007/978-3-319-23231-7_13

    Chapter  Google Scholar 

  6. Jeevan M, Hanmandlu M, Panigrahi BK (2016) Information set based gait authentication system. Neurocomputing 207(C):1–14

    Google Scholar 

  7. Johansson G (1976) Visual motion perception. Sci Am 232(6):76–88. https://doi.org/10.1038/scientificamerican0675-76

    Article  Google Scholar 

  8. Kobayashi T, Otsu N (2008) Three-way auto-correlation approach to motion recognition. Pattern Recogn Lett 30(3):212–221

    Article  Google Scholar 

  9. Kovač J, Peer P (2013) Transformation based walking speed normalization for gait recognition. KSII Trans Internet Inf Syst 7(11):2690–2701

    Article  Google Scholar 

  10. Kovač J, Peer P (2014) Human skeleton model based dynamic features for walking speed invariant gait recognition. Math Probl Eng 2014:1–15. https://doi.org/10.1155/2014/484320

    Article  Google Scholar 

  11. Kusakunniran W (2014) Attribute-based learning for gait recognition using spatio-temporal interest points. Image Vis Comput 32 (12):1117–1126. https://doi.org/10.1016/j.imavis.2014.10.004

    Article  Google Scholar 

  12. Kusakunniran W, Wu Q, Zhang J, Li H (2011) Speed-invariant gait recognition based on procrustes shape analysis using higher-order shape configuration. In: Image Processing. IEEE, pp 545–548. https://doi.org/10.1109/ICIP.2011.6116403

  13. Kusakunniran W, Wu Q, Zhang J, Li H (2012) Differential composition model. Syst Man Cybern 42(6):1654–1668

    Article  Google Scholar 

  14. Lee S, Collins R (2007) Shape variation-based frieze pattern for robust gait recognition. In: Computer Vision and Pattern Recognition. IEEE, pp 1–8. https://doi.org/10.1109/CVPR.2007.383138

  15. Li W, Kuo CCJ, Peng J Gait recognition via gei subspace projections and collaborative representation classification, Neurocomputing

  16. Liu Z, Sarkar S (2004) Simplest representation yet for gait recognition: averaged silhouette. In: International Conference on Pattern Recognition, vol 4. IEEE, pp 211–214. https://doi.org/10.1109/ICPR.2004.1333741

  17. Liu Z, Sarkar S (2006) Improved gait recognition by gait dynamics normalization. IEEE Trans Pattern Anal Mach Intell 28(6):863–76

    Article  Google Scholar 

  18. Lu H, Plataniotis K, Venetsanopoulos A (2008) A full-body layered deformable model for automatic model-based gait recognition. J Advan Signal Process 2008:1–14. https://doi.org/10.1155/2008/261317

    Article  MATH  Google Scholar 

  19. Makihara Y, Mannami H, Tsuji A, Hossain MA, Sugiura K, Mori A, Yagi Y (2012) The OU-ISIR gait database comprising the treadmill dataset. Trans Comput Vision Appl 4:53–62

    Google Scholar 

  20. Peternel M, Leonardis A (2004) Visual learning and recognition of a probabilistic spatio-temporal model of cyclic human locomotion. In: International Conference on Pattern Recognition, vol 4. IEEE, pp 146–149. https://doi.org/10.1109/ICPR.2004.1333725

  21. Rahati S, Moravejian R, Kazemi FM (2008) Gait recognition using wavelet transform. In: International Conference on Information Technology: New Generations. IEEE, pp 932–936. https://doi.org/10.1109/ITNG.2008.124

  22. Sarkar S, Phillips PJ, Liu Z, Vega IR, Grother P, Bowyer KW (2005) The humanID gait challenge problem: data sets, performance, and analysis. IEEE Trans Pattern Anal Mach Intell 27(2):162–177. https://doi.org/10.1109/TPAMI.2005.39

    Article  Google Scholar 

  23. Tan D, Huang K, Yu S, Tan T (2006) Efficient night gait recognition based on template matching. In: International Conference on Pattern Recognition. IEEE, pp 1000–1003. https://doi.org/10.1109/ICPR.2006.478

  24. Tanawongsuwan R, Bobick A (2004) Modelling the effects of walking speed on appearance-based gait recognition. In: Computer Vision and Pattern Recognition, vol 2. IEEE, pp 783–790. https://doi.org/10.1109/CVPR.2004.1315244

  25. Tsuji A, Makihara Y, Yagi Y (2010) Silhouette transformation based on walking speed for gait identification. In: International Conference on Computer Vision and Pattern Recognition, pp 717– 722

  26. Valcik J, Sedmidubsky J, Zezula P Assessing similarity models for human-motion retrieval applications, Computer Animation and Virtual Worlds. https://doi.org/10.1002/cav.1674

    Article  Google Scholar 

  27. Veeraraghavan A, Srivastava A, Roy-Chowdhury AK, Chellappa R (2009) Rate-invariant recognition of humans and their activities. Trans Image Process 18 (6):1326–39

    Article  MathSciNet  Google Scholar 

  28. Xue Z, Ming D, Song W, Wan B, Jin S (2010) Infrared gait recognition based on wavelet transform and support vector machine. Pattern Recogn 43(8):2904–2910. https://doi.org/10.1016/j.patcog.2010.03.011

    Article  Google Scholar 

  29. Yoo J, Nixon MS (2011) Automated markerless analysis of human gait motion for recognition and classification. ETRI J Inf Telecommun Electron 33(2):259–266

    Google Scholar 

  30. Yu T, Zou JH (2012) Automatic human gait imitation and recognition in 3d from monocular video with an uncalibrated camera. Math Probl Eng 2012:1–35

    MathSciNet  MATH  Google Scholar 

  31. Zhang C, Liu W, Ma H, Fu H (2016) Siamese neural network based gait recognition for human identification. In: International Conference on Acoustics, Speech and Signal Processing. IEEE, pp 2832–2836. https://doi.org/10.1109/ICASSP.2016.7472194

Download references

Acknowledgments

Research was partly financed by the European Union, European Social Fund. This research was supported in parts also by the ARRS (Slovenian Research Agency) Research Program P2-0214 (A) Computer Vision and the ARRS Research Program P2-0250 (B) Metrology and Biometric Systems.

Author information

Authors and Affiliations

  1. Faculty of Computer and Information Science, University of Ljubljana, Večna pot 113, 1000, Ljubljana, Slovenia

    Jure Kovač & Peter Peer

  2. Faculty of Electrical Engineering, University of Ljubljana, Tržaška cesta 25, 1000, Ljubljana, Slovenia

    Vitomir Štruc

Authors
  1. Jure Kovač
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Vitomir Štruc
    View author publications

    You can also search for this author inPubMed Google Scholar

  3. Peter Peer
    View author publications

    You can also search for this author inPubMed Google Scholar

Corresponding author

Correspondence to Peter Peer.

Ethics declarations

Conflict of interests

The authors declare that they have no conflict of interest.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Kovač, J., Štruc, V. & Peer, P. Frame–based classification for cross-speed gait recognition. Multimed Tools Appl 78, 5621–5643 (2019). https://doi.org/10.1007/s11042-017-5469-0

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-017-5469-0

Keywords