Paper Abstract and Keywords |
Presentation |
2018-12-25 15:00
Human Gait Recognition Method for Long and Baggy Clothes Using Machine Learning Abrar Alharbi, Eiji Kamioka (SIT) MoNA2018-50 |
Abstract |
(in Japanese) |
(See Japanese page) |
(in English) |
Human gait is a significant biometric feature used for the identification of people by their style of walking. In comparison with other methods of biometric information, gait offers recognition from a distance at low resolution while requiring no user interaction, whereas other biometrics are likely to require a certain level of interaction. In This paper, a human gait recognition method is presented to identify people who are wearing long baggy clothes like Thoub and Abaya. Microsoft Kinect sensor is used as a tool to establish a skeleton based gait database. The skeleton joint positions are obtained and used as input parameters for classification. An evaluation experiment was carried out with 15 walking subjects, each having 30 walking sequences in total, achieving the recognition rate of 100% using Support Vector Machine classifier. |
Keyword |
(in Japanese) |
(See Japanese page) |
(in English) |
Gait Recognition / Biometric Systems / Kinect Sensor / Model-based System / Machine Learning / / / |
Reference Info. |
IEICE Tech. Rep., vol. 118, no. 378, MoNA2018-50, pp. 67-72, Dec. 2018. |
Paper # |
MoNA2018-50 |
Date of Issue |
2018-12-18 (MoNA) |
ISSN |
Online edition: ISSN 2432-6380 |
Copyright and reproduction |
All rights are reserved and no part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopy, recording, or any information storage and retrieval system, without permission in writing from the publisher. Notwithstanding, instructors are permitted to photocopy isolated articles for noncommercial classroom use without fee. (License No.: 10GA0019/12GB0052/13GB0056/17GB0034/18GB0034) |
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MoNA2018-50 |
Conference Information |
Committee |
MoNA |
Conference Date |
2018-12-25 - 2018-12-25 |
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(See Japanese page) |
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General |
Paper Information |
Registration To |
MoNA |
Conference Code |
2018-12-MoNA |
Language |
English |
Title (in Japanese) |
(See Japanese page) |
Sub Title (in Japanese) |
(See Japanese page) |
Title (in English) |
Human Gait Recognition Method for Long and Baggy Clothes Using Machine Learning |
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Gait Recognition |
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Biometric Systems |
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Kinect Sensor |
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Model-based System |
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Machine Learning |
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1st Author's Name |
Abrar Alharbi |
1st Author's Affiliation |
Shibaura Institute of Technology (SIT) |
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Eiji Kamioka |
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Shibaura Institute of Technology (SIT) |
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Speaker |
Author-1 |
Date Time |
2018-12-25 15:00:00 |
Presentation Time |
20 minutes |
Registration for |
MoNA |
Paper # |
MoNA2018-50 |
Volume (vol) |
vol.118 |
Number (no) |
no.378 |
Page |
pp.67-72 |
#Pages |
6 |
Date of Issue |
2018-12-18 (MoNA) |
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