Point-Triplet Descriptors for 3D Facial Landmark Localisation

Abstract

An investigation to localise facial landmarks from 3D images is presented, without using any assumption concerning facial pose. This paper introduces new surface descriptors, which are derived from either unstructured face data, or a radial basis function (RBF) model of the facial surface. Two new variants of feature descriptors are described, generally named as point - triplet descriptors because they require three vertices to be computed. The first is related to the classical depth map feature, which is referred to as weighted - interpolated depth map. The second variant of descriptors are derived from an implicit RBF model, they are referred to as surface RBF signature (SRS) features. Both variants of descriptors are able to encode surface information within a triangular region defined by a point - triplet into a surface signature, which could be useful not only for 3D face processing but also within a number of graph based retrieval applications. These descriptors are embedded into a system designed to localise the nose - tip and two inner - eye corners. Landmark localisation performance is reported by computing errors of estimated landmark locations against our respective ground --truth data from the Face Recognition Grand Challenge (FRGC) database.