Trokielewicz, M; Czajka, A; Maciejewicz, P
This paper presents the experimental study revealing weaker performance of the automatic iris recognition methods for cataract-aected eyes when compared to healthy eyes. There is little research on the topic,mostly incorporating scarce databases that are often decient in images representing more than one illness. We built our own database, acquiring 1288 eye images of 37 patients of the Medical University of Warsaw.Those images represent several common ocular diseases, such as cataract, along with less ordinary conditions,such as iris pattern alterations derived from illness or eye trauma. Images were captured in near-infrared light (used in biometrics) and for selected cases also in visible light (used in ophthalmological diagnosis). Since cataract is a disorder that is most populated by samples in the database, in this paper we focus solely on this illness. To assess the extent of the performance deterioration we use three iris recognition methodologies (commercial and academic solutions) to calculate genuine match scores for healthy eyes and those in uenced by cataract. Results show a signicant degradation in iris recognition reliability manifesting by worsening the genuine scores in all three matchers used in this study (12% of genuine score increase for an academic matcher, up to 175% of genuine score increase obtained for an example commercial matcher). This increase in genuine scores aected the nal false non-match rate in two matchers. To our best knowledge this is the only study of such kind that employs more than one iris matcher, and analyzes the iris image segmentation as a potential source of decreased reliability.