A Comparison of 3D Facial Features in a Population from Zimbabwe and United States. E Bhaskar, CH Kau.

Date: February 2020. Source: European Journal of Dentistry, 14(01): 100-106, DOI: 10.1055/s-0040-1702258. Objectives: The purpose of this study was to determine the differences in three-dimensional (3D) facial features in a population from Zimbabwe and the United States. In addition, this study seeks to establish an average facial template of each population allowing clinicians to treat…

A Cross-Sectional Study to Understand 3D Facial Differences in a Population of African Americans and Caucasians. CH Kau, J Wang, M Davis.

Date: December 2019. Source: European Journal of Dentistry 2019; 13(04): 485-496 DOI: 10.1055/s-0039-3400551. Objective: The purpose of this cross-sectional retrospective study was to use three-dimensional surface imaging to determine gender dimorphism and facial morphological changes from adolescence to adulthood in African American and Caucasian populations. Materials and Methods: Three-dimensional images were captured and the total…

Quantifying normal head form and craniofacial asymmetry of elementary school students in Taiwan. C-K Hsu, RR Hallac, R Denadai et al.

Date: December 2019. Source: Journal of Plastic, Reconstructive & Aesthetic Surgery, Volume 72, Issue 12, Pages 2033-2040. Background: Defining three-dimensional (3D) normal craniofacial morphology in healthy children could provide craniofacial surgeons a reference point to assess disease, plan surgical reconstruction, and evaluate treatment outcome. The purposes of this study were to report normal craniofacial form…

MeshMonk: Open-source large-scale intensive 3D phenotyping. JD White, A Ortega-Castrillón, H Matthews et al.

Date: April 2019. Source: Scientific Reports 9, 6085. https://doi.org/10.1038/s41598-019-42533-y. Abstract: Dense surface registration, commonly used in computer science, could aid the biological sciences in accurate and comprehensive quantification of biological phenotypes. However, few toolboxes exist that are openly available, non-expert friendly, and validated in a way relevant to biologists. Here, we report a customizable toolbox…

MeshGAN: Non-linear 3D Morphable Models of Faces. S Cheng, M Bronstein, Y Zhou, I Kotsia, M Pantic, S Zafeiriou.

Date: April 2019. Source: Cornell University Library – arXiv.org, Computer Vision and Pattern Recognition. Abstract: Generative Adversarial Networks (GANs) are currently the method of choice for generating visual data. Certain GAN architectures and training methods have demonstrated exceptional performance in generating realistic synthetic images (in particular, of human faces). However, for 3D object, GANs still…

Thesis. Craniofacial morphology and the use of neonatal non-invasive ventilation therapy. Don He.

Date: August 2018. Source: Thesis. University of British Columbia Library. Craniofacial Science, Dentistry, Faculty of University of British Columbia. Objective: A prospective cohort study with the overall objective to characterize the three-dimensional facial morphology of preterm infants over the course of the first 18 months of corrected age, participating in the Neonatal Follow-up Program of…

Estimating age and synthesising growth in children and adolescents using 3D facial prototypes. H Matthews, A Penington, J Clement et al.

Date: May 2018. Source: Forensic Science International, Volume 286, pp 61-69. Highlights: • Describes a framework for age estimation and growth prediction from 3D photographs. • Accuracy of both approaches is assessed. • This work can facilitate person identification and building 3D facial composites. Abstract: 3D facial images are becoming increasingly common. They provide more…