Non-Metric Biometric Clustering

Abstract

The goal of this research is to demonstrate how a non-metric clustering technique can be used to effectively reduce the search time for finding matches among biometric templates. Some biometric modalities (such as fingerprint) have proven to not cluster effectively with traditional clustering techniques. Without clustering, identification requires an expensive exhaustive search. This research explores the effectiveness of a novel clustering technique using false matches in a non-metric space. False matches are typically undesirable false positive errors that increase with gallery size. This clustering approach uses these false matches as references for clustering in non-metric similarity space. Searches can then be restricted to only those clusters that claim the probe as a member.