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Computational models of facial attractiveness judgments
P Matthew Bronstad1, Judith H Langlois, Richard Russell
1Department of Psychology, University of Texas at Austin, Austin, TX 78712, USA. matthew.bronstad@schepens.harvard.edu
Perception
|April 11, 2008
Summary
Two computational models accurately predicted human facial attractiveness ratings. The models identified averageness and sexual dimorphism as key factors, but not facial symmetry, in attractiveness judgments.
Area of Science:
- Psychology
- Computer Science
- Biometrics
Background:
- Human facial attractiveness is a complex trait influenced by various factors.
- Previous models often relied on manual measurements and lacked robust validation.
- Understanding the computational basis of attractiveness perception is crucial.
Purpose of the Study:
- To develop and validate computational models for predicting human facial attractiveness ratings.
- To identify key visual features contributing to perceived facial attractiveness.
- To compare the efficacy of different modeling approaches.
Main Methods:
- Designed two computational models: one using partial least squares (PLS) on image data, and another using principal component analysis (PCA) and a perceptron on feature measurements.
- Trained models using facial images and corresponding human attractiveness ratings.
- Evaluated model performance against human judgments.
Main Results:
- Both computational models achieved predictions indistinguishable from human attractiveness ratings.
- The PLS model identified image factors corresponding to facial averageness and sexual dimorphism.
- Facial symmetry was not a significant factor in either model or human judgments.
Conclusions:
- Computational models can effectively replicate human judgments of facial attractiveness.
- Averageness and sexual dimorphism are key determinants of facial attractiveness, supporting existing hypotheses.
- Facial symmetry does not appear to be a significant factor in human attractiveness perception.
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