An extended geometric and morphometric framework for assessing low-cost 3D facial reconstruction and acquisition methods

Abstract Three-dimensional (3D) facial shape analysis plays an increasingly important role in biomedical and clinical research. However, the high cost and limited accessibility of advanced 3D acquisition systems constrain their widespread use, fostering the development of low-cost alternatives for facial model acquisition and reconstruction. This study presents a comprehensive evaluation methodology that integrates geometric accuracy metrics with landmark-based Geometric Morphometrics, providing a statistically robust and anatomically meaningful framework for assessing facial morphology preservation. The proposed methodology was validated through a comparative study using high-resolution stereophotogrammetry (SPG) as the gold standard, a smartphone-based infrared structured light scanner, and state-of-the-art deep learning approaches for 3D reconstruction from 2D images. Quantitative and morphometric results demonstrated that smartphone-based scans achieved the highest geometric and morphological fidelity, with over 80% of surface points within 1 mm of the SPG reference. These findings confirm the potential of low-cost scanning devices for accurate and reproducible 3D facial analysis, while highlighting the value of the proposed framework as a standardized tool for validating and guiding the development of clinically reliable facial reconstruction technologies.