A Regression Approach for Robust Gait Periodicity Detection with Deep Convolutional Networks

  • Kejun WangEmail author
  • Liangliang Liu
  • Xinnan Ding
  • Yibo Xu
  • Haolin Wang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11257)


This paper presents a regression approach to gait periodicity detection via fitting gait sequence to a sine function by deep convolutional neural networks. The key idea is to model the gait fluctuation as a sinusoidal function because of similar periodic regularity. Each frame of the gait video corresponds to a function value that can represent its periodic features. Convolutional network serves to learn and locate a frame in a gait cycle. To the best of our knowledge, it is the first work based on deep neural networks for gait period detection in the literature. An extensive empirical evaluation is provided on the CASIA-B dataset in terms of different views and network architectures with comparison to the existing works. The results show the good accuracy and robustness of the proposed method for gait periodicity detection.


Gait period detection Deep convolutional neural networks Gait recognition Biometrics technology 


  1. 1.
    Phillips, P.J.: Human identification technical challenges. In: 2002 International Conference on Image Processing, pp. 49–52. IEEE, Rochester (2002)Google Scholar
  2. 2.
    Makihara, Y., Sagawa, R., Mukaigawa, Y., Echigo, T., Yagi, Y.: Gait recognition using a view transformation model in the frequency domain. In: Leonardis, A., Bischof, H., Pinz, A. (eds.) ECCV 2006. LNCS, vol. 3953, pp. 151–163. Springer, Heidelberg (2006). Scholar
  3. 3.
    Li, C., Min, X., Sun, S., Lin, W., Tang, Z.: Deepgait: a learning deep convolutional representation for view-invariant gait recognition using joint Bayesian. Appl. Sci. 7(3), 210 (2017)CrossRefGoogle Scholar
  4. 4.
    Collins, R.T., Gross, R., Shi. J.: Silhouette-based human identification from body shape and gait. In: 5th IEEE International Conference on Automatic Face and Gesture Recognition, pp. 366–372. IEEE, Washington (2002)Google Scholar
  5. 5.
    Wang, L., Tan, T., Ning, H., Hu, W.: Silhouette analysis-based gait recognition for human identification. IEEE Trans. Pattern Anal. Mach. Intell. 25(12), 1505–1518 (2003)CrossRefGoogle Scholar
  6. 6.
    LeCun, Y., Boser, B., Denker, J., Henderson, D.: Handwritten digit recognition with a back-propagation network. In: Advances in Neural Information Processing Systems, pp. 396–404. ACM, San Francisco (1990)Google Scholar
  7. 7.
    Krizhevsky, A., Sutskever, I., Hinton, G.: ImageNet classification with deep convolutional neural networks. In: International Conference on Neural Information Processing Systems, pp. 1106–1114. ACM, New York (2012)Google Scholar
  8. 8.
    Lee, C.P., Tan, A.W.C., Tan, S.C.: Gait recognition with transient binary patterns. Vis. Commun. Image Represent. 33(C), 69–77 (2015)CrossRefGoogle Scholar
  9. 9.
    Wang, C., Zhang, J., Wang, L., Pu, J., Yuan, X.: Human identification using temporal information preserving gait template. IEEE Trans. Pattern Anal. Mach. Intell. 34(11), 2164–2176 (2012)CrossRefGoogle Scholar
  10. 10.
    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)CrossRefGoogle Scholar
  11. 11.
    Ben, X., Meng, W., Yan, R.: Dual-ellipse fitting approach for robust gait periodicity detection. Neurocomputing. 79(3), 173–178 (2012)CrossRefGoogle Scholar
  12. 12.
    Lecun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proc. IEEE 86(11), 2278–2324 (1998)CrossRefGoogle Scholar
  13. 13.
    Simonyan, K., Zisserman, A..: Very deep convolutional networks for large-scale image recognition. CoRR (2014).
  14. 14.
    Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., et al.: Going deeper with convolutions. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–9. IEEE, Boston (2015)Google Scholar
  15. 15.
    Szegedy, C., Ioffe, S., Vanhoucke, V., Alemi, A.: Inception-v4, Inception-ResNet and the impact of residual connections on learning. CoRR (2016).
  16. 16.
    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, pp. 441–444. IEEE, Hong Kong (2006)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Kejun Wang
    • 1
    Email author
  • Liangliang Liu
    • 1
  • Xinnan Ding
    • 1
  • Yibo Xu
    • 1
  • Haolin Wang
    • 1
  1. 1.College of AutomationHarbin Engineering UniversityHarbinChina

Personalised recommendations