This paper presents a novel approach, called “Pre-computed Voxel Nearest Neighbor”, to reduce the computational time for shape matching in a biometrics context.
In a biometrics scenario, gallery images are enrolled into the database ahead of the matching step, which gives us the opportunity to build related data structures before the probe shape is examined. The proposed approach shifts the heavy computation burden to the enrollment stage, which is done offline. Experiments in 3D ear biometrics with 369 subjects and 3D face biometrics with 219 subjects demonstrate the effectiveness of this approach. (Publisher abstract provided)