Random Hash Code Generation for Cancelable Fingerprint Templates using Vector Permutation and Shift-order Process
This addresses security and privacy issues in biometric authentication systems, though it appears incremental as it builds on existing transformation methods.
The paper tackled the vulnerability of cancelable biometric templates to information leakage by proposing a non-invertible distance preserving scheme using vector permutation and shift-order processes, achieving high-performance accuracy better than existing state-of-the-art schemes on FVC2002 and FVC2004 datasets.
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.