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Gender classification in human gait using support vector machine

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
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. pp. 138-145 .

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.

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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
ORCID for Mark Nixon: ORCID iD orcid.org/0000-0002-9174-5934

Catalogue record

Date deposited: 01 Feb 2007
Last modified: 07 Oct 2020 02:32

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