DeepGeoFusion: personalized facial beauty prediction through geometric-visual fusion

Introduction Personalized 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. Methods We 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. Results Extensive 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. Discussion DeepGeoFusion 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.