Viability of Optical Coherence Tomography for Iris Presentation Attack Detection

TL;DR

This paper explores the use of Optical Coherence Tomography (OCT) imaging for iris presentation attack detection, comparing its effectiveness with traditional NIR and visible spectrum methods using deep learning models.

Contribution

It introduces OCT imaging as a novel modality for iris PAD and evaluates its performance against traditional methods using deep neural networks.

Findings

OCT provides promising results for iris PAD.

OCT outperforms traditional imaging in certain attack scenarios.

Deep architectures effectively differentiate bonafide and attack samples.

Abstract

In this paper, we propose the use of Optical Coherence Tomography (OCT) imaging for the problem of iris presentation attack (PA) detection. We assess its viability by comparing its performance with respect to traditional iris imaging modalities, viz., near-infrared (NIR) and visible spectrum. OCT imaging provides a cross-sectional view of an eye, whereas traditional imaging provides 2D iris textural information. PA detection is performed using three state-of-the-art deep architectures (VGG19, ResNet50 and DenseNet121) to differentiate between bonafide and PA samples for each of the three imaging modalities. Experiments are performed on a dataset of 2,169 bonafide, 177 Van Dyke eyes and 360 cosmetic contact images acquired using all three imaging modalities under intra-attack (known PAs) and cross-attack (unknown PAs) scenarios. We observe promising results demonstrating OCT as a viable solution for iris presentation attack detection.