Merging Graphics and Vision for 3D Face Recognition

Abstract

This paper presents a new approach to automatic 3D face modelling from unstructured point cloud data. An efficient B-Spline surface-fitting algorithm is used to obtain an initial parametric surface for each face point cloud data set. Knot vectors for each individual face surface are then standardised to produce a set of uniform knot vectors so that all the surfaces can be seen as fitted with the same set of knot vectors. Mapping from object space to shape space can then be established so that each 3D face can be described by a small number of shape descriptors. The use of shape descriptors allows automatic registration between face models. More importantly, it allows dynamic facial variation to be modelled and analysed via 3D warping, resulting in a powerful approach to quantifying the differences among individuals required for face recognition. 3D warping is often used in simulations in computer graphics. This paper explains, for the first time, how 3D warping can be exploited for face recognition based on multi-resolution analysis of warping fields. The methodology allows the quantitative study of variation in characteristics previously only described from a qualitative perspective.