Characterizing Light-Adapted Pupil Size in the NIR Spectrum

Abstract

Advances in iris recognition discuss the impact of pupil size variations on iris matching accuracy, thus promoting the need to model these covariates that are present in the near infrared (NIR) spectrum to measure their level of authenticity. This work incorporates these principles to propose a novel methodology that automatically distinguishes subject-specific variations of light-adapted pupil size behavior in iris video sequences consisting of two main steps. In the first step, the sinuous nature of the light-adapted pupil size is characterized, which depends on the dilation extrema, the dilative rate of change, and midpoint behavior. In the second and final step, these aspects are fed into a classification framework that distinguishes subject-specific light-adapted pupil size responses. Experimental results, when using the West Virginia University Pupillary Light Reflex Ramp (WVU-PLR Ramp) dataset, convey the efficacy of this approach with classification accuracies ranging from 92% - 100% when support vector machines (SVMs) are used and 100% when neural networks (NNs) are applied. The results of this work can be used to effectively describe light-adapted variations in pupil size. Additionally, these results indicate that the changes in light-adapted pupil size can potentially be a soft iris biometric trait.