Towards making HCS ear detection robust against rotation

In identity retrieval from crime scene images, the outer ear (auricle) has ever since been regarded as a valuable characteristic. Because of its unique and permanent shape, the auricle also attracted the attention of researches in the field of biometrics over the last years. Since then, numerous pattern recognition techniques have been applied to ear images but similarly to face recognition, rotation and pose still pose problems to ear recognition systems.

One solution for this is 3D ear imaging. the segmentation of the ear, prior to the actual feature extraction step, however, remains an unsolved problem. In 2010 Zhou at al. have proposed a solution for ear detection in 3D images, which incorporates a nave classifier using Shape Index Histogram. Histograms of Categorized Shapes (HCS) is reported to be efficient and accurate, but has difficulties with rotations.

In our work, we extend the performance measures provided by Zhou et al. by evaluating the detection rate of the HCS detector under more realistic conditions. This includes performance measures with ear images under pose variations. Secondly, we propose to modify the ear detection approach by Zhou et al. towards making it invariant to rotation by using a rotation symmetric, circular detection window.

Shape index histograms are extracted at different radii in order to get overlapping subsets within the circle. The detection performance of the modified HCS detector is evaluated on two different datasets, one of them containing images n various poses.