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

Noha A. Hikal

Research Article

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

by Noha A. Hikal

International Journal of Computer Applications

Foundation of Computer Science (FCS), NY, USA

Volume 68 - Issue 9

Published: April 2013

Authors: Noha A. Hikal

10.5120/11610-6991

PDF

Noha A. Hikal . A Comparative Evaluation Study of Automated Gait Recognition based on Spatiotemporal Feature and Different Neural Network Classifiers. International Journal of Computer Applications. 68, 9 (April 2013), 36-42. DOI=10.5120/11610-6991

                        @article{ 10.5120/11610-6991,
                        author  = { Noha A. Hikal },
                        title   = { 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 },
                        doi     = { 10.5120/11610-6991 },
                        publisher = { Foundation of Computer Science (FCS), NY, USA }
                        }

                        %0 Journal Article
                        %D 2013
                        %A Noha A. Hikal
                        %T A Comparative Evaluation Study of Automated Gait Recognition based on Spatiotemporal Feature and Different Neural Network Classifiers%T 
                        %J International Journal of Computer Applications
                        %V 68
                        %N 9
                        %P 36-42
                        %R 10.5120/11610-6991
                        %I Foundation of Computer Science (FCS), NY, USA

Abstract

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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Index Terms

Computer Science

Information Sciences

No index terms available.

Keywords

Gait energy image discrete cosine transform discrete wavelet transform principle component analysis support vector machine multilayer perceptron radial basis function generalized feed forward network