Human centric recognition of 3D ear models

AbstractComparing ear photographs is considered to be an important aspect of disaster victim identification and other forensic and security applications. An interesting approach concerns the construction of 3D ear models by fitting the parameters of a ‘standard’ ear shape, in order to transform it into an optimal approximation of a 3D ear image. A feature list is then extracted from each 3D ear model and used in the recognition process. In this paper, we study how the quality and usability of a recognition process can be improved by computational intelligence techniques. More specifically, we study and illustrate how bipolar data modelling and aggregation techniques can be used for improving the representation and handling of data imperfections. A novel bipolar measure for computing the similarity between corresponding feature lists is proposed. This measure is based on the Minkowski distance, but explicitly deals with hesitation that is caused by bad image quality. Moreover, we investigate how forensic e...