Analysis of 3D Facial Dysmorphology in Genetic Syndromes from Unconstrained 2D Photographs

The quantification of facial dysmorphology is essential for the detection and diagnosis of genetic conditions. Facial analysis benefits from 3D image data, but 2D photography is more widely available at clinics. The aim of this paper is to analyze 3D facial dysmorphology using unconstrained (uncalibrated) 2D pictures at three orientations: frontal, left and right profiles. We estimate a unified 3D face shape by fitting a 3D morphable model (3DMM) to all the images by minimizing the differences between the 2D projected position of the selected 3D vertices in the 3DMM and their corresponding position in the 2D pictures. Using the estimated 3D face shape, we compute a set of facial dysmorphology measurements and train a classifier to identify genetic syndromes. Evaluated on a set of 48 subjects with and without genetic conditions, our method reduced the landmark detection errors obtained by using a single photograph by 44%, 48%, and 49% on the frontal photograph, left profile, and right profile, respectively. We achieved a point-to-point projection error of 1.98 ± 0.38% normalized to the size of face, significantly improving (p ≤ 0.01) the error obtained with state-of-the-art methods of 4.17 ± 2.83%. In addition, the geometric features calculated from the 3D reconstructed face obtained an accuracy of 73% in the detection of facial dysmorphology associated to genetic syndromes, compared with the error of 58% using state-of-the-art methods from 2D pictures. That accuracy increased to 96% when we included local texture information. Our results demonstrate the potential of this framework to assist in the earlier and remote detection of genetic syndromes throughout the world.