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

TL;DR

This study investigates how pupil size normalization and synthetic iris generation affect human performance in iris verification, highlighting the importance of pupil alignment and challenges posed by high-quality synthetic images.

Contribution

It introduces a modern autoencoder-based pupil normalization method and evaluates human verification accuracy with authentic and synthetic iris images.

Findings

Pupil size normalization improves verification accuracy.

Humans can distinguish between authentic and synthetic irises.

Accuracy drops when comparing authentic irises to high-quality synthetic ones.

Abstract

Iris recognition is a mature biometric technology offering remarkable precision and speed, and allowing for large-scale deployments to populations exceeding a billion enrolled users (e.g., AADHAAR in India). However, in forensic applications, a human expert may be needed to review and confirm a positive identification before an iris matching result can be presented as evidence in court, especially in cases where processed samples are degraded (e.g., in post-mortem cases) or where there is a need to judge whether the sample is authentic, rather than a result of a presentation attack. This paper presents a study that examines human performance in iris verification in two controlled scenarios: (a) under varying pupil sizes, with and without a linear/nonlinear alignment of the pupil size between compared images, and (b) when both genuine and impostor iris image pairs are synthetically generated. The results demonstrate that pupil size normalization carried out by a modern autoencoder-based identity-preserving image-to-image translation model significantly improves verification accuracy. Participants were also able to determine whether iris pairs corresponded to the same or different eyes when both images were either authentic or synthetic. However, accuracy declined when subjects were comparing authentic irises against high-quality, same-eye synthetic counterparts. These findings (a) demonstrate the importance of pupil-size alignment for iris matching tasks in which humans are involved, and (b) indicate that despite the high fidelity of modern generative models, same-eye synthetic iris images are more often judged by humans as different-eye images, compared to same-eye authentic image pairs. We offer data and human judgments along with this paper to allow full replicability of this study and future works.