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Protocol for Data Collection and Analysis Applied to Automated Facial Expression Analysis Technology and Temporal Analysis for Sensory Evaluation
Published on: August 26, 2016
A statistical model of facial attractiveness
Christopher P Said1, Alexander Todorov
1Psychology Department, New York University, New York, NY 10003, USA. chris.said@nyu.edu
Psychological Science
|August 20, 2011
Summary
Facial attractiveness is better predicted by a multidimensional face space model than by facial averageness or sexual dimorphism. This new model reveals complex attractiveness components, especially for male faces.
Area of Science:
- Psychology
- Computer Vision
- Human Perception
Background:
- Facial attractiveness research traditionally focuses on facial averageness and sexual dimorphism.
- These factors, while parsimonious, explain limited variance in perceived attractiveness, particularly for male faces.
Purpose of the Study:
- To develop and validate a more predictive model of facial attractiveness.
- To identify novel components contributing to facial attractiveness beyond averageness and sexual dimorphism.
Main Methods:
- A regression model was constructed defining attractiveness based on a face's position within a multidimensional face space.
- The predictive power of this model was compared against traditional averageness and sexual dimorphism accounts.
Main Results:
- The multidimensional face space model significantly outperformed averageness and sexual dimorphism in predicting facial attractiveness.
- The model identified previously unreported dimensions influencing attractiveness and resolved conflicting findings regarding sexual dimorphism's effect on male facial attractiveness.
Conclusions:
- A multidimensional face space approach offers a more comprehensive understanding of facial attractiveness.
- This model provides greater predictive power and reveals nuanced attractiveness factors, advancing the field beyond simpler metrics.
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