Gender classification in human gait using support vector machine
Gender classification in human gait using support vector machine
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.
138-145
Yoo, Jang
45c92603-cdd9-44cc-b8eb-67d9a5e149bc
Hwang, D
0f7f24d5-4a7f-4b0c-b1a7-875a7f6a7a18
Nixon, Mark
2b5b9804-5a81-462a-82e6-92ee5fa74e12
2006
Yoo, Jang
45c92603-cdd9-44cc-b8eb-67d9a5e149bc
Hwang, D
0f7f24d5-4a7f-4b0c-b1a7-875a7f6a7a18
Nixon, Mark
2b5b9804-5a81-462a-82e6-92ee5fa74e12
Yoo, Jang, Hwang, D and Nixon, Mark
(2006)
Gender classification in human gait using support vector machine.
ADVANCED CONCEPTS FOR INTELLIGENT VISION SYSTEMS.
.
Record type:
Conference or Workshop Item
(Other)
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.
Text
jang_gender.pdf
- Other
More information
Published date: 2006
Venue - Dates:
ADVANCED CONCEPTS FOR INTELLIGENT VISION SYSTEMS, 2006-01-01
Organisations:
Southampton Wireless Group
Identifiers
Local EPrints ID: 263371
URI: http://eprints.soton.ac.uk/id/eprint/263371
PURE UUID: 069f8cc8-dc45-4657-9f08-17e8c9669b64
Catalogue record
Date deposited: 01 Feb 2007
Last modified: 15 Mar 2024 02:35
Export record
Contributors
Author:
Jang Yoo
Author:
D Hwang
Download statistics
Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.
View more statistics
