2026-04-13T02:11:11Zhttps://recercat.cat/oai/request

oai:recercat.cat:10230/729462026-04-08T14:49:33Zcom_2072_6col_2072_452952

BabyFM: towards accurate 3D baby facial models using spectral decomposition and asymmetry swapping Morales Muñoz, Maria Araceli Alomar Adrover, Antònia Porras Pérez, Antonio Reyes Linguraru, Marius George Piella Fenoy, Gemma Sukno, Federico Mateo 3D morphable model Statistical shape model Baby facial shape Spectral correspondences Asymmetry swapping In this paper, we present the first publicly available 3D statistical facial shape model of babies, the Baby Face Model (BabyFM). Constructing a model of the facial geometry of babies entails specific challenges, such as occlusions, extreme and uncontrollable expressions, and data shortage. We address these challenges by proposing (1) a non-template dependent method that jointly estimates a 3D facial baby-specific template and the point-to-point correspondences; (2) a novel method to establish correspondences based on the spectral decomposition of the Laplace Beltrami Operator, which provides a more robust theoretical foundation than state-of-the-art methods; and (3) an asymmetry-swapping strategy to alleviate the shortage of large scale datasets by decoupling the identity-related and the asymmetry-related shape deformation fields. The latter leads to a data augmentation technique that we integrate within the Gaussian Process Morphable Model framework, providing a simple way of combining synthetic or sample covariance functions. We exhaustively evaluate each stage of our method and demonstrate that (1) when aiming at the 3D facial geometry of a baby, a specific model of babies is needed, since the pre-built publicly available models constructed with adults or older children are not able to accurately represent the facial shape of babies; (2) our spectral approach improves correspondences accuracy with respect to state-of-the-art-methods; and (3) the proposed data augmentation technique enhances the robustness of the BabyFM. This work is partly supported by MICIU/AEI/10.13039/501100011033/ under the project grant PID2020-114083GB-I00 and PRE2021-097544 scholarship, the ICREA Academia programme and the NIH Eunice Kennedy Shriver National Institute of Child Health & Human Development grant R42 HD08171203. 2026-03-31T08:30:00Z 2026-03-31T08:30:00Z 2025 2026-03-31T08:29:59Z info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Computers in Biology and Medicine. 2025 Mar;186:109652 info:eu-repo/grantAgreement/ES/2PE/PID2020-114083GB-I00 © 2025 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). http://creativecommons.org/licenses/by-nc-nd/4.0/ info:eu-repo/semantics/openAccess Elsevier