Bipolar Comparison of 3D Ear Models

Comparing ear photographs is considered to be an important aspect of victim identification. In this paper we study how automated ear comparison can be improved with soft computing techniques. More specifically we describe and illustrate how bipolar data modelling techniques can be used for handling data imperfections more adequately. In order to minimise rescaling and reorientation problems, we start with 3D ear models that are obtained from 2D ear photographs. To compare two 3D models, we compute and aggregate the similarities between corresponding points. Hereby, a novel bipolar similarity measure is proposed. This measure is based on Euclidian distance, but explicitly deals with hesitation caused by bad data quality. Comparison results are expressed using bipolar satisfaction degrees which, compared to traditional approaches, provide a semantically richer description of the extent to which two ear photographs match.