A computational approach to investigating facial attractiveness factors using geometric morphometric analysis and deep learning
Abstract Numerous studies discuss the features that constitute facial attractiveness. In recent years, computational research has received attention because it can examine facial features without relying on prior research hypotheses. This approach uses many face stimuli and models the relationship b...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2023-11-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-023-47084-x |
| _version_ | 1797576610815148032 |
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| author | Takanori Sano Hideaki Kawabata |
| author_facet | Takanori Sano Hideaki Kawabata |
| author_sort | Takanori Sano |
| collection | DOAJ |
| description | Abstract Numerous studies discuss the features that constitute facial attractiveness. In recent years, computational research has received attention because it can examine facial features without relying on prior research hypotheses. This approach uses many face stimuli and models the relationship between physical facial features and attractiveness using methods such as geometric morphometrics and deep learning. However, studies using each method have been conducted independently and have technical and data-related limitations. It is also difficult to identify the factors of actual attractiveness perception using only computational methods. In this study, we examined morphometric features important for attractiveness perception through geometric morphometrics and impression evaluation. Furthermore, we used deep learning to analyze important facial features comprehensively. The results showed that eye-related areas are essential in determining attractiveness and that different racial groups contribute differently to the impact of shape and skin information on attractiveness. The approach used in this study will contribute toward understanding facial attractiveness features that are universal and diverse, extending psychological findings and engineering applications. |
| first_indexed | 2024-03-10T21:55:22Z |
| format | Article |
| id | doaj.art-3bc92a3fec1c4565b3b4c00d8350c998 |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-03-10T21:55:22Z |
| publishDate | 2023-11-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-3bc92a3fec1c4565b3b4c00d8350c9982023-11-19T13:08:02ZengNature PortfolioScientific Reports2045-23222023-11-0113111210.1038/s41598-023-47084-xA computational approach to investigating facial attractiveness factors using geometric morphometric analysis and deep learningTakanori Sano0Hideaki Kawabata1Graduate School of Sociology, Keio UniversityGraduate School of Sociology, Keio UniversityAbstract Numerous studies discuss the features that constitute facial attractiveness. In recent years, computational research has received attention because it can examine facial features without relying on prior research hypotheses. This approach uses many face stimuli and models the relationship between physical facial features and attractiveness using methods such as geometric morphometrics and deep learning. However, studies using each method have been conducted independently and have technical and data-related limitations. It is also difficult to identify the factors of actual attractiveness perception using only computational methods. In this study, we examined morphometric features important for attractiveness perception through geometric morphometrics and impression evaluation. Furthermore, we used deep learning to analyze important facial features comprehensively. The results showed that eye-related areas are essential in determining attractiveness and that different racial groups contribute differently to the impact of shape and skin information on attractiveness. The approach used in this study will contribute toward understanding facial attractiveness features that are universal and diverse, extending psychological findings and engineering applications.https://doi.org/10.1038/s41598-023-47084-x |
| spellingShingle | Takanori Sano Hideaki Kawabata A computational approach to investigating facial attractiveness factors using geometric morphometric analysis and deep learning Scientific Reports |
| title | A computational approach to investigating facial attractiveness factors using geometric morphometric analysis and deep learning |
| title_full | A computational approach to investigating facial attractiveness factors using geometric morphometric analysis and deep learning |
| title_fullStr | A computational approach to investigating facial attractiveness factors using geometric morphometric analysis and deep learning |
| title_full_unstemmed | A computational approach to investigating facial attractiveness factors using geometric morphometric analysis and deep learning |
| title_short | A computational approach to investigating facial attractiveness factors using geometric morphometric analysis and deep learning |
| title_sort | computational approach to investigating facial attractiveness factors using geometric morphometric analysis and deep learning |
| url | https://doi.org/10.1038/s41598-023-47084-x |
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