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.
Roizenblatt @cite_1 were probably the first to analyze how eye pathologies might influence the performance of iris recognition. They report False Non-Match Rate (FNMR) equal to 11
@cite_1
1974821667 2082248211 2102796633 2132324157
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. 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. A method for rapid visual recognition of personal identity is described, based on the failure of a statistical test of independence. The most unique phenotypic feature visible in a person's face is the detailed texture of each eye's iris. The visible texture of a person's iris in a real-time video image is encoded into a compact sequence of multi-scale quadrature 2-D Gabor wavelet coefficients, whose most-significant bits comprise a 256-byte "iris code". Statistical decision theory generates identification decisions from Exclusive-OR comparisons of complete iris codes at the rate of 4000 per second, including calculation of decision confidence levels. The distributions observed empirically in such comparisons imply a theoretical "cross-over" error rate of one in 131000 when a decision criterion is adopted that would equalize the false accept and false reject error rates. In the typical recognition case, given the mean observed degree of iris code agreement, the decision confidence levels correspond formally to a conditional false accept probability of one in about 10 sup 31 . > Detection of the eyes in human facial images is important in preprocessing for face recognition, since the results can be used to determine the position, size, and direction of the face. This paper describes a method of detection of both irises from a gray-scale facial image. In this method, first a small region (“blob”) which is likely to be an iris is determined by using a separability filter (developed by Fukui and Yamaguchi). Second, taking all the blobs, the costs of all segments connecting the irises and the inclinations of the segments which are within a predetermined value are computed. This cost can be calculated from the regions surrounding the blobs, by using the number of votes for the maximum-voting circle, the separability of the surrounding small regions, the mean brightness within the iris, and the normalized correlation coefficient between a partial image including a pair of blobs and the template for the eyes. The pair of blobs that minimizes the cost is regarded as the irises. The proposed method has been tested by using all 150 images (excluding images with glasses) from the University of Bern Facial Image Database. The experimental results show that the success rates are between 95.3 (worst) and 97.3 (best). If minor errors are tolerated, the success rates are between 96.7 (worst) and 99.3 (best). © 2005 Wiley Periodicals, Inc. Electron Comm Jpn Pt 2, 88(5): 29–39, 2005; Published online in Wiley InterScience (www.interscience.wiley. com). DOI 10.1002 ecjb.20178