Multi-view Gait Recognition Method Based on RBF Network

  • Yaru Qiu
  • Yonghong SongEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10996)


Gait is an important biometrics in human identification, but the view variation problem seriously affects the accuracy of gait recognition. Existing methods for multi-view gait-based identification mainly focus on transforming the features of one view to another view, which might be unsuitable for the real applications. In this paper, we propose a multi-view gait recognition method based on RBF network that employs a unique view-invariant model. First, extracts the gait features by calculating the gait individual image (GII), which could better capture the discriminative information for cross view gait recognition. Then, constructs a joint model, use the DLDA algorithm to project the model and get a projection matrix. Finally, the projected eigenvectors are classified by RBF network. Experiments have been conducted in the CASIA-B database to prove the validity of the proposed method. Experiment results shows that our method performs better than the state-of-the-art multi-view methods.


Gait recognition RBF network Invariant feature 


  1. 1.
    Tafazzoli, F., Safabakhsh, R.: Model-based human gait recognition using leg and arm movements. Eng. Appl. AI 23(8), 1237–1246 (2010)Google Scholar
  2. 2.
    Yam, C.Y., Nixon, M.S., Carter, J.N.: Automated person recognition by walking and running via model-based approaches. Pattern Recogn. 37(5), 1057–1072 (2004)CrossRefGoogle Scholar
  3. 3.
    Han, J., Bhanu, B.: Individual recognition using gait energy image. IEEE Trans. Pattern Anal. Mach. Intell. 28(2), 316–322 (2006)CrossRefGoogle Scholar
  4. 4.
    Zhang, E., Zhao, Y., Xiong, W.: Active energy image plus 2DLPP for gait recognition. Signal Process. 90(7), 2295–2302 (2010)CrossRefGoogle Scholar
  5. 5.
    Wang, C., Zhang, J., Wang, L.: Human identification using temporal information preserving gait template. IEEE Trans. Pattern Anal. Mach. Intell. 34(11), 2164–2176 (2012)CrossRefGoogle Scholar
  6. 6.
    Yu, S., Tan, D., Tan, T.: A framework for evaluating the effect of view angle, clothing and carrying condition on gait recognition. In: ICPR, vol. 4, pp. 441–444 (2006)Google Scholar
  7. 7.
    Bodor, R., Drenner, A., Fehr, D.: View-independent human motion classification using image-based reconstruction. Image Vis. Comput. 27(8), 1194–1206 (2009)CrossRefGoogle Scholar
  8. 8.
    Zhang, Z., Troje, N.F.: View-independent person identification from human gait. Neurocomputing 69(1–3), 250–256 (2005)CrossRefGoogle Scholar
  9. 9.
    Zhao, G., Liu, G., Li, H.: 3D gait recognition using multiple cameras. In: FG, pp. 529–534 (2006)Google Scholar
  10. 10.
    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
  11. 11.
    Kusakunniran, W., Wu, Q., Li, H.: Support vector regression for multi-view gait recognition based on local motion feature selection. In: CVPR, pp. 974–981 (2010)Google Scholar
  12. 12.
    Kusakunniran, W., Wu, Q., Zhang, J.: Gait recognition under various viewing angles based on correlated motion regression. IEEE Trans. Circ. Syst. Video Technol. 22(6), 966–980 (2012)CrossRefGoogle Scholar
  13. 13.
    Bashir, K., Xiang, T., Gong, S.: Cross view gait recognition using correlation strength. In: BMVC, pp. 1–11 (2010)Google Scholar
  14. 14.
    Zhang, Z., Chen, J., Wu, Q.: GII representation-based cross-view gait recognition by discriminative projection with list-wise constraints. IEEE Trans. Cybern. 88(99), 1–13 (2017)Google Scholar
  15. 15.
    Portillo-Portillo, J., Leyva, R., Sanchez, V.: Cross view gait recognition using joint-direct linear discriminant analysis. Sensors 17(1), 6 (2017)Google Scholar
  16. 16.
    Tao, D., Li, X., Wu, X.: General tensor discriminant analysis and gabor features for gait recognition. IEEE Trans. Pattern Anal. Mach. Intell. 29(10), 1700–1715 (2007)CrossRefGoogle Scholar
  17. 17.
    Alldrin, N., Smith, A., Turnbull, D.: Classifying facial expression with radial basis function networks, using gradient descent and K-means (2003)Google Scholar
  18. 18.
    Yu, S., Wang, Q., Shen, L.: View invariant gait recognition using only one uniform model. In: ICPR, pp. 889–894 (2016)Google Scholar

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© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.School of Software EngineeringXi’an Jiaotong UniversityXi’anChina
  2. 2.School of Electronic and Information EngineeringXi’an Jiaotong UniversityXi’anChina

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