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Reseach Article

View variations Effect in Human Gait Recognition using Sub-Window Extraction Algorithm

by Gyan C. Shivhare, Unmukh Datta
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 73 - Number 12
Year of Publication: 2013
Authors: Gyan C. Shivhare, Unmukh Datta
10.5120/12797-0180

Gyan C. Shivhare, Unmukh Datta . View variations Effect in Human Gait Recognition using Sub-Window Extraction Algorithm. International Journal of Computer Applications. 73, 12 ( July 2013), 46-49. DOI=10.5120/12797-0180

@article{ 10.5120/12797-0180,
author = { Gyan C. Shivhare, Unmukh Datta },
title = { View variations Effect in Human Gait Recognition using Sub-Window Extraction Algorithm },
journal = { International Journal of Computer Applications },
issue_date = { July 2013 },
volume = { 73 },
number = { 12 },
month = { July },
year = { 2013 },
issn = { 0975-8887 },
pages = { 46-49 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume73/number12/12797-0180/ },
doi = { 10.5120/12797-0180 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:39:56.930199+05:30
%A Gyan C. Shivhare
%A Unmukh Datta
%T View variations Effect in Human Gait Recognition using Sub-Window Extraction Algorithm
%J International Journal of Computer Applications
%@ 0975-8887
%V 73
%N 12
%P 46-49
%D 2013
%I Foundation of Computer Science (FCS), NY, USA
Abstract

In this paper, the view variations effect in human gait recognition using sub-window extraction algorithm is proposed. Here different variation is created based on the walking people in three different angles (i. e. 00, 450 and 900)with respect to particular line. Our proposed method works on two different phases: Extraction phase and Recognition Phase. In first phase, gait images, captured from different angles, are enhanced using clipping, filtering and histogram equalization. Then apply proposed sub window extraction algorithm on enhanced gait images and gathered different features like person length, leg angle, leg length, hand length etc. Finally apply back propagation algorithm for the recognition of gait images. Experiments are carried out using different datasets.

References
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Index Terms

Computer Science
Information Sciences

Keywords

Back propagation algorithm BPA Gait Recognition Neural Network Sub-window extraction