Alignment-Free Cancellable Template with Clustered-Minutiae Local Structure

Abstract

Cancellable fingerprint template has increasingly received interest in research thanks to not only the security for the user's original features but also the stable performance for the system. In this paper, we propose a new method to design cancellable fingerprint template with local structure by clustering the minutiae using the k Nearest Neighbor (kNN) algorithm. In other words, $k$ minutiae in a fingerprint that are closest to a reference minutia form a local structure. Pairwise features from the reference minutia and each of the member in the cluster are extracted and used for local structure matching. The partial Discrete Fourier Transformation was applied as the non-invertible transformation. This method has been evaluated with four public databases FVC2002 DB1-DB3, and FVC2004 DB2. The Equal Error Rate achieved for each database is 0.2%, 0.04%, 4.78%, and 7.64%, respectively.