Effect of cataract surgery and pupil dilation on iris pattern recognition for personal authentication

The richness of the iris texture and its variability across individuals make it a useful biometric trait for personal authentication. One of the key stages in classical iris recognition is the normalization process, where the annular iris region is mapped to a dimensionless pseudo-polar coordinate system. This process results in a rectangular structure that can be used to compensate for differences in scale and variations in pupil size. Most iris recognition methods in the literature adopt linear sampling in the radial and angular directions when performing iris normalization. In this paper, a biomechanical model of the iris is used to define a novel nonlinear normalization scheme that improves iris recognition accuracy under different degrees of pupil dilation. The proposed biomechanical model is used to predict the radial displacement of any point in the iris at a given dilation level, and this information is incorporated in the normalization process. Experimental results on the WVU pupil light reflex database (WVU-PLR) indicate the efficacy of the proposed technique, especially when matching iris images with large differences in pupil size.

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“…These factors include changes in the ambient lighting conditions [11], alcohol [1], drugs [5], and aging [6,8].…”

Section: Effect Of Changes In Pupil Size: An Overviewmentioning

confidence: 99%

A biomechanical approach to iris normalization

Tomeo-Reyes

Ross

Clark

et al. 2015

2015 International Conference on Biometrics (ICB)

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“…Dhir et al [16] studied the influence of the effects of mydriatics accompanying cataract surgery. A group of 15 patients had their eyes enrolled before surgery.…”

Section: Related Workmentioning

confidence: 99%

Implications of ocular pathologies for iris recognition reliability

Trokielewicz

Czajka

Maciejewicz

2017

Image and Vision Computing

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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.

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“…In an experiment conducted by Dhir et al 8 in addition to cataract surgery itself, an influence of mydriatics is studied. 15 patients have been enrolled in the system and then a verification was performed 5, 10 and 15 minutes after mydriatics were applied and also 2 weeks after the procedure.…”

Section: Related Workmentioning

confidence: 99%

Cataract influence on iris recognition performance

2014

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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.

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Effect of cataract surgery and pupil dilation on iris pattern recognition for personal authentication

Cited by 26 publications

References 5 publications

A biomechanical approach to iris normalization

A biomechanical approach to iris normalization

Implications of ocular pathologies for iris recognition reliability

Cataract influence on iris recognition performance

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