AUTHOR=Wang Kunwei , Li Yanzhi , Huang Dong , Feng Junmei , Feng Xiaoyi 
  
TITLE=DeepGeoFusion: personalized facial beauty prediction through geometric-visual fusion
  
JOURNAL=Frontiers in Computer Science
  
VOLUME=Volume 7 - 2025
  
YEAR=2026
  
URL=https://www.frontiersin.org/journals/computer-science/articles/10.3389/fcomp.2025.1692523
  
DOI=10.3389/fcomp.2025.1692523
  
ISSN=2624-9898
  
ABSTRACT=IntroductionPersonalized facial beauty prediction is a critical advancement beyond population-level models with transformative applications in aesthetic surgery planning and user-centric recommendation systems, while contemporary methods face limitations in modeling aesthetically sensitive facial regions, fusing heterogeneous geometric and visual features, and reducing extensive annotation dependency for personalization.MethodsWe propose DeepGeoFusion, a novel framework that synergizes Vision Mamba-extracted global visual features with anatomically constrained facial graphs (constructed from 86 landmarks via Delaunay triangulation), using the Graph Node Attention Projection Fusion (GNAPF) block for cross-modal alignment and a lightweight adaptation mechanism to generate personalized preference vectors from 10 seed images via confidence-gated optimization.ResultsExtensive experiments on SCUT-FBP5500 demonstrate statistically significant improvements in personalized prediction accuracy and robust performance across genders and ethnicities compared to state-of-the-art methods.DiscussionDeepGeoFusion effectively addresses key limitations of existing methods by integrating complementary geometric and visual features, enabling efficient personalization with minimal annotation and highlighting practical value for aesthetic-related applications requiring personalized assessments.