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Erschienen in:
2005 | OriginalPaper | Buchkapitel
Gender Classification in Human Gait Using Support Vector Machine
verfasst von : Jang-Hee Yoo, Doosung Hwang, Mark S. Nixon
Erschienen in: Advanced Concepts for Intelligent Vision Systems
Verlag: Springer Berlin Heidelberg
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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.