Pupil dilation degrades iris biometric performance

Iris biometrics research has largely ignored the problems associated with variations in pupil dilation between the enrollment image and the image to be recognized or verified. Indeed, in most current systems, information about pupil dilation is discarded when the iris region is normalized to a dimensionless polar coordinate system from which the iris code is obtained. This work studies the effect of pupil dilation on the accuracy of iris biometrics. We found that when the degree of dilation is similar at enrollment and recognition, comparisons involving highly dilated pupils result in worse recognition performance than comparisons involving constricted pupils. We also found that when the matched images have similarly highly dilated pupils, the mean Hamming distance of the match distribution increases and the mean Hamming distance of the non-match distribution decreases, bringing the distributions closer together from both directions. We further found that when matching enrollment and recognition images of the same person, larger differences in pupil dilation yield higher template dissimilarities, and so a greater chance of a false non-match. We recommend that a measure of pupil dilation be kept as meta-data for every iris code. Also, the absolute dilation of the two images, and the dilation difference between them, should factor into a confidence measure for an iris match.