  This paper addresses the problem of 3D face recognition using simultaneous
sparse approximations on the sphere. The 3D face point clouds are first aligned
with a novel and fully automated registration process. They are then
represented as signals on the 2D sphere in order to preserve depth and geometry
information. Next, we implement a dimensionality reduction process with
simultaneous sparse approximations and subspace projection. It permits to
represent each 3D face by only a few spherical functions that are able to
capture the salient facial characteristics, and hence to preserve the
discriminant facial information. We eventually perform recognition by effective
matching in the reduced space, where Linear Discriminant Analysis can be
further activated for improved recognition performance. The 3D face recognition
algorithm is evaluated on the FRGC v.1.0 data set, where it is shown to
outperform classical state-of-the-art solutions that work with depth images.
