Call for Paper - August 2022 Edition
IJCA solicits original research papers for the August 2022 Edition. Last date of manuscript submission is July 20, 2022. Read More

A Comparative Evaluation Study of Automated Gait Recognition based on Spatiotemporal Feature and Different Neural Network Classifiers

International Journal of Computer Applications
© 2013 by IJCA Journal
Volume 68 - Number 9
Year of Publication: 2013
Noha A. Hikal

Noha A Hikal. Article: A Comparative Evaluation Study of Automated Gait Recognition based on Spatiotemporal Feature and Different Neural Network Classifiers. International Journal of Computer Applications 68(9):36-42, April 2013. Full text available. BibTeX

	author = {Noha A. Hikal},
	title = {Article: A Comparative Evaluation Study of Automated Gait Recognition based on Spatiotemporal Feature and Different Neural Network Classifiers},
	journal = {International Journal of Computer Applications},
	year = {2013},
	volume = {68},
	number = {9},
	pages = {36-42},
	month = {April},
	note = {Full text available}


New areas of applications such as: human-computer interaction, access control, surveillance, activity monitoring and clinical analysis depend on hepatic technology. Gait analysis has been explored thoroughly during the last decade as a behavioral biometric feature which doesn't require subject interaction. In this paper, persons can be recognized from their gait regardless of the angle of walking seen. The performance of four artificial neural networks (ANNs) based classifiers was evaluated and tested, based on spatiotemporal features. The results show that discrete wavelet transforms and support vector machine recognition technique provides a recognition rates up to 94%. Moreover, it is characterized by speed and accuracy compared with other classifiers.


  • K. Delac, M. Grgic, A survey of biometric recognition methods, 46th International Symposium Electronics in Marine, 2004, pp. 184-193.
  • A. K. Jain, Technology: biometric recognition, Nature 449(6) (2007) 38-40.
  • S. Sarkar, et al, The human ID gait challenge problems: data sets, performance and analysis, IEEE Trans. Pattern Analysis. Machine Intelligence 27(2) (2005), pp. 162-177.
  • S. Yu, et al, A study on gait based gender classification, IEEE transaction on image processing,vol. 18,No. 8 august 2009
  • Wei Zeng, Cong Wang, Human gait recognition via deterministic learning, Elsevier Science, Journal of neural network, Volume 35, November, 2012 Pages 92-102
  • R. Hu, W. Shen, H. Wang, Recursive spatiotemporal subspace learning for gait recognition, ELSEVIER, neurocomputing 73 (2010),pp. 1892-1899
  • H. Lu, P. Venetsanopoulos, A layered deformable model for gait analysis, 7th International Conference on |Automatic Face and Gesture Recognition, April 2006, pp. 249-254
  • G. Zhao, G. Liu, H. Li, 3D gait recognition using multiple cameras, 7th International Conference on Automatic Face and Gesture Recognition. April 2006, pp. 529-534.
  • F. Tafazzoli, R. Safabakhsh, Model-based human gait recognition using leg and arm movements, ELSEVIER, Engineering Application of Artificial Intelligence (2010), doi:10. 1016/j. engappai. 2010. 07. 004
  • http://www. cbsr. ia. ac. cn/english/Gait%20Databases. asp CASIA database. Mars 2013
  • Z. Xue, et al, Infrared gait recognition based on wavelet transform and support vector machine, ELSEVIER, pattern recognition 43 (2010), pp. 2904-1910.
  • John C. Russ, The Image Processing Handbook, 3rd Ed. , CRC press ISBN:0849325323, 1998
  • R. C. Gonzales and R. E. Woods. Digital Image Processing. Prentice Hall, second edition. ISBN 0-201-18075-8. 2002
  • C. Torrence, & G. Compo, A practical guide to wavelet analysis, American Meteorological Society, Vol. 79, No. 1, January 1998.
  • Lakhmi Jain, Anna Maria, Recent advances in artificial neural networks design and applications. 2000 by CRC press LLC
  • MATLAB 2011. b, Neuro-Solutions 5
  • S. Yu, D. Tan, T. Tan, A framework for evaluating the effect of view angle clothing and carrying condition on gait recognition, 18th International Conference of Pattern Recognition, vol. 4,Hong Kong , China,2006, pp. 441-444.
  • K. Bashir, T. Xiang, S. Gong, Gait recognition without subject cooperation, ELSEVIER, pattern recognition letters 31 (2010). Pp. 2052-2062.