Convolutional Neural Networks for Iris Presentation Attack Detection: Toward Cross-Dataset and Cross-Sensor Generalization

TLDR: A Convolutional Neural Network architecture for presentation attack detection, that is observed to have good cross-dataset generalization capability and to use the pre-normalized iris rather than the normalized iris, thereby avoiding spatial information loss.

Abstract: Iris recognition systems are vulnerable to presentation attacks where an adversary employs artifacts such as 2D prints of the eye, plastic eyes, and cosmetic contact lenses to obfuscate their own identity or to spoof the identity of another subject. In this work, we design a Convolutional Neural Network (CNN) architecture for presentation attack detection, that is observed to have good cross-dataset generalization capability. The salient features of the proposed approach include: (a) the use of the pre-normalized iris rather than the normalized iris, thereby avoiding spatial information loss; (b) the tessellation of the iris region into overlapping patches to enable data augmentation as well as to learn features that are location agnostic; (c) fusion of information across patches to enhance detection accuracy; (d) incorporating a "segmentation mask" in order to automatically learn the relative importance of the pupil and iris regions; (e) generation of a "heat map" that displays patch-wise presentation attack information, thereby accounting for artifacts that may impact only a small portion of the iris region. Experiments confirm the efficacy of the proposed approach.