Human Ear Detection from 3d Side Face Range Images

Ear is a new class of relatively stable biometrics which is not affected by facial expressions, cosmetics and eye glasses. To use ear biometrics for human identification, ear detection is the first part of an ear recognition system. In this chapter we propose two approaches for locating human ears in side face range images: (a) template matching based ear detection and (b) ear shape model based detection. For the first approach, the model template is represented by an averaged histogram of shape index that can be computed from principal curvatures. The ear detection is a four-step process: step edge detection and thresholding, image dilation, connect-component labeling and template matching. For the second approach, the ear shape model is represented by a set of discrete 3D vertices corresponding to ear helix and anti-helix parts. Given a side face range image, step edges are extracted and then the edge segments are dilated, thinned and grouped into different clusters which are the potential regions containing an ear. 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; during this process the ear helix and antihelix parts are identified. Experiments are performed with a large number of real side face range images to demonstrate the effectiveness of the proposed approaches.