This paper presents a novel iris coding method based on differences of discrete cosine transform (DCT) coefficients of overlapped angular patches from normalized iris images. The feature extraction capabilities of the DCT are optimized on the two largest publicly available iris image data sets, 2,156 images of 308 eyes from the CASIA database and 2,955 images of 150 eyes from the Bath database. On this data, we achieve 100 percent Correct Recognition Rate (CRR) and perfect Receiver-Operating Characteristic (ROC) Curves with no registered false accepts or rejects. Individual feature bit and patch position parameters are optimized for matching through a product-of-sum approach to Hamming distance calculation. For verification, a variable threshold is applied to the distance metric and the False Acceptance Rate (FAR) and False Rejection Rate (FRR) are recorded. A new worst-case metric is proposed for predicting practical system performance in the absence of matching failures, and the worst case theoretical Equal Error Rate (EER) is predicted to be as low as 2.59 x 10(-4) on the available data sets.

Purpose The purpose of this study was to investigate the effect of cataract surgery and pupil dilation on iris pattern recognition for personal authentication. Methods Prospective non-comparative cohort study. Images of 15 subjects were captured before (enrolment), and 5, 10, and 15 min after instillation of mydriatics before routine cataract surgery. After cataract surgery, images were captured 2 weeks thereafter. Enrolled and test images (after pupillary dilation and after cataract surgery) were segmented to extract the iris. This was then unwrapped onto a rectangular format for normalization and a novel method using the Discrete Cosine Transform was applied to encode the image into binary bits. The numerical difference between two iris codes (Hamming distance, HD) was calculated. The HD between identification and enrolment codes was used as a score and was compared with a confidence threshold for specific equipment, giving a match or non-match result. The Correct Recognition Rate (CRR) and Equal Error Rates (EERs) were calculated to analyse overall system performance. Results After cataract surgery, perfect identification and verification was achieved, with zero false acceptance rate, zero false rejection rate, and zero EER. After pupillary dilation, non-elastic deformation occurs and a CRR of 86.67% and EER of 9.33% were obtained. Conclusions Conventional circle-based localization methods are inadequate. Matching reliability decreases considerably with increase in pupillary dilation. Cataract surgery has no effect on iris pattern recognition, whereas pupil dilation may be used to defeat an iris-based authentication system.

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