Efficient Generation of Cancelable Face Templates Based on Quantum Image Hilbert Permutation

Abstract

The pivotal need to identify people requires efficient and robust schemes to guarantee high levels of personal information security. This paper introduces an encryption algorithm to generate cancelable face templates based on quantum image Hilbert permutation. The objective is to provide sufficient distortion of human facial biometrics to be stored in a database for authentication requirements through encryption. The strength of the proposed Cancelable Biometric (CB) scheme is guaranteed through the ability to generate cancelable face templates by performing the scrambling operation of the face biometrics after addition of a noise mask with a pre-specified variance and an initial seed. Generating the cancelable templates depends on a strategy with three basic steps: Initialization, Odd module, and Even module. Notably, the proposed scheme achieves high recognition rates based on the Area under the Receiver Operating Characteristic (AROC) curve, with a value up to 99.51%. Furthermore, comparisons with the state-of-the-art schemes for cancelable face recognition are performed to validate the proposed scheme.