# Optimized facial landmark modeling with medical aesthetic constraints by a multi-objective genetic algorithm

## TL;DR

This paper introduces a new method for facial beauty assessment using medical aesthetics and a genetic algorithm, achieving high accuracy and clinical relevance.

## Contribution

A novel multi-objective genetic algorithm with aesthetic-driven initialization and a new facial landmark model for precise beauty scoring.

## Key findings

- The proposed method achieved a Pearson’s correlation coefficient of 0.8216 on facial beauty datasets.
- The model outperformed existing methods with lower mean absolute and root mean square errors.
- The selected features align with professional plastic surgeons' judgments, ensuring clinical relevance.

## Abstract

“Facial Beauty” is not an absolute physical attribute but a subjective social and cultural construct. Facial beauty assessment is an interdisciplinary field that integrates computer vision and medical aesthetics (MAs) to quantify personal judgment regarding facial attractiveness. In this study, the beauty assessment we adopted was based on the scores given by plastic surgeons; this method is more professional and is supported by a theoretical basis. We derived a set of MA features that encompass global traits, local details, and curvature aspects from established aesthetic principles. Incorporating these features enhances predictive accuracy in facial beauty. Furthermore, we propose a feature selection algorithm with aesthetic-driven initialization embedded in a multi-objective evolutionary framework. Additionally, we introduce an MA facial landmark model that provides explicit annotation of bilateral zygomatic, orbital, and nasal points for precise attractiveness scoring. Experimental results on the South China University of Technology-Facial Beauty Perception (SCUT-FBP) and SCUT-FBP5500 datasets and the Chicago Face Dataset demonstrate superior performance (Pearson’s correlation coefficient = 0.8216, mean absolute error = 0.2638, and root mean square error = 0.3743) over state-of-the-art methods, validating its clinical relevance. This study provides a practical tool for beauty evaluation, where the selected features align with professional judgments, enabling transparent and explainable outcomes in both clinical and cosmetic applications.