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Paper Abstract and Keywords
Presentation 2018-11-01 11:00
Emotion Extraction from Face Images and its Quantification/Visualization -- Experimental Evaluation in Music Concerts and Rakugo Performance --
Seima Todo, Tetsuo Tsujioka, Kazunobu Okazaki, Akihiro Odanaka (Osaka City Univ.) CS2018-63
Abstract (in Japanese) (See Japanese page) 
(in English) Nowadays, machine learning, especially a deep learning technique for image classification, has been one of attractive research areas. Emotion extraction from human face image also has numch attention as one of applications of the deep learning technique. In our research, for the purpose of application of deep learning technique to art (thus, fusion of art and engineering), we have tried to make emotion extraction from face images of audience in music concerts and rakugo performance. Experimental evaluations have been practically made with 28-30 subjects, in classic music, gamelan music, improvising misic and rakugo (comic story telling) performance. The video of all the subjects' faces has been recorded, the face images has been cliped from the video every one seconds, and quantification of emotion has been made by inputing them to a deep learning engine. In the experiment, heart rate also has been measured for all the subjects simultaneously, for analysis of state of stress / relax based on LF/HF evaluation. For visualization, we have created the experimental results by colored face animation videos, synchronously together with the performer's video, and then emotion transitions and stress / relax state transtions. In additional analysis of correlation of emotion of happiness between the subjects, it is cleared that similarity of emotion between subjects can be quantitatively-expressible and detectable.
Keyword (in Japanese) (See Japanese page) 
(in English) Machine learning / Deep learning / Face image / Emotion extraction / Fusion of art and engineering / Music concert / Comic story telling /  
Reference Info. IEICE Tech. Rep., vol. 118, no. 275, CS2018-63, pp. 43-49, Nov. 2018.
Paper # CS2018-63 
Date of Issue 2018-10-25 (CS) 
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)
Notes on Review This article is a technical report without peer review, and its polished version will be published elsewhere.
Download PDF CS2018-63

Conference Information
Committee CS  
Conference Date 2018-11-01 - 2018-11-02 
Place (in Japanese) (See Japanese page) 
Place (in English) The Shiki Museum 
Topics (in Japanese) (See Japanese page) 
Topics (in English) Broadband Access Systems, Home Networks, Network Services, Applications for Communications, etc. 
Paper Information
Registration To CS 
Conference Code 2018-11-CS 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Emotion Extraction from Face Images and its Quantification/Visualization 
Sub Title (in English) Experimental Evaluation in Music Concerts and Rakugo Performance 
Keyword(1) Machine learning  
Keyword(2) Deep learning  
Keyword(3) Face image  
Keyword(4) Emotion extraction  
Keyword(5) Fusion of art and engineering  
Keyword(6) Music concert  
Keyword(7) Comic story telling  
Keyword(8)  
1st Author's Name Seima Todo  
1st Author's Affiliation Osaka City University (Osaka City Univ.)
2nd Author's Name Tetsuo Tsujioka  
2nd Author's Affiliation Osaka City University (Osaka City Univ.)
3rd Author's Name Kazunobu Okazaki  
3rd Author's Affiliation Osaka City University (Osaka City Univ.)
4th Author's Name Akihiro Odanaka  
4th Author's Affiliation Osaka City University (Osaka City Univ.)
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Speaker Author-1 
Date Time 2018-11-01 11:00:00 
Presentation Time 20 minutes 
Registration for CS 
Paper # CS2018-63 
Volume (vol) vol.118 
Number (no) no.275 
Page pp.43-49 
#Pages
Date of Issue 2018-10-25 (CS) 


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