Biometric Verification: Looking Beyond Raw Similarity Scores

Abstract

Most biometric verification techniques make decisions based solely on a score that represents the similarity of the query template with the reference template of the claimed identity stored in the database. When multiple templates are available, a fusion scheme can be designed using the similarities with these templates. Combining several templates to construct a composite template and selecting a set of useful templates has also been reported in addition to usual multi-classifier fusion methods when multiple matchers are available. These commonly adopted techniques rarely make use of the large number of non-matching templates in the database or training set. In this paper, we highlight the usefulness of such a fusion scheme while focusing on the problem of fingerprint verification. For each enrolled template, we identify its cohorts (similar fingerprints) based on a selection criterion. The similarity scores of the query template with the reference template and its cohorts from the database are used to make the final verification decision using two approaches: a likelihood ratio based normalization scheme and a Support Vector Machine (SVM)-based classifier. We demonstrate the accuracy improvements using the proposed method with no a priori knowledge about the database or the matcher under consideration using a publicly available database and matcher. Using our cohort selection procedure and the trained SVM, we show that accuracy can be significantly improved at the expense of few extra matches.