Shape-based quantification and classification of three dimensional face data for craniofacial research

22q11.2DS been shown to be one of the most common multiple anomaly syndromes in humans. Early detection is important as many affected individuals are born with a conotruncal cardiac anomaly, mild-to-moderate immune deficiency and learning disabilities, all of which can benefit from early intervention.;Given a set of labeled 3D training meshes acquired from stereo imaging of heads, the goal of this dissertation is to develop a successful methodology for discriminating between 22q11.2DS affected individuals and the general population and for quantifying the degree of dysmorphology of facial features. Although many approaches for such discrimination exist in the medical and computer vision literature, the goal is to develop methods that focus on 3D shape of both the face as a whole and specific local features.;The main contributions of this work are: an automated methodology for pose alignment, automatic generation of global and local data representations, robust automatic placement of landmarks, generation of local descriptors for nasal and oral facial features, and a 22q11.2DS classification rate which rivals medical experts. The methods developed for the 22q11.2DS phenotype should be widely applicable to the shape-based quantification of any other craniofacial dysmorphology.