Fingerprint Alignment for A Minutiae-Based Fuzzy Vault

Abstract

The fuzzy vault is an innovative cryptographic construct that uses error correction techniques to compensate for natural biometric variation. For fingerprints, the fuzzy vault can be used to compensate for the insertion and deletion of minutiae between samples, within the cryptographic framework. However, fingerprint biometrics also suffer from the problem that samples at enrolment and verification cannot be captured and recorded within a universally agreed frame of reference. There is currently no efficient fingerprint pre-alignment technique that also protects the template. In this paper we propose a pre-alignment algorithm that incorporates quantifiable template protection and explore the suitability of three minutiae-based structures for the algorithm. We find that one of the structures is strongly suitable with respect to the goals of our pre-alignment algorithm and its impact on the false non-match rate of an overall system is quantified. Our research also clarifies the key characteristics required from minutiae-based structures for high performance.