VISER: Visually-Informed System for Enhanced Robustness in Open-Set Iris Presentation Attack Detection

TL;DR

This paper explores the use of human saliency data, especially denoised eye tracking heatmaps, to improve open-set iris presentation attack detection, demonstrating superior generalization over traditional methods.

Contribution

It compares various human-derived saliency methods and introduces a novel approach using denoised eye tracking heatmaps for enhanced robustness in iris PAD.

Findings

Denoised eye tracking heatmaps outperform other saliency methods in generalization.

The proposed approach improves APCER at a BPCER of 1%.

Provides trained models, code, and saliency maps for reproducibility.

Abstract

Human perceptual priors have shown promise in saliency-guided deep learning training, particularly in the domain of iris presentation attack detection (PAD). Common saliency approaches include hand annotations obtained via mouse clicks and eye gaze heatmaps derived from eye tracking data. However, the most effective form of human saliency for open-set iris PAD remains under-explored. In this paper, we conduct a series of experiments comparing hand annotations, eye tracking heatmaps, segmentation masks, and foundation model embeddings to a state-of-the-art deep learning-based baseline on the task of open-set iris PAD. Results for open-set PAD in a leave-one-attack-type out paradigm indicate that denoised eye tracking heatmaps show the best generalization improvement over cross entropy in Attack Presentation Classification Error Rate (APCER) at Bona Fide Presentation Classification Error Rate (BPCER) of 1%. Along with this paper, we offer trained models, code, and saliency maps for reproducibility and to facilitate follow-up research efforts.