Fast 3D Face Reconstruction from a Single Image Using Different Deep Learning Approaches for Facial Palsy Patients

Autor: Duc-Phong Nguyen, Tan-Nhu Nguyen, Stéphanie Dakpé, Marie-Christine Ho Ba Tho, Tien-Tuan Dao
Přispěvatelé: Biomécanique et Bioingénierie (BMBI), Université de Technologie de Compiègne (UTC)-Centre National de la Recherche Scientifique (CNRS), Ho Chi Minh City University of Technology and Education, Vietnam., CHirurgie, IMagerie et REgénération tissulaire de l’extrémité céphalique - Caractérisation morphologique et fonctionnelle - UR UPJV 7516 (CHIMERE), Université de Picardie Jules Verne (UPJV), CHU Amiens-Picardie, Laboratoire de Mécanique, Multiphysique, Multiéchelle - UMR 9013 (LaMcube), Centrale Lille-Université de Lille-Centre National de la Recherche Scientifique (CNRS)
Jazyk: angličtina
Rok vydání: 2022
Předmět:
Zdroj: Bioengineering
Bioengineering, 2022, 9 (11), pp.619. ⟨10.3390/bioengineering9110619⟩
Bioengineering; Volume 9; Issue 11; Pages: 619
ISSN: 2306-5354
Popis: The 3D reconstruction of an accurate face model is essential for delivering reliable feedback for clinical decision support. Medical imaging and specific depth sensors are accurate but not suitable for an easy-to-use and portable tool. The recent development of deep learning (DL) models opens new challenges for 3D shape reconstruction from a single image. However, the 3D face shape reconstruction of facial palsy patients is still a challenge, and this has not been investigated. The contribution of the present study is to apply these state-of-the-art methods to reconstruct the 3D face shape models of facial palsy patients in natural and mimic postures from one single image. Three different methods (3D Basel Morphable model and two 3D Deep Pre-trained models) were applied to the dataset of two healthy subjects and two facial palsy patients. The reconstructed outcomes were compared to the 3D shapes reconstructed using Kinect-driven and MRI-based information. As a result, the best mean error of the reconstructed face according to the Kinect-driven reconstructed shape is 1.5±1.1mm. The best error range is 1.9±1.4mm when compared to the MRI-based shapes. Before using the procedure to reconstruct the 3D faces of patients with facial palsy or other facial disorders, several ideas for increasing the accuracy of the reconstruction can be discussed based on the results. This present study opens new avenues for the fast reconstruction of the 3D face shapes of facial palsy patients from a single image. As perspectives, the best DL method will be implemented into our computer-aided decision support system for facial disorders.
Databáze: OpenAIRE
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