Cancellable Multi-Biometric Template Generation Based on Arnold Cat Map and Aliasing

Abstract

The cancellable biometric transformations are designed to be computationally difficult to obtain the original biometric data. This paper presents a cancellable multi-biometric identification scheme that includes four stages: biometric data collection and processing, Arnold's Cat Map encryption, decimation process to reduce the size, and final merging of the four biometrics in a single generated template. First, a 2D matrix of size 128 × 128 is created based on Arnold's Cat Map (ACM). The purpose of this rearrangement is to break the correlation between pixels to hide the biometric patterns and merge these patterns together for more security. The decimation is performed to keep the dimensions of the overall cancellable template similar to those of a single template to avoid data redundancy. Moreover, some sort of aliasing occurs due to decimation, contributing to the intended distortion of biometric templates. The hybrid structure that comprises encryption, decimation, and merging generates encrypted and distorted cancellable templates. The simulation results obtained for performance evaluation show that the system is safe, reliable, and feasible as it achieves high security in the presence of noise.