Viability of Optical Coherence Tomography for Iris Presentation Attack Detection
This addresses the problem of biometric security for iris recognition systems, but it is incremental as it applies existing deep learning methods to a new imaging modality.
The paper tackled iris presentation attack detection by proposing Optical Coherence Tomography (OCT) imaging and comparing it to traditional near-infrared and visible spectrum methods, achieving promising results in experiments with 2,169 bonafide and 537 attack samples using deep learning architectures.
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.