Advertisement

Signal, Image and Video Processing

, Volume 13, Issue 1, pp 43–51 | Cite as

On the selection of spatiotemporal filtering with classifier ensemble method for effective gait recognition

  • Mohammad H. GhaeminiaEmail author
  • Shahriar B. Shokouhi
Original Paper
  • 34 Downloads

Abstract

In this paper, we improve the performance of gait recognition by modeling human’s motion with spatiotemporal gait features. Since existing methods often use average of silhouettes, i.e., gait energy image to model the gait, temporal information of walking may not be preserved under covariate factors. To handle such features in different conditions, we study the gait model from energy viewpoint. In the proposed method, energy of a gait, i.e., spatiotemporal feature, is derived from a newly designed filtering approach and the energies within a period will be aggregated into a single template that is called gait spatiotemporal image. The required features are truly extracted from spatial and temporal impulse responses that are redesigned and optimized for the gait. Moreover, to recognize the gait under covariate factors, a hybrid decision-level classifier based on random subspace method has been utilized for the given templates. Experimental results on well-known public datasets demonstrate the efficacy of our model. The proposed gait recognition system achieves the recognition rate of 72.25% for Rank1 and 85.64% for Rank5 on the USF dataset that is improved by at least 2% in Rank1 and 0.3% in Rank5 with respect to recent template-based methods.

Keywords

Gait biometrics Motion-based filtering Spatiotemporal representation Ensemble classification 

References

  1. 1.
    Xu, D., Huang, Y., Zeng, Z., Xu, X.: Human gait recognition using patch distribution feature and locality-constrained group sparse representation. IEEE Trans. Image Process. 21(1), 316–326 (2012)MathSciNetCrossRefzbMATHGoogle Scholar
  2. 2.
    Han, J., Bhanu, B.: Individual recognition using gait energy image. IEEE Trans. Pattern Anal. Mach. Intell. 28(2), 316–322 (2006)CrossRefGoogle Scholar
  3. 3.
    Guan, Y., Li, C.-T., Roli, F.: On reducing the effect of covariate factors in gait recognition: a classifier ensemble method. IEEE Trans. Pattern Anal. Mach. Intell. 37(7), 1521–1528 (2015)CrossRefGoogle Scholar
  4. 4.
    Atta, R., Shaheen, S., Ghanbari, M.: Human identification based on temporal lifting using 5/3 wavelet filters and radon transform. J. Pattern Recognit. 69, 213–224 (2017)CrossRefGoogle Scholar
  5. 5.
    Wang, C., Zhang, L.W.J., 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
  6. 6.
    Lam, T.H.W., Cheung, K.H., Liu, J.N.K.: Gait flow image: a silhouette-based gait representation for human identification. J. Pattern Recognit. 44(4), 973–987 (2011)CrossRefzbMATHGoogle Scholar
  7. 7.
    Dollar, P., Rabaud, V., Cottrell, G., Belongie, S.: Behavior recognition via sparse spatio-temporal filters. In: IEEE International Workshop VS-PETS, Beijing, China (2005)Google Scholar
  8. 8.
    Shabani, A.H., Clausi, D.A., Zelek, J.S.: Improved spatio-temporal salient feature detection for action recognition. In: Proceedings of the British Machine Vision Conference, Dundee (2011)Google Scholar
  9. 9.
    Ghaeminia, M.H., Shokouhi, S.B.: GSI: efficient spatio-temporal template for human gait recognition. Int. J. Biom. (IJBM) 10, 29–51 (2018)CrossRefGoogle Scholar
  10. 10.
    Ghaeminia, M.H., Badiezadeh, A., Shokouhi, S.B.: An efficient energy model for human gait recognition. In: DICTA, Gold Coast (2016)Google Scholar
  11. 11.
    Makihara, Y., Suzuki, A., Muramatsu, D., Li, X., Yagi, Y.: Joint intensity and spatial metric learning for robust gait recognition. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI (2017)Google Scholar
  12. 12.
    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
  13. 13.
    Hossain, M., Makihara, Y., Wang, J., Yagi, Y.: Clothing-invariant gait identification using part-based clothing categorization and adaptive weight control. Pattern Recognit. 43(6), 2281–2291 (2010)CrossRefGoogle Scholar
  14. 14.
    Guan, Y., Li, C.-T., Hu, Y.: Random subspace method for gait recognition. In: IEEE International Conference on Multimedia Expo Workshops (2012)Google Scholar
  15. 15.
    Choudhury, S.D., Tjahjadi, T.: Robust view-invariant multiscale gait recognition. Pattern Recognit. 48(3), 798–811 (2015)CrossRefGoogle Scholar
  16. 16.
    Lai, Z., Xu, Y., Jin, Z., Zhang, D.: Human gait recognition via sparse discriminant projection learning. IEEE Trans. Circuits Syst. Video Technol. 24(10), 1651–1662 (2014)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag London Ltd., part of Springer Nature 2018

Authors and Affiliations

  1. 1.School of Electrical EngineeringIran University of Science and TechnologyTehranIran

Personalised recommendations