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.