  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.
