Performance Evaluation and Prediction for 3D Ear Recognition

摘要

Existing ear recognition approaches do not give theoretical or experimental performance prediction. Therefore, the discriminating power of ear bio-metric for human identification cannot be evaluated. This paper addresses two interrelated problems: (a) proposes an integrated local descriptor for representation to recognize human ears in 3D. Comparing local surface descriptors between a test and a model image, an initial correspondence of local surface patches is established and then filtered using simple geometric constraints. The performance of the proposed ear recognition system is evaluated on a real range image database of 52 subjects. (b) A binomial model is also presented to predict the ear recognition performance. Match and non-matched distances obtained from the database of 52 subjects are used to estimate the distributions. By modeling cumulative match characteristic (CMC) curve as a binomial distribution, the ear recognition performance can be predicted on a larger gallery.