Large scale face identification by combined iconic features and 3D joint invariant signatures

In this paper, we present a 2D/3D multimodal face identification system. A set of iconic fiducial points and descriptors is first extracted from the images of the faces and a preliminary correspondence between the points is established on the basis of the descriptor content. Subsequently, the points are mapped on the scans and used to calculate 3D joint differential invariant vectors that define a signature of the face. Since a correspondence between the invariants is inherited from the 2D feature point matching, the signatures of the faces can be efficiently compared by evaluating the distance between corresponding vectors, thus validating the 2D matching hypothesis. This methodology guarantees an effective and fast alignment of the 3D scans, avoids iterative registration procedures and provides a simple similarity measure for face identification. Extensive tests were carried out on the FRGCv2 and on the Bosphorus databases, which both contain 3D and texture information of faces. Results show that the method is robust to expressions provided the images are of good quality, and that it is particularly suited to identification tasks in the cases of medium to large databases with multiple gallery enrolment. Indeed, in these scenarios, the performance was superior or comparable to state of the art methods, with execution times often faster by several orders of magnitude.