Robust Scheme for Iris Presentation Attack Detection Using Multiscale Binarized Statistical Image Features

Vulnerability of iris recognition systems remains a challenge due to diverse presentation attacks that fail to assure the reliability when adopting these systems in real-life scenarios. In this paper, we present an in-depth analysis of presentation attacks on iris recognition systems especially focusing on the photo print attacks and the electronic display (or screen) attack. To this extent, we introduce a new relatively large scale visible spectrum iris artefact database comprised of 3300 iris normal and artefact samples that are captured by simulating five different attacks on iris recognition system. We also propose a novel presentation attack detection (PAD) scheme based on multiscale binarized statistical image features and linear support vector machines. Extensive experiments are carried out on four different publicly available iris artefact databases that have revealed the outstanding performance of the proposed PAD scheme when benchmarked with various well-established state-of-the-art schemes.