Random Hash Code Generation for Cancelable Fingerprint Templates using Vector Permutation and Shift-order Process

TL;DR

This paper introduces a novel non-invertible hash code generation method for cancelable fingerprint templates using vector permutation and shift-order processes, enhancing security and privacy while maintaining high accuracy.

Contribution

It proposes a new scheme combining KPCA, vector permutation, and shift-order processes to improve security and accuracy in cancelable fingerprint templates.

Findings

High accuracy on FVC2002 and FVC2004 datasets

Resilience to security and privacy attacks

Outperforms existing state-of-the-art schemes

Abstract

Cancelable biometric techniques have been used to prevent the compromise of biometric data by generating and using their corresponding cancelable templates for user authentication. However, the non-invertible distance preserving transformation methods employed in various schemes are often vulnerable to information leakage since matching is performed in the transformed domain. In this paper, we propose a non-invertible distance preserving scheme based on vector permutation and shift-order process. First, the dimension of feature vectors is reduced using kernelized principle component analysis (KPCA) prior to randomly permuting the extracted vector features. A shift-order process is then applied to the generated features in order to achieve non-invertibility and combat similarity-based attacks. The generated hash codes are resilient to different security and privacy attacks whilst fulfilling the major revocability and unlinkability requirements. Experimental evaluation conducted on 6 datasets of FVC2002 and FVC2004 reveals a high-performance accuracy of the proposed scheme better than other existing state-of-the-art schemes.