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Paper Abstract and Keywords
Presentation 2013-01-23 09:30
Face model creation based on simultaneous execution of hierarchical training-set clustering and common local feature extraction
Takayuki Fukui, Toshikazu Wada, Hiroshi Oike, Jun Sakata (Wakayama Univ.) PRMU2012-84 MVE2012-49
Abstract (in Japanese) (See Japanese page) 
(in English) Face image retrieval based on local features has advantages of short elapsed time and robustness against the occlusions. However, the keypoint detection, beforehand with the feature description, may fail due to illumination change. For solving this problem, top-down model-based keypoint detection must be effective, where man-made face model does not fit for this task. This report addresses the problem of bottom-up face model creation from examples, which can be formalized as common local feature extraction among examples. For this purpose, a measure called Diverse Density (DD) established in the field of Multiple Instance Learning (MIL) can be applied. DD at a point in a feature space represents how the point is close to other positive examples while keeping enough distance from negative examples. Because of this this property, DD is defined as a product of metrics, which can easily be affected by exceptional data, i.e., if one negative data leaps into the neighbor of a positive example, the DD around there becomes lower. Actually, face images have wide variations of face organs’ positions, beard, mustache, glasses, and so on. Under these variations, DD for wide varieties of face images will be low at any point in the feature point. For solving this problem, we propose a method performing hierarchical clustering and common feature extraction simultaneously. In this method, DD score is employed as a measure representing the integrity of the face image set, and hierarchical clustering is performed by merging the cluster pair having maximum DD score. Through experiments on 1021 CASPEAL face images, we confirmed that multiple face models are successfully constructed.
Keyword (in Japanese) (See Japanese page) 
(in English) Multiple Instance Learning / Diverse Density / hierarchical clustering / common image features / / / /  
Reference Info. IEICE Tech. Rep., vol. 112, no. 385, PRMU2012-84, pp. 23-28, Jan. 2013.
Paper # PRMU2012-84 
Date of Issue 2013-01-16 (PRMU, MVE) 
ISSN Print edition: ISSN 0913-5685    Online edition: ISSN 2432-6380
Copyright
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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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Conference Information
Committee PRMU MVE IPSJ-CVIM  
Conference Date 2013-01-23 - 2013-01-24 
Place (in Japanese) (See Japanese page) 
Place (in English)  
Topics (in Japanese) (See Japanese page) 
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Paper Information
Registration To PRMU 
Conference Code 2013-01-PRMU-MVE-CVIM 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Face model creation based on simultaneous execution of hierarchical training-set clustering and common local feature extraction 
Sub Title (in English)  
Keyword(1) Multiple Instance Learning  
Keyword(2) Diverse Density  
Keyword(3) hierarchical clustering  
Keyword(4) common image features  
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1st Author's Name Takayuki Fukui  
1st Author's Affiliation Wakayama University (Wakayama Univ.)
2nd Author's Name Toshikazu Wada  
2nd Author's Affiliation Wakayama University (Wakayama Univ.)
3rd Author's Name Hiroshi Oike  
3rd Author's Affiliation Wakayama University (Wakayama Univ.)
4th Author's Name Jun Sakata  
4th Author's Affiliation Wakayama University (Wakayama Univ.)
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Speaker Author-1 
Date Time 2013-01-23 09:30:00 
Presentation Time 30 minutes 
Registration for PRMU 
Paper # PRMU2012-84, MVE2012-49 
Volume (vol) vol.112 
Number (no) no.385(PRMU), no.386(MVE) 
Page pp.23-28 
#Pages
Date of Issue 2013-01-16 (PRMU, MVE) 


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