Pupil Dilation Analysis and Mitigation in Iris Recognition

The use of iris biometrics is increasing for both governmental and private applications. As its use becomes more ubiquitous, it is imperative to determine and mitigate the factors that diminish its performance. It has been assumed that iris biometrics is a stable biometric modality. However, current research continues to demonstrate that it is susceptible to changes due to aging and dilation. Both of these factors have been shown to affect the authentic comparisons, and by consequence the overall recognition accuracy. In this dissertation, we examine and expand methods that incorporate knowledge of pupil dilation. We show that by including pupil dilation information into the enrollment phase there is an overall improvement in iris biometric performance. In the second part of this dissertation we focus on the analysis of template aging by examining the models and data used in NIST's IREX VI report. The two research areas are bridged with our analysis of aging and dilation difference where we found that both factors contribute to the behavior of match scores. The research in each of these two areas improves our understanding of iris biometrics with respect to pupil dilation and template aging.