Identity verification through finger matching: A comparison of Support Vector Machines and Gaussian Basis Functions classifiers

Abstract

The paper presents a people identity verification system based on the matching of top view finger snapshots, supplementing purely geometrical finger shape comparison with textural information. Low dimensional feature vectors are used to train binary classifiers based on small Gaussian Basis Functions networks which, in this task, are able to match Support Vector Machines performance while outperforming them in runtime efficiency, thereby exposing a different facet in the comparison which complements available literature reports.