Robust localization of ears by feature level fusion and context information

The outer ear has been established as a stable and unique biometric characteristic, especially in the field of forensic image analysis. In the last decade, increasing efforts have been made for building automated authentication systems utilizing the outer ear. One essential processing step in these systems is the detection of the ear region. Automated ear detection faces a number of challenges, such as invariant processing of both left and right ears, as well as the handling of occlusion and pose variations. We propose a new approach for the detection of ears, which uses features from texture and depth images, as well as context information. With a detection rate of 99% on profile images, our approach is highly reliable. Moreover, it is invariant to rotations and it can detect left and right ears. We also show, that our method is working under realistic conditions by providing simulation results on a more challenging dataset, which contains images of occluded ears from various poses.