Investigation of Gait Representations in Lower Knee Gait Recognition

Chirawat Wattanapanich, Hong Wei

Abstract

This paper investigates the effect of lower knee gait representations on gait recognition. After reviewing three emerging gait representations, i.e. Gait Energy Image (GEI), Gait Entropy Image (GEnI), and Gait Gaussian Image (GGI), a new gait representation, Gait Gaussian Entropy Image (GGEnI), is proposed to combine advantages of entropy and Gaussian in improving the robustness to noises and appearance changes. Experimental results have shown that lower knee gait representations can successfully detect camera view angles in CASIA Gait Dataset B, and they are better than full body representations in gait recognition under the condition of wearing coat. The gait representations involving the Gaussian technique have shown robustness to noises, whilst the representations involving entropy provide a better robustness to appearance changes.

References

  1. Arora, P. & Srivastava, S. Gait Recognition Using Gait Gaussian Image. Signal Processing And Integrated Networks (Spin), 2015 2nd International Conference On, 19-20 Feb. 2015 2015. 791-794.
  2. Bashir, K., Tao, X. & Shaogang, G. Gait Recognition Using Gait Entropy Image. Crime Detection And Prevention (Icdp 2009), 3rd International Conference On, 3-3 Dec. 2009 2009. 1-6.
  3. Bashir, K., Xiang, T. & Gong, S. 2010. Gait Recognition Without Subject Cooperation. Pattern Recognition Letters, 31, 2052-2060.
  4. Chang, C.-C. & Lin, C.-J. 2011. Libsvm: A Library For Support Vector Machines. Acm Trans. Intell. Syst. Technol., 2, 1-27.
  5. Chattopadhyay, P., Sural, S. & Mukherjee, J. 2014. Frontal Gait Recognition From Incomplete Sequences Using Rgb-D Camera. Information Forensics And Security, Ieee Transactions On, 9, 1843-1856.
  6. Haifeng, H. 2014. Multiview Gait Recognition Based On Patch Distribution Features And Uncorrelated Multilinear Sparse Local Discriminant Canonical Correlation Analysis. Circuits And Systems For Video Technology, Ieee Transactions On, 24, 617-630.
  7. Han, J. & Bhanu, B. 2006. Individual Recognition Using Gait Energy Image. Pattern Analysis And Machine Intelligence, Ieee Transactions On, 28, 316-322.
  8. Hu, N. H.-L., Tong; Wooi-Haw, Tan ; Timothy Tzen-Vun, Yap; Pei-Fen, Chong; Junaidi, Abdullah 2011. Human Identification Based On Extracted Gait Features. International Journal On New Computer Architectures And Their Applications (Ijncaa), 1(2).
  9. Iwashita, Y., Ogawara, K. & Kurazume, R. 2014. Identification Of People Walking Along Curved Trajectories. Pattern Recognition Letters, 48, 60-69.
  10. Jackson, J. E. 2003. A User's Guide To Principal Components, Canada, John Wiley & Sons.
  11. Jolliffe, I. T. 2002. Principal Component Analysis, 2nd Edition, New York, Springer-Verlag New York, Inc.
  12. Mansur, A., Makihara, Y., Aqmar, R. & Yagi, Y. Gait Recognition Under Speed Transition. Computer Vision And Pattern Recognition (Cvpr), 2014 Ieee Conference On, 23-28 June 2014 2014. 2521-2528.
  13. Nandy, A., Pathak, A., Chakraborty, P. & Nandi, G. C. Gait Identification Using Component Based Gait Energy Image Analysis. Signal Propagation And Computer Technology (Icspct), 2014 International Conference On, 12-13 July 2014 2014. 380-385.
  14. Ralph, G. J., Shi 2001. The Cmu Motion Of Body (Mobo) Database Technical Report. Robotic Institute, Carnegie Mellon University.
  15. Rong, Z., Vogler, C. & Metaxas, D. Human Gait Recognition. Computer Vision And Pattern Recognition Workshop, 2004. Cvprw 7804. Conference On, 27-02 June 2004 2004. 18-18.
  16. Shirke, S., Pawar, S. S. & Shah, K. Literature Review: Model Free Human Gait Recognition. Communication Systems And Network Technologies (Csnt), 2014 Fourth International Conference On, 7-9 April 2014 2014. 891-895.
  17. Shutler, J., Grant, M., Nixon, M. S. & Carter, J. N. 2002. On A Large Sequence-Based Human Gait Database. Fourth International Conference On Recent Advances In Soft Computing.
  18. Yang, Y., Tu, D. & Li, G. Gait Recognition Using Flow Histogram Energy Image. Pattern Recognition (Icpr), 2014 22nd International Conference On, 24-28 Aug. 2014 2014. 444-449.
  19. Yu, S., Tan, D. & Tan, T. A Framework For Evaluating The Effect Of View Angle, Clothing And Carrying Condition On Gait Recognition. Pattern Recognition, 2006. Icpr 2006. 18th International Conference On, 0-0 0 2006. 441-444.
  20. Zeng, W., Wang, C. & Yang, F. 2014. Silhouette-Based Gait Recognition Via Deterministic Learning. Pattern Recognition, 47, 3568-3584.
  21. Zhang, E., Zhao, Y. & Xiong, W. 2010. Active Energy Image Plus 2dlpp For Gait Recognition. Signal Processing, 90, 2295-2302.
  22. Zheng, S., Zhang, J. G., Huang, K. Q., He, R. & Tan, T. N. 2011. Robust View Transformation Model For Gait Recognition. 2011 18th Ieee International Conference On Image Processing (Icip).
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Paper Citation


in Harvard Style

Wattanapanich C. and Wei H. (2016). Investigation of Gait Representations in Lower Knee Gait Recognition . In Proceedings of the 5th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM, ISBN 978-989-758-173-1, pages 678-683. DOI: 10.5220/0005817006780683


in Bibtex Style

@conference{icpram16,
author={Chirawat Wattanapanich and Hong Wei},
title={Investigation of Gait Representations in Lower Knee Gait Recognition},
booktitle={Proceedings of the 5th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,},
year={2016},
pages={678-683},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005817006780683},
isbn={978-989-758-173-1},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 5th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,
TI - Investigation of Gait Representations in Lower Knee Gait Recognition
SN - 978-989-758-173-1
AU - Wattanapanich C.
AU - Wei H.
PY - 2016
SP - 678
EP - 683
DO - 10.5220/0005817006780683