| Presentation | 2022-10-18 Examination of facial attractiveness features using geometric morphological analysis of and deep learning methods. Takanori Sano, Hideaki Kawabata, |
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| PDF Download Page |  PDF download Page Link |
| Abstract(in Japanese) | (See Japanese page) |
| Abstract(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) |
| Keyword(in English) | Facial attractiveness / Geometric morphometrics / Deep learning / CNN / Grad-CAM |
| Paper # | HIP2022-52 |
| Date of Issue | 2022-10-10 (HIP) |
| Conference Information | |
| Committee | HIP |
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| Conference Date | 2022/10/17(2days) |
| 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. |
| Chair | Yuji Wada(Ritsumeikan Univ.) |
| Vice Chair | Hiroyuki Umemoto(AIST) / Sachiko Kiyokawa(Nagoya Univ.) |
| Secretary | Hiroyuki Umemoto(Kyushu Univ.) / Sachiko Kiyokawa(NICT) |
| Assistant | Ippei Negishi(Kanazawa Inst. of Tech.) / Daisuke Tanaka(Tottori Univ.) |
| Paper Information | |
| Registration To | Technical Committee on Human Information Processing |
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| Language | JPN |
| 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 |
| 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.) |
| Date | 2022-10-18 |
| Paper # | HIP2022-52 |
| Volume (vol) | vol.122 |
| Number (no) | HIP-213 |
| Page | pp.pp.26-31(HIP), |
| #Pages | 6 |
| Date of Issue | 2022-10-10 (HIP) |