Personal verification based on extraction and characterisation of retinal feature points

This paper describes a methodology of verification of individuals based on a retinal biometric pattern. The pattern consists in feature points of the retinal vessel tree, namely bifurcations and crossovers. These landmarks are detected and characterised adding semantic information to the biometric pattern. The typical authentication process of a person once extracted the biometric pattern includes matching it with the stored pattern for the authorised user obtaining a similarity value between them. A matching algorithm and a deep analysis of similarity metrics performance is presented. The semantic information added for the feature points allows to reduce the computation load in the matching process as only points classified equally can be matched. The system is capable of establishing a safe confidence band in the similarity measure space between scores for patterns of the same individual and between different individuals.