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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
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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Conference Information
Committee PRMU BioX  
Conference Date 2016-03-24 - 2016-03-25 
Place (in Japanese) (See Japanese page) 
Place (in English)  
Topics (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)  
Keyword(1) Gait  
Keyword(2) Cross-view  
Keyword(3) Recognition  
Keyword(4) Deep learning  
Keyword(5) CNN  
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  
5th Author's Affiliation 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 
Date of Issue 2016-03-17 (BioX, PRMU) 

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