D-NetPAD: An Explainable and Interpretable Iris Presentation Attack Detector

TL;DR

This paper introduces D-NetPAD, an explainable deep learning-based iris presentation attack detector that achieves high accuracy and generalizes well across different attack types, sensors, and datasets, with visual explanations of its decision process.

Contribution

The work presents a novel DenseNet-based iris PA detector with explainability features, demonstrating superior performance and robustness over existing methods.

Findings

Achieves 98.58% TDR at 0.2% FDR on proprietary dataset.

Outperforms state-of-the-art on LivDet-2017 dataset.

Provides visual explanations using t-SNE and Grad-CAM.

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 circumvent the system. In this work, we propose an effective and robust iris PA detector called D-NetPAD based on the DenseNet convolutional neural network architecture. It demonstrates generalizability across PA artifacts, sensors and datasets. Experiments conducted on a proprietary dataset and a publicly available dataset (LivDet-2017) substantiate the effectiveness of the proposed method for iris PA detection. The proposed method results in a true detection rate of 98.58\% at a false detection rate of 0.2\% on the proprietary dataset and outperfoms state-of-the-art methods on the LivDet-2017 dataset. We visualize intermediate feature distributions and fixation heatmaps using t-SNE plots and Grad-CAM, respectively, in order to explain the performance of D-NetPAD. Further, we conduct a frequency analysis to explain the nature of features being extracted by the network. The source code and trained model are available at https://github.com/iPRoBe-lab/D-NetPAD.