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Reseach Article

Human Gait Recognition using Silhouette Vector and Principal Component Analysis

Published on December 2014 by Sagar A. More, Pramod J. Deore
National Conference on Advances in Communication and Computing
Foundation of Computer Science USA
NCACC2014 - Number 1
December 2014
Authors: Sagar A. More, Pramod J. Deore

Sagar A. More, Pramod J. Deore . Human Gait Recognition using Silhouette Vector and Principal Component Analysis. National Conference on Advances in Communication and Computing. NCACC2014, 1 (December 2014), 1-6.

author = { Sagar A. More, Pramod J. Deore },
title = { Human Gait Recognition using Silhouette Vector and Principal Component Analysis },
journal = { National Conference on Advances in Communication and Computing },
issue_date = { December 2014 },
volume = { NCACC2014 },
number = { 1 },
month = { December },
year = { 2014 },
issn = 0975-8887,
pages = { 1-6 },
numpages = 6,
url = { /proceedings/ncacc2014/number1/19117-2010/ },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
%0 Proceeding Article
%1 National Conference on Advances in Communication and Computing
%A Sagar A. More
%A Pramod J. Deore
%T Human Gait Recognition using Silhouette Vector and Principal Component Analysis
%J National Conference on Advances in Communication and Computing
%@ 0975-8887
%V NCACC2014
%N 1
%P 1-6
%D 2014
%I International Journal of Computer Applications

Lot of research in the field of human recognition is being carried out. Gait recognition is a relatively new approach which is gaining momentum in biometrics. We have demonstrated a simple approach as a solution to this problem. We have taken a feature which was proposed earlier i. e. the Silhouette Vector. This is the distance of boundary points from the centroid of the silhouette as it rotates 360 degrees. Additional to the silhouette vector, we divided the silhouette image into three equal parts vertically (Rectangular Features) and computed some statistical properties of these parts. These properties were also added to the silhouette vector and given to the PCA training system. Training was performed using silhouette vectors and rectangular vectors for each subject. For testing the system, nearest neighbor method was used which is one of the simplest algorithms used for classification problems. The test subject is assigned to the class which is the minimum Euclidean distance from it. Inclusion of the additional features has improved the system performance greatly. Cumulative match score was used to analyze the system performance.

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Index Terms

Computer Science
Information Sciences


Gait Cycle Silhouette Vector Rectangular Vector Pca