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Selecting the Effective Regions for Gait Recognition by Sparse Representation

  • Jiaqi Tan
  • Jiawei Wang
  • Shiqi Yu
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10996)

Abstract

In gait recognition the variations of clothing and carrying conditions can change the human body shape greatly. So the gait feature extracted from human body images will be greatly affected and the performance will decrease drastically. Thus in this paper, we proposed one gait recognition method to improve the robustness towards these variations. The main idea is to select effective regions by sparse representation. If the region can be represented by features from gait data without variations, that means the region is not occluded by some objects. Experimental results on a large gait dataset show that the proposed method can achieve high recognition rates, and even outperform some deep learning based methods.

Keywords

Gait recognition Sparse representation HOG features Gait energy image 

Notes

Acknowledgment

The work is supported by the strategic new and future industrial development fund of Shenzhen (Grant No. 20170504160426188).

References

  1. 1.
    Bashir, K., Xiang, T., Gong, S.: Gait recognition using gait entropy image. In: 3rd International Conference on Crime Detection and Prevention (ICDP 2009), pp. 1–6, January 2009Google Scholar
  2. 2.
    Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2005), vol. 1, pp. 886–893, June 2005Google Scholar
  3. 3.
    Dupuis, Y., Savatier, X., Vasseur, P.: Feature subset selection applied to model-free gait recognition. Image Vis. Comput. 31(8), 580–591 (2013)CrossRefGoogle Scholar
  4. 4.
    Gong, M., Xu, Y., Yang, X., Zhang, W.: Gait identification by sparse representation. In: 2011 Eighth International Conference on Fuzzy Systems and Knowledge Discovery (FSKD), vol. 3, pp. 1719–1723, July 2011Google Scholar
  5. 5.
    Han, J., Bhanu, B.: Individual recognition using gait energy image. IEEE Trans. Pattern Anal. Mach. Intell. 28(2), 316–322 (2006)CrossRefGoogle Scholar
  6. 6.
    Jeevan, M., Jain, N., Hanmandlu, M., Chetty, G.: Gait recognition based on gait pal and pal entropy image. In: 2013 IEEE International Conference on Image Processing, pp. 4195–4199, September 2013Google Scholar
  7. 7.
    Kusakunniran, W.: Recognizing gaits on spatio-temporal feature domain. IEEE Trans. Inf. Forensics Secur. 9(9), 1416–1423 (2014)CrossRefGoogle Scholar
  8. 8.
    Lai, Z., Xu, Y., Jin, Z., Zhang, D.: Human gait recognition via sparse discriminant projection learning. IEEE Trans. Circ. Syst. Video Technol. 24(10), 1651–1662 (2014)CrossRefGoogle Scholar
  9. 9.
    Lai, Z., Xu, Y., Yang, J., Tang, J., Zhang, D.: Sparse tensor discriminant analysis. IEEE Trans. Image Process. 22(10), 3904–3915 (2013)MathSciNetCrossRefGoogle Scholar
  10. 10.
    Liao, R., Cao, C., Garcia, E.B., Yu, S., Huang, Y.: Pose-based temporal-spatial network (PTSN) for gait recognition with carrying and clothing variations. In: Zhou, J., et al. (eds.) CCBR 2017. LNCS, vol. 10568, pp. 474–483. Springer, Cham (2017).  https://doi.org/10.1007/978-3-319-69923-3_51CrossRefGoogle Scholar
  11. 11.
    Pati, Y.C., Rezaiifar, R., Krishnaprasad, P.S.: Orthogonal matching pursuit: recursive function approximation with applications to wavelet decomposition. In: Proceedings of 27th Asilomar Conference on Signals, Systems and Computers, vol. 1, pp. 40–44, November 1993Google Scholar
  12. 12.
    Tanawongsuwan, R., Bobick, A.: Gait recognition from time-normalized joint-angle trajectories in the walking plane. In: IEEE Conference on Computer Vision and Pattern Recognition, p. 726 (2001)Google Scholar
  13. 13.
    Wang, J., Garcia, E.B., Yu, S., Zhang, D.: Windowed DMD for gait recognition under clothing and carrying condition variations. In: Zhou, J. (ed.) CCBR 2017. LNCS, vol. 10568, pp. 484–492. Springer, Cham (2017).  https://doi.org/10.1007/978-3-319-69923-3_52CrossRefGoogle Scholar
  14. 14.
    Wang, L., Tan, T., Hu, W., Ning, H.: Automatic gait recognition based on statistical shape analysis. IEEE Trans. Image Process. 12(9), 1120–1131 (2003)MathSciNetCrossRefGoogle Scholar
  15. 15.
    Wu, Z., Huang, Y., Wang, L., Wang, X., Tan, T.: A comprehensive study on cross-view gait based human identification with deep CNNS. IEEE Trans. Pattern Anal. Mach. Intell. 39(2), 209–226 (2016)CrossRefGoogle Scholar
  16. 16.
    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)MathSciNetCrossRefGoogle Scholar
  17. 17.
    Yu, S., Chen, H., Reyes, E.B.G., Poh, N.: Gaitgan: invariant gait feature extraction using generative adversarial networks. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), pp. 532–539. IEEE (2017)Google Scholar
  18. 18.
    Yu, S., Chen, H., Wang, Q., Shen, L., Huang, Y.: Invariant feature extraction for gait recognition using only one uniform model. Neurocomputing 239, 81–93 (2017)Google Scholar
  19. 19.
    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 (ICPR 2006), vol. 4, pp. 441–444 (2006)Google Scholar
  20. 20.
    Yu, S., Wang, L., Hu, W., Tan, T.: Gait analysis for human identification in frequency domain. In: International Conference on Image and Graphics, pp. 282–285 (2004)Google Scholar
  21. 21.
    Yu, S., Wang, Q., Shen, L., Huang, Y.: View invariant gait recognition using only one uniform model. In: 23rd International Conference on Pattern Recognition (ICPR 2016), pp. 889–894 (2016)Google Scholar
  22. 22.
    Zhang, E., Zhao, Y., Xiong, W.: Active energy image plus 2DLPP for gait recognition. Signal Process. 90(7), 2295–2302 (2010)CrossRefGoogle Scholar
  23. 23.
    Zhao, G., Liu, G., Li, H., Pietikainen, M.: 3D gait recognition using multiple cameras. In: International Conference on Automatic Face and Gesture Recognition, pp. 529–534 (2006)Google Scholar
  24. 24.
    Zheng, S., Zhang, J., Huang, K., He, R., Tan, T.: Robust view transformation model for gait recognition. In: 18th IEEE International Conference on Image Processing (ICIP), pp. 2073–2076. IEEE (2011)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.College of Computer Science and Software EngineeringShenzhen UniversityShenzhenChina
  2. 2.College of Physics and EnergyShenzhen UniversityShenzhenChina

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