TY - GEN
T1 - A Geometric and Morphometric Methodology for Evaluating Low-Cost 3D Facial Acquisition and Reconstruction Techniques
AU - Heredia-Lidón, Álvaro
AU - Moñux-Bernal, Alejandro
AU - González, Alejandro
AU - Echeverry-Quiceno, Luis M.
AU - Andreu-Montoriol, Mireia
AU - Gallardo, Susanna
AU - Casado, Aroa
AU - Esther Esteban, María
AU - Martínez-Abadías, Neus
AU - Sevillano, Xavier
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2026.
PY - 2026
Y1 - 2026
N2 - Three-dimensional (3D) facial shape analysis has gained interest due to its potential clinical applications. However, the high cost of advanced 3D facial acquisition systems limits their widespread use, driving the development of low-cost acquisition and reconstruction methods. This study introduces a novel evaluation methodology that goes beyond traditional geometry-based benchmarks by integrating morphometric shape analysis techniques, providing a statistical framework for assessing facial morphology preservation. As a case study, we compare smartphone-based 3D scans with state-of-the-art deep learning reconstruction methods from 2D images, using high-end stereophotogrammetry models as ground truth. This methodology enables a quantitative assessment of global and local shape differences, offering a biologically meaningful validation approach for low-cost 3D facial acquisition and reconstruction techniques.
AB - Three-dimensional (3D) facial shape analysis has gained interest due to its potential clinical applications. However, the high cost of advanced 3D facial acquisition systems limits their widespread use, driving the development of low-cost acquisition and reconstruction methods. This study introduces a novel evaluation methodology that goes beyond traditional geometry-based benchmarks by integrating morphometric shape analysis techniques, providing a statistical framework for assessing facial morphology preservation. As a case study, we compare smartphone-based 3D scans with state-of-the-art deep learning reconstruction methods from 2D images, using high-end stereophotogrammetry models as ground truth. This methodology enables a quantitative assessment of global and local shape differences, offering a biologically meaningful validation approach for low-cost 3D facial acquisition and reconstruction techniques.
KW - Deep Learning-Based Reconstruction
KW - Facial Shape Analysis
KW - Geometric Morphometrics
KW - Methodology
UR - https://www.scopus.com/pages/publications/105013021752
UR - http://hdl.handle.net/20.500.14342/5497
U2 - 10.1007/978-3-031-99565-1_13
DO - 10.1007/978-3-031-99565-1_13
M3 - Conference contribution
AN - SCOPUS:105013021752
SN - 9783031995644
VL - 15937
T3 - Lecture Notes in Computer Science
SP - 163
EP - 174
BT - Pattern Recognition and Image Analysis - 12th Iberian Conference, IbPRIA 2025, Proceedings
A2 - Gonçalves, Nuno
A2 - Oliveira, Hélder P.
A2 - Sánchez, Joan Andreu
PB - Springer Science and Business Media Deutschland GmbH
T2 - 12th Iberian Conference on Pattern Recognition and Image Analysis, IbPRIA 2025
Y2 - 30 June 2025 through 3 July 2025
ER -