Shape Model-Based 3D Ear Detection from Side Face Range Images

Ear detection is an important part of an ear recognition system. In this paper we propose a shape model-based technique for locating human ears in side face range images. The ear shape model is represented by a set of discrete 3D vertices corresponding to ear helix and anti-helix parts. Given side face range images, step edges are extracted considering the fact that there are strong step edges around the ear helix part. Then the edge segments are dilated, thinned and grouped into different clusters which are potential regions containing ears. For each cluster, we register the ear shape model with the edges. The region with the minimum mean registration error is declared as the detected ear region; the ear helix and anti-helix parts are meanwhile identified. Experiments are performed with a large number of real face range images to demonstrate the effectiveness of our approach. The contributions of this paper are: (a) a ear shape model for locating 3D ears in side face range images, (b) an effective approach to detect human ears from side face range images, (c) experimental results on a large number of ear images.