3D Face Recognition Based on Non-iterative Registration and Single B-Spline Patch Modelling Techniques

摘要

This paper presents a new approach to automatic 3D face recognition using a model-based approach. This work uses real 3D dense point cloud data acquired with a scanner using a stereo photogrammetry technique. Since the point clouds are in varied orientations, by applying a non-iterative registration method, we automatically transform each point cloud to a canonical position. Unlike the iterative ICP algorithm, our non-iterative registration process is scale invariant. An efficient B-spline surface-fitting technique is developed to represent 3D faces in a way that allows efficient surface comparison. This is based on a novel knot vector standardisation algorithm which allow a single B-Spline surface to be fitted onto a complex object represented as a unstructured points cloud. Consequently, dense correspondences across objects are established. Several experiments have been conducted and 91% recognition rate can be achieved.