Paper Abstract and Keywords |
Presentation |
2016-03-25 10:15
Cross-view Gait Recognition using Convolutional Neural Network Kohei Shiraga, Yasushi Makihara, Daigo Muramatsu (Osaka Univ.), Tomio Echigo (Osaka Electro-Communication Univ.), Yasushi Yagi (Osaka Univ.) BioX2015-57 PRMU2015-180 |
Abstract |
(in Japanese) |
(See Japanese page) |
(in English) |
We propose a robust cross-view gait recognition method employing a convolutional neural network (CNN) in this paper. We focus on gait energy image (GEI) as an input to a CNN, and design a structure of CNN so that it can extract a view-invariant and discriminative feature from the input GEI; we call this network {it GEINet}. In order to demonstrate the effectiveness of GEINet for cross-view gait recognition, we evaluated recognition accuracy of GEINet on a subset of OU-ISIR large population dataset under multiple settings. The evaluation results show that the proposed GEINet outperforms the state-of-the-art approaches especially in verification scenarios. |
Keyword |
(in Japanese) |
(See Japanese page) |
(in English) |
Gait / Cross-view / Recognition / Deep learning / CNN / / / |
Reference Info. |
IEICE Tech. Rep., vol. 115, no. 516, BioX2015-57, pp. 87-92, March 2016. |
Paper # |
BioX2015-57 |
Date of Issue |
2016-03-17 (BioX, PRMU) |
ISSN |
Print edition: ISSN 0913-5685 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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BioX2015-57 PRMU2015-180 |
Conference Information |
Committee |
PRMU BioX |
Conference Date |
2016-03-24 - 2016-03-25 |
Place (in Japanese) |
(See Japanese page) |
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Paper Information |
Registration To |
BioX |
Conference Code |
2016-03-PRMU-BioX |
Language |
Japanese |
Title (in Japanese) |
(See Japanese page) |
Sub Title (in Japanese) |
(See Japanese page) |
Title (in English) |
Cross-view Gait Recognition using Convolutional Neural Network |
Sub Title (in English) |
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Keyword(1) |
Gait |
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Cross-view |
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Recognition |
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Deep learning |
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CNN |
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1st Author's Name |
Kohei Shiraga |
1st Author's Affiliation |
Osaka University (Osaka Univ.) |
2nd Author's Name |
Yasushi Makihara |
2nd Author's Affiliation |
Osaka University (Osaka Univ.) |
3rd Author's Name |
Daigo Muramatsu |
3rd Author's Affiliation |
Osaka University (Osaka Univ.) |
4th Author's Name |
Tomio Echigo |
4th Author's Affiliation |
Osaka Electro-Communication University (Osaka Electro-Communication Univ.) |
5th Author's Name |
Yasushi Yagi |
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Osaka University (Osaka Univ.) |
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Speaker |
Author-1 |
Date Time |
2016-03-25 10:15:00 |
Presentation Time |
30 minutes |
Registration for |
BioX |
Paper # |
BioX2015-57, PRMU2015-180 |
Volume (vol) |
vol.115 |
Number (no) |
no.516(BioX), no.517(PRMU) |
Page |
pp.87-92 |
#Pages |
6 |
Date of Issue |
2016-03-17 (BioX, PRMU) |
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