ORIGINAL RESEARCH article
Front. Comput. Neurosci.
This article is part of the Research TopicExploring Precision Medicine in Reconstructive and Aesthetic SurgeryView all 12 articles
Optimized Facial Landmark Modeling with Medical Aesthetic Constraints by a Multi-Objective Genetic Algorithm
Provisionally accepted
- 1City University of Macau, Taipa, Macao, SAR China
- 2Guangdong Women and Children Hospital, Guangzhou, China
Select one of your emails
You have multiple emails registered with Frontiers:
Notify me on publication
Please enter your email address:
If you already have an account, please login
You don't have a Frontiers account ? You can register here
"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 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 correlation coefficient = 0.8216, mean absolute error = 0.2638, root mean square error = 0.3743) over state-of-the-art methods, validating its clinical relevance. This work 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. Keywords: facial beauty assessment, genetic algorithm, machine learning, medical aesthetics, performance evaluation.
Keywords: Facial beauty assessment, Genetic Algorithm, machine learning, Medical aesthetics, performance evaluation
Received: 14 Sep 2025; Accepted: 04 Feb 2026.
Copyright: © 2026 Yan, Wen, Tan and Ye. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Yuan Ye
Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.