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| 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 |
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| 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 |
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| 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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| 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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