Open AccessPrincipal component analysis for human gait recognition system
Author(s) -
Othman O. Khalifa,
Bilal Jawed,
Sharif Shah Newaj Bhuiyn
Publication year - 2019
Publication title -
bulletin of electrical engineering and informatics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.251
H-Index - 12
ISSN - 2302-9285
DOI - 10.11591/eei.v8i2.1493
Subject(s) - silhouette , principal component analysis , gait , artificial intelligence , pattern recognition (psychology) , gait analysis , classifier (uml) , computer science , computer vision , component (thermodynamics) , orientation (vector space) , mathematics , physical medicine and rehabilitation , geometry , medicine , physics , thermodynamics
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.