Implications of Ocular Pathologies for Iris Recognition Reliability
Mateusz Trokielewicz, Adam Czajka, Piotr Maciejewicz

TL;DR
This study investigates how various eye diseases impact iris recognition accuracy, revealing that conditions affecting iris structure or causing obstructions significantly hinder recognition performance and segmentation reliability.
Contribution
It provides the largest publicly available dataset of iris images from diseased eyes and offers a comprehensive analysis of disease effects on iris recognition systems.
Findings
Enrollment is sensitive to eye conditions obstructing the iris
Diseases increasing dissimilarity between same-eye samples
Eye conditions cause segmentation errors in recognition
Abstract
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…
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