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cs.CV2026

Generalist Multimodal LLMs Gain Biometric Expertise via Human Salience

Jacob Piland, Byron Dowling, Christopher Sweet +1

Iris presentation attack detection (PAD) is critical for secure biometric deployments, yet developing specialized models faces significant practical barriers: collecting data repre…

cs.CV2026

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

Byron Dowling, Jacob Piland, Eleanor Frederick +2

Human perceptual priors have shown promise in saliency-guided deep learning training, particularly in the domain of iris presentation attack detection (PAD). Common saliency approa…

cs.CV2026

When Humans Judge Irises: Pupil Size Normalization as an Aid and Synthetic Irises as a Challenge

Mahsa Mitcheff, Adam Czajka

Iris recognition is a mature biometric technology offering remarkable precision and speed, and allowing for large-scale deployments to populations exceeding a billion enrolled user…

cs.CV2025

Gradient-Guided Exploration of Generative Model's Latent Space for Controlled Iris Image Augmentations

Mahsa Mitcheff, Siamul Karim Khan, Adam Czajka

Developing reliable iris recognition and presentation attack detection methods requires diverse datasets that capture realistic variations in iris features and a wide spectrum of a…

cs.CV2025

Salient Information Preserving Adversarial Training Improves Clean and Robust Accuracy

Timothy Redgrave, Adam Czajka

In this work we introduce Salient Information Preserving Adversarial Training (SIP-AT), an intuitive method for relieving the robustness-accuracy trade-off incurred by traditional…

cs.CV2024

Training Better Deep Learning Models Using Human Saliency

Aidan Boyd, Patrick Tinsley, Kevin W. Bowyer +1

This work explores how human judgement about salient regions of an image can be introduced into deep convolutional neural network (DCNN) training. Traditionally, training of DCNNs…

cs.CV2024

Privacy-Safe Iris Presentation Attack Detection

Mahsa Mitcheff, Patrick Tinsley, Adam Czajka

This paper proposes a framework for a privacy-safe iris presentation attack detection (PAD) method, designed solely with synthetically-generated, identity-leakage-free iris images.…