Iris + Ocular: Generalized Iris Presentation Attack Detection Using Multiple Convolutional Neural Networks

Abstract

An iris recognition system is vulnerable to presentation attacks, or PAs, where an adversary presents artifacts such as printed eyes, plastic eyes or cosmetic contact lenses to defeat the system. Existing PA detection schemes do not have good generalization capability and often fail in cross-dataset scenarios, where training and testing are performed on vastly different datasets. In this work, we address this problem by fusing the outputs of three Convolutional Neural Network (CNN) based PA detectors, each of which examines different portions of the input image. The first CNN (I-CNN) focuses on the iris region only, the second CNN (F-CNN) uses the entire ocular region and the third CNN (S-CNN) uses a subset of patches sampled from the ocular region. Experiments conducted on two publicly available datasets (LivDetW15 and BERC-IF) and on a proprietary dataset (IrisID) confirm that the use of a bag of CNNs is effective in improving the generalizability of PA detectors.