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Evolving attractive faces using morphing technology and a genetic algorithm: a new approach to determining ideal
Brian J F Wong1, Koohyar Karimi, Zlatko Devcic
1Department of Otolaryngology-Head and Neck Surgery, University of California Irvine, Orange, California 92812, USA. bjwong@uci.edu
The Laryngoscope
|April 11, 2008
Summary
This study used a genetic algorithm and morphing software to evolve more attractive synthetic faces, successfully mimicking natural selection to identify key facial attributes. The findings offer a novel approach to understanding facial aesthetics.
Area of Science:
- Computer-aided facial analysis
- Evolutionary algorithms in aesthetics
- Digital morphometrics
Background:
- Facial attractiveness is subjective and difficult to quantify.
- Traditional methods for identifying attractive facial features are limited.
- Computational approaches offer new possibilities for aesthetic research.
Purpose of the Study:
- To determine if a genetic algorithm and morphing software can evolve more attractive synthetic faces.
- To evaluate this computational approach as a tool for defining ideal facial attributes.
- To identify specific facial morphometric features correlated with perceived attractiveness.
Main Methods:
- A genetic algorithm iteratively morphed digital images of female faces based on attractiveness scores from focus groups.
- Four generations of synthetic faces were evolved using selection pressure based on attractiveness.
- Morphometric measurements of 150 synthetic faces were analyzed and correlated with attractiveness scores.
Main Results:
- Average attractiveness scores significantly increased with each generation.
- Key features correlated with attractiveness included nasal width, eyebrow arch height, and lip thickness.
- Evolved faces approximated classical aesthetic canons, with oval shapes, arched eyebrows, and full lips predominating.
Conclusions:
- Genetic algorithms and morphing software can successfully evolve attractive synthetic faces.
- This computational method effectively mimics the evolution of facial attractiveness.
- The approach provides a robust alternative to traditional methods for aesthetic analysis.
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