  This paper presents an analysis of how iris recognition is influenced by eye
disease and an appropriate dataset comprising 2996 images of irises taken from
230 distinct eyes (including 184 affected by more than 20 different eye
conditions). The images were collected in near infrared and visible light
during routine ophthalmological examination. The experimental study carried out
utilizing four independent iris recognition algorithms (MIRLIN, VeriEye, OSIRIS
and IriCore) renders four valuable results. First, the enrollment process is
highly sensitive to those eye conditions that obstruct the iris or cause
geometrical distortions. Second, even those conditions that do not produce
visible changes to the structure of the iris may increase the dissimilarity
between samples of the same eyes. Third, eye conditions affecting the geometry
or the tissue structure of the iris or otherwise producing obstructions
significantly decrease same-eye similarity and have a lower, yet still
statistically significant, influence on impostor comparison scores. Fourth, for
unhealthy eyes, the most prominent effect of disease on iris recognition is to
cause segmentation errors. To our knowledge this paper describes the largest
database of iris images for disease-affected eyes made publicly available to
researchers and offers the most comprehensive study of what we can expect when
iris recognition is employed for diseased eyes.
