Reconstruction of three-dimensional facial geometric features related to fetal alcohol syndrome using adult surrogates

• Main Author: Atuhaire, Felix
• Other Authors: Mutsvangwa, Tinashe
• Format: Dissertation
• Language: English
• Published: Faculty of Health Sciences 2021
• Subjects: Biomedical Engineering
• Online Access: http://hdl.handle.net/11427/32579

Fetal alcohol syndrome (FAS) is a condition caused by prenatal alcohol exposure. The diagnosis of FAS is based on the presence of central nervous system impairments, evidence of growth abnormalities and abnormal facial features. Direct anthropometry has traditionally been used to obtain facial data to assess the FAS facial features. Research efforts have focused on indirect anthropometry such as 3D surface imaging systems to collect facial data for facial analysis. However, 3D surface imaging systems are costly. As an alternative, approaches for 3D reconstruction from a single 2D image of the face using a 3D morphable model (3DMM) were explored in this research study. The research project was accomplished in several steps. 3D facial data were obtained from the publicly available BU-3DFE database, developed by the State University of New York. The 3D face scans in the training set were landmarked by different observers. The reliability and precision in selecting 3D landmarks were evaluated. The intraclass correlation coefficients for intra- and inter-observer reliability were greater than 0.95. The average intra-observer error was 0.26 mm and the average inter-observer error was 0.89 mm. A rigid registration was performed on the 3D face scans in the training set. Following rigid registration, a dense point-to-point correspondence across a set of aligned face scans was computed using the Gaussian process model fitting approach. A 3DMM of the face was constructed from the fully registered 3D face scans. The constructed 3DMM of the face was evaluated based on generalization, specificity, and compactness. The quantitative evaluations show that the constructed 3DMM achieves reliable results. 3D face reconstructions from single 2D images were estimated based on the 3DMM. The MetropolisHastings algorithm was used to fit the 3DMM features to 2D image features to generate the 3D face reconstruction. Finally, the geometric accuracy of the reconstructed 3D faces was evaluated based on ground-truth 3D face scans. The average root mean square error for the surface-to-surface comparisons between the reconstructed faces and the ground-truth face scans was 2.99 mm. In conclusion, a framework to estimate 3D face reconstructions from single 2D facial images was developed and the reconstruction errors were evaluated. The geometric accuracy of the 3D face reconstructions was comparable to that found in the literature. However, future work should consider minimizing reconstruction errors to acceptable clinical standards in order for the framework to be useful for 3D-from-2D reconstruction in general, and also for developing FAS applications. Finally, future work should consider estimating a 3D face using multi-view 2D images to increase the information available for 3D-from-2D reconstruction.