A Fully Automatic Approach for Human Recognition from Profile Images Using 2D and 3D Ear Data

The use of ear shape as a biometric trait for recognizing people in different applications is one of the most recent trends in the research communities. In this work, a fully automatic and fast technique based on the AdaBoost algorithm is used to detect a subject’s ear from his/her 2D and corresponding 3D profile images. A modified version of the Iterative Closest Point (ICP) algorithm is then used for the matching of this extracted probe ear to the previously stored ear data in a gallery database. A coarse-to-fine hierarchical technique is used where the ICP algorithm is first applied on low and then on high resolution meshes of 3D ear data. We obtain a rank one recognition rate of 93% while testing with the University of Notre Dame Biometrics Database. The proposed recognition approach does not require any manual intervention or sharp extraction of ear contour from the detected ear region. No segmentation of the extracted ear is required and more importantly, the system performance does not rely on the presence of a particular feature of the ear.