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The human face is a complex trait under strong genetic control, as evidenced by the striking visual similarity between twins. Nevertheless, heritability estimates of facial traits have often been surprisingly low or difficult to replicate, while the construction of facial phenotypes that correspond to naturally perceived facial features remains largely a mystery.

We performed a large-scale heritability study of face geometry that aims to address these issues, using high-resolution, three-dimensional facial models on a cohort of 952 twins recruited from the TwinsUK registry, and processed through a novel landmarking workflow, GESSA (Geodesic Ensemble Surface Sampling Algorithm). The algorithm places thousands of landmarks throughout the facial surface and automatically establishes point-wise correspondence across faces. These landmarks enabled us to intuitively characterize facial geometry at a fine level of detail through four different curvature indices, yielding accurate heritability maps of the human face.

The interactive 3D viewer allows detailed exploration of our findings. Four curvature maps of the mean face in our dataset can be visualized. They showcase the different attributes of the curvature indices used to extract the facial phenotypes. The respective heritability maps are intuitive graphical representations of the extent to which facial geometry is controlled by genetic variability.

The details of our phenotyping methodology and the biological findings can be found in the reference article:

Tsagkrasoulis Dimosthenis, Hysi Pirro, Spector Tim and Montana Giovanni. Heritability maps of human face morphology through large-scale automated three-dimensional phenotyping. Scientific Reports. 2017, 7, 45885.