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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.


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