  This paper presents the experimental study revealing weaker performance of
the automatic iris recognition methods for cataract-affected eyes when compared
to healthy eyes. There is little research on the topic, mostly incorporating
scarce databases that are often deficient 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 influenced by
cataract. Results show a significant 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 affected the final 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.
