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Paper Abstract and Keywords
Presentation 2022-10-18 10:50
Examination of facial attractiveness features using geometric morphological analysis of and deep learning methods.
Takanori Sano, Hideaki Kawabata (Keio Univ.) HIP2022-52
Abstract (in Japanese) (See Japanese page) 
(in English) In psychology, various studies have been conducted on the features that constitute facial attractiveness. In recent years, studies that computationally model the relationship between facial features and perceived attractiveness have attracted much attention. The methods have used geometric morphometrics and deep learning methods. However, each method has been studied independently, and there has yet to be integrated research that considers both models. In this study, Therefore, we combined each method to examine the details of facial attractiveness features. The results showed that the eyebrow region tends to be related to attractiveness in male images regardless of which method is used, which is consistent with findings in psychology. This approach is expected to contribute to understanding highly universal facial attractiveness features and extend psychological results and engineering applications.
Keyword (in Japanese) (See Japanese page) 
(in English) Facial attractiveness / Geometric morphometrics / Deep learning / CNN / Grad-CAM / / /  
Reference Info. IEICE Tech. Rep., vol. 122, no. 213, HIP2022-52, pp. 26-31, Oct. 2022.
Paper # HIP2022-52 
Date of Issue 2022-10-10 (HIP) 
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)
Download PDF HIP2022-52

Conference Information
Committee HIP  
Conference Date 2022-10-17 - 2022-10-18 
Place (in Japanese) (See Japanese page) 
Place (in English) Kyoto Terrsa 
Topics (in Japanese) (See Japanese page) 
Topics (in English) Eye Movement (including Accommodation and Pupil), Spatial Perception (Depth Perception, Motion Perception, etc.), etc. 
Paper Information
Registration To HIP 
Conference Code 2022-10-HIP 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Examination of facial attractiveness features using geometric morphological analysis of and deep learning methods. 
Sub Title (in English)  
Keyword(1) Facial attractiveness  
Keyword(2) Geometric morphometrics  
Keyword(3) Deep learning  
Keyword(4) CNN  
Keyword(5) Grad-CAM  
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Keyword(8)  
1st Author's Name Takanori Sano  
1st Author's Affiliation Keio University (Keio Univ.)
2nd Author's Name Hideaki Kawabata  
2nd Author's Affiliation Keio University (Keio Univ.)
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Speaker Author-1 
Date Time 2022-10-18 10:50:00 
Presentation Time 25 minutes 
Registration for HIP 
Paper # HIP2022-52 
Volume (vol) vol.122 
Number (no) no.213 
Page pp.26-31 
#Pages 6 
Date of Issue 2022-10-10 (HIP) 

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