High Confidence Visual Recognition of Persons by a Test of Statistical Independence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Experiments with an Improved Iris Segmentation Algorithm
AUTOID '05 Proceedings of the Fourth IEEE Workshop on Automatic Identification Advanced Technologies
A Bayesian Approach to Deformed Pattern Matching of Iris Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Image understanding for iris biometrics: A survey
Computer Vision and Image Understanding
IEEE Transactions on Pattern Analysis and Machine Intelligence
New Methods in Iris Recognition
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Efficient iris recognition by characterizing key local variations
IEEE Transactions on Image Processing
IEEE Transactions on Circuits and Systems for Video Technology
Nonlinear Iris deformation correction based on Gaussian model
ICB'07 Proceedings of the 2007 international conference on Advances in Biometrics
ICB '09 Proceedings of the Third International Conference on Advances in Biometrics
Estimating and fusing quality factors for iris biometric images
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans - Special issue on recent advances in biometrics
Car plate recognition by whole 2-D image
Expert Systems with Applications: An International Journal
On the commonality of iris biometrics
Proceedings of the 9th International Conference on Advances in Mobile Computing and Multimedia
Image security and biometrics: a review
HAIS'12 Proceedings of the 7th international conference on Hybrid Artificial Intelligent Systems - Volume Part II
Genetically identical irises have texture similarity that is not detected by iris biometrics
Computer Vision and Image Understanding
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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.