Deep Learning-Assisted Iris Liveness Detection Mechanisms

Iris recognition systems are attaining wide consideration for securing critical computing infrastructure. Though, these systems are exposed to a various kind of attacks or threat that may degrade the performance of the overall system in real-time scenario. Among all, spoofing attacks are the most attempted once, where an adversary tries to break the security by offering a fake artifact of a real biometrical trait. In this paper, we explore most recent development in the area of iris vitality detection mechanisms that are inspired by deep learning-based models. Besides, we illustrate an analysis of iris vitality detection (IVD) anti-spoofing datasets and performance evaluation protocols. Our overall instigation indicates that majority of the recent methods perform well in known attack scenario, however exhibit limited performance in unseen attacks set-up. One of the major challenges is to develop a robust IVD method with smaller datasets that has demonstrates superior performance in cross-scenario settings.