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. An assessment of how iris recognition is influenced by eye diseases is presented.Reasons beyond lower biometric performance for eyes with conditions are discussed.A dataset of images for 184 illness-affected eyes is offered and described.
This paper offers a significant extension of the work presented in @cite_28 , including an analysis for the fourth, additional iris recognition algorithm. To our best knowledge this study presents the most comprehensive and up-to-date insight into the subject of the influence of ocular pathologies on the reliability of iris biometrics.
@cite_28
2163155631 2082248211 2140595082 1974821667
The richness and apparent stability of the iris texture make it a robust biometric trait for personal authentication. The performance of an automated iris recognition system is affected by the accuracy of the segmentation process used to localize the iris structure. Most segmentation models in the literature assume that the pupillary, limbic, and eyelid boundaries are circular or elliptical in shape. Hence, they focus on determining model parameters that best fit these hypotheses. However, it is difficult to segment iris images acquired under nonideal conditions using such conic models. In this paper, we describe a novel iris segmentation scheme employing geodesic active contours (GACs) to extract the iris from the surrounding structures. Since active contours can 1) assume any shape and 2) segment multiple objects simultaneously, they mitigate some of the concerns associated with traditional iris segmentation models. The proposed scheme elicits the iris texture in an iterative fashion and is guided by both local and global properties of the image. The matching accuracy of an iris recognition system is observed to improve upon application of the proposed segmentation algorithm. Experimental results on the CASIA v3.0 and WVU nonideal iris databases indicate the efficacy of the proposed technique. Although human iris pattern is widely accepted as a stable biometric feature, recent research has found some evidences on the aging effect of iris system. In order to investigate changes in iris recognition performance due to the elapsed time between probe and gallery iris images, we examine the effect of elapsed time on iris recognition utilizing 7,628 iris images from 46 subjects with an average of ten visits acquired over two years from a legacy database at Clarkson University. Taken into consideration the impact of quality factors such as local contrast, illumination, blur and noise on iris recognition performance, regression models are built with and without quality metrics to evaluate the degradation of iris recognition performance based on time lapse factors. Our experimental results demonstrate the decrease of iris recognition performance along with increased elapsed time based on two iris recognition system (the modified Masek algorithm and a commercial software VeriEye SDK). These results also reveal the significance of quality factors in iris recognition regression indicating the variability in match scores. According to the regression analysis, our study in this paper helps provide the quantified decrease on match scores with increased elapsed time, which indicates the possibility to implement the prediction scheme for iris recognition performance based on learning of impact on time lapse factors. Iris recognition systems are among the most accurate of all biometric technologies with immense potential for use in worldwide security applications. This study examined the effect of eye pathology on iris recognition and in particular whether eye disease could cause iris recognition systems to fail. The experiment involved a prospective cohort of 54 patients with anterior segment eye disease who were seen at the acute referral unit of the Princess Alexandra Eye Pavilion in Edinburgh. Iris camera images were obtained from patients before treatment was commenced and again at follow-up appointments after treatment had been given. The principal outcome measure was that of mathematical difference in the iris recognition templates obtained from patients' eyes before and after treatment of the eye disease. Results showed that the performance of iris recognition was remarkably resilient to most ophthalmic disease states, including corneal oedema, iridotomies (laser puncture of iris) and conjunctivitis. Problems were, however, encountered in some patients with acute inflammation of the iris (iritis anterior uveitis). The effects of a subject developing anterior uveitis may cause current recognition systems to fail. Those developing and deploying iris recognition should be aware of the potential problems that this could cause to this key biometric technology. Algorithms developed by the author for recognizing persons by their iris patterns have now been tested in many field and laboratory trials, producing no false matches in several million comparison tests. The recognition principle is the failure of a test of statistical independence on iris phase structure encoded by multi-scale quadrature wavelets. The combinatorial complexity of this phase information across different persons spans about 249 degrees of freedom and generates a discrimination entropy of about 3.2 b mm sup 2 over the iris, enabling real-time decisions about personal identity with extremely high confidence. The high confidence levels are important because they allow very large databases to be searched exhaustively (one-to-many "identification mode") without making false matches, despite so many chances. Biometrics that lack this property can only survive one-to-one ("verification") or few comparisons. The paper explains the iris recognition algorithms and presents results of 9.1 million comparisons among eye images from trials in Britain, the USA, Japan, and Korea.