  This paper presents an analysis of how the iris recognition is impacted by
eye diseases and an appropriate dataset comprising 2996 iris images of 230
distinct eyes (including 184 illness-affected eyes representing more than 20
different eye conditions). The images were collected in near infrared and
visible light during a routine ophthalmological practice. The experimental
study shows four valuable results. First, the enrollment process is highly
sensitive to those eye conditions that make the iris obstructed or introduce
geometrical distortions. Second, even those conditions that do not produce
visible changes to the iris structure may increase the dissimilarity among
samples of the same eyes. Third, eye conditions affecting iris geometry, its
tissue structure or producing obstructions significantly decrease the iris
recognition reliability. Fourth, for eyes afflicted by a disease, the most
prominent effect of the disease on iris recognition is to cause segmentation
errors. To our knowledge this is the first database of iris images for
disease-affected eyes made publicly available to researchers, and the most
comprehensive study of what we can expect when the iris recognition is deployed
for non-healthy eyes.
