Covariate and Co-Structural Influences on Human Facial Morphology: Decoding the Structural Blueprint Behind Facial Shape

Abstract

The human face is one of the most intricate yet informative structures, serving as a key identifier in forensic investigations, an indicator of medical conditions, and a crucial factor in surgical planning. Over the past few decades, significant effort has been dedicated to understanding the genetic architecture underlying facial morphology. However, this focus often overlooks the substantial influence of covariates, such as biogeographic ancestry, and structural components like the skull. While these factors are acknowledged, their anthropological is frequently reduced to statistical models that bypass anatomical considerations. Furthermore, many of the complex models developed to reconstruct facial shape are not yet practically applicable. This dissertation addresses these gaps by investigating how regional, rather than just global, biogeographic ancestry influences facial morphology and whether genetic models of biogeographic ancestry align with phenotypic expression. Our findings indicate that broad categorizations such as “European” do not fully capture ancestral variation, yet incorporating too many genetic principal components risks overcorrection. To address this, we introduce a novel standardized, phenotype-based approach using consensus faces. Additionally, we present a validated, standardized method for efficiently masking and analyzing the human skull using over 6,000 quasi-landmarks. This methodology is further expanded to include a facial mask, where both the skull and face are intrinsically linked through anatomically corresponding quasi-landmarks. This innovation enables the simultaneous study of facial soft tissue thickness (FSTT), cranial shape, and facial morphology in a computationally efficient manner that has not been previously achieved. The use of correspondence masks permits modeling of the relationship between the skull and face, facilitating craniofacial reconstruction and laying the foundation for an open-source FSTT and facial measurement database. Ultimately, this dissertation explores standardization, global applicability, with the aim of facilitating real-world applications of a scientifically transparent computational approach to facial image projection from skeletal remains. By integrating genetic, anthropological, and statistical approaches, it describes a streamlined methodology that can harness structural knowledge of facial variation to develop practical tools useful in forensic and medical applications. Moreover, it highlights the need for global large-scale collaborative research to further advance this field on both fundamental science and applied levels.