The behavior of nine 3D shape descriptors which were computed on the surface of 3D face models, is studied.
The set of descriptors includes six curvature-based ones, SPIN images, Folded SPIN Images, and Finger prints.
Instead of defining clusters of vertices based on the value of a given primitive surface feature, a face template
composed by 28 anatomical regions, is used to segment the models and to extract the location of different
landmarks and fiducial points. Vertices are grouped by: region, region boundaries, and subsampled versions of
them. The aim of this study is to analyze the discriminant capacity of each descriptor to characterize regions
and to identify key points on the facial surface. The experiment includes testing with data from neutral faces
and faces showing expressions. Also, in order to see the usefulness of the bending-invariant canonical form
(BICF) to handle variations due to facial expressions, the descriptors are computed directly from the surface
and also from its BICF. In the results: the values, distributions, and relevance indexes of each set of vertices,