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Conference Paper Gender Classification in Human Gait Using Support Vector Machine
Cited 113 time in scopus Share share facebook twitter linkedin kakaostory
Authors
Jang Hee Yoo, Doo Sung Hwang, Mark S. Nixon
Issue Date
2005-09
Citation
International Conference on Advanced Concepts for Intelligent Vision Systems (ACIVS) 2005 (LNCS 3708), v.3708, pp.138-145
Publisher
Springer
Language
English
Type
Conference Paper
DOI
https://dx.doi.org/10.1007/11558484_18
Abstract
We describe an automated system that classifies gender by utilising a set of human gait data. The gender classification system consists of three stages: i) detection and extraction of the moving human body and its contour from image sequences; ii) extraction of human gait signature by the joint angles and body points; and iii) motion analysis and feature extraction for classifying gender in the gait patterns. A sequential set of 2D stick figures is used to represent the gait signature that is primitive data for the feature generation based on motion parameters. Then, an SVM classifier is used to classify gender in the gait patterns. In experiments, higher gender classification performances, which are 96% for 100 subjects, have been achieved on a considerably larger database. © Springer-Verlag Berlin Heidelberg 2005.
KSP Keywords
Classification system, Detection and extraction, Feature extractioN, Feature generation, Image sequence, Joint angles, Motion parameters, SVM Classifier, Support VectorMachine(SVM), an automated system, gait patterns