SIC-Gen: A Synthetic Iris-Code Generator

Abstract

Nowadays large-scale identity management systems enrol more than one billion data subjects. In order to limit transaction times, biometric indexing is a suitable method to reduce the search space in biometric identifications. Effective testing of such biometric identification systems and biometric indexing approaches requires large datasets of biometric data. Currently, the size of the publicly available iris datasets is insufficient, especially for system scalability assessments. Synthetic data generation offers a potential solution to this issue; however, it is challenging to generate data hat is both statistically sound and visually realistic - for the iris, the currently available approaches prove unsatisfactory. In this paper, we present a method for generation of synthetic binary iris-based templates, i.e. Iris-Codes, which are the de facto standard used throughout major biometric deployments around the world. We validate the statistical properties of the synthetic templates and show that they closely resemble ones produced from real ocular images. With the proposed approach, large databases of synthetic Iris-Codes with flexibly adjustable properties can be generated.