3D Surface Reconstruction and Recognition

In this paper we propose a novel 3D face recognition system. Furthermore we propose and discuss the development of a 3D reconstruction system designed specifically for the purpose of face recognition. The reconstruction subsystem utilises a capture rig comprising of six cameras to obtain two independent stereo pairs of the subject face during a structured light projection with the remaining two cameras obtaining texture data under normal lighting conditions. Whilst the most common approaches to 3D reconstruction use least square comparison of image intensity values, our system achieves dense point matching using Gabor Wavelets as the primary correspondence measure. The matching process is aided by Voronoi segmentation of the input images using strong confidence correlations as Voronoi seeds. Additional matches are then propagated outwards from the initial seed matches to produce a dense point cloud and surface model. Within the recognition subsystem models are first registered to a generic head model, and then an ICP variant is applied between the recognition subject and each model in the comparison database, using the average point-to-plane error as the recognition metric. Our system takes full advantage of the additional information obtained from the shape and structure of the face, thus combating some of the inherent weaknesses of traditional 2D methods such as pose and illumination variations. This novel reconstruction / recognition process shows 98.2% accuracy on databases containing in excess of 175 meshes.