Principal component analysis for human gait recognition system

Khalifa, Othman Omran and Jawed, Bilal and Bhuiyn, Sharif Shah Newaj (2019) Principal component analysis for human gait recognition system. Bulletin of Electrical Engineering and Informatics, 8 (2). pp. 571-578. ISSN 2302-9285

	PDF - Published Version Restricted to Registered users only Download (363kB) | Request a copy
	PDF (SCOPUS) Restricted to Registered users only Download (128kB) | Request a copy

Abstract

This paper represents a method for Human Recognition system using Principal Component Analysis. Human Gait recognition works on the gait of walking subjects to identify people without them knowing or without their permission. The initial step in this kind of system is to generate silhouette frames of walking human. A number of features couldb be exytacted from these frames such as centriod ratio, heifht, width and orientation. The Principal Component Analysis (PCA) is used for the extracted features to condense the information and produces the main components that can represent the gait sequences for each waiking human. In the testing phase, the generated gait sequences are recognized by using a minimum distance classifier based on eluclidean distance matched with the one that already exist in the database used to identify walking subject.

Item Type:	Article (Journal)
Additional Information:	4119/72092
Uncontrolled Keywords:	Biometric, Gait recognition, Human identification, PCA, Silhouette, Video surveillance,
Subjects:	T Technology > T Technology (General) T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK5101 Telecommunication. Including telegraphy, radio, radar, television
Kulliyyahs/Centres/Divisions/Institutes (Can select more than one option. Press CONTROL button):	Kulliyyah of Engineering Kulliyyah of Engineering > Department of Electrical and Computer Engineering
Depositing User:	Prof. Dr Othman O. Khalifa
Date Deposited:	13 May 2019 12:04
Last Modified:	24 Nov 2019 21:27
URI:	http://irep.iium.edu.my/id/eprint/72092

Actions (login required)

View Item