Similarity Metrics Analysis for Feature Point Based Retinal Authentication

Abstract

Biometrics refer to identity verification of individuals based on some physiologic or behavioural characteristics (face, fingerprint, signature...). The typical authentication process of a person consists in extracting a biometric pattern of him/her and matching it with the stored pattern for the authorized user obtaining a similarity value between patterns. If that similarity is bigger than some threshold the authentication is accepted, otherwise is rejected. Thus, the similarity metrics determine the system ability to successfully classify authentications as authorized or unauthorized. In this work, an analysis of similarity metrics performance is presented for a biometric system in which retinal vessel feature points are used as biometric pattern. The results of the system allow to establish a confidence band for the metric threshold where no errors are obtained for training and test sets.