ArticleScientific reports2023
A computational approach to investigating facial attractiveness factors using geometric morphometric analysis and deep learning.
Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Validation of a Machine Learning-Derived Algorithm for the Measurement of Facial First Impressions.Aesthetic plastic surgery · 2026Article
- Artificial Intelligence in Non-Surgical Cosmetic Procedures: A Multi-Stakeholder Revolution.Aesthetic plastic surgery · 2026Review
- The differences in essential facial areas for impressions between humans and deep learning models: An eye-tracking and explainable AI approach.British journal of psychology (London, England : 1953) · 2026Article
- Self-reported social media use does not affect cross-cultural consensus in first impressions.Evolutionary human sciences · 2026Article
- Influence of facial pattern on the aesthetic assessment of the inclination of the occlusal plane in the frontal view.Clinical oral investigations · 2025Article
- Masked autoencoder of multi-scale convolution strategy combined with knowledge distillation for facial beauty prediction.Scientific reports · 2025Article
- Truth Lies in the Depths: Novel Insights into Facial Aesthetic Measurements from a U.S. Survey Panel.Aesthetic plastic surgery · 2024Article
- Impact of orthodontic-induced facial morphology changes on aesthetic evaluation: a retrospective study.BMC oral health · 2024Article
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Authors and funding
2 authors.
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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.
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