Image understanding for iris biometrics: A survey
Computer Vision and Image Understanding
Optimal features subset selection and classification for iris recognition
Journal on Image and Video Processing - Regular
On estimating performance indices for biometric identification
Pattern Recognition
Image Averaging for Improved Iris Recognition
ICB '09 Proceedings of the Third International Conference on Advances in Biometrics
Low-complexity iris coding and recognition based on directionlets
IEEE Transactions on Information Forensics and Security
Robust iris recognition algorithm for non-cooperative environment
International Journal of Biometrics
A new unconstrained iris image analysis and segmentation method in biometrics
ISBI'09 Proceedings of the Sixth IEEE international conference on Symposium on Biomedical Imaging: From Nano to Macro
Parallelizing iris recognition
IEEE Transactions on Information Forensics and Security - Special issue on electronic voting
Iris recognition using signal-level fusion of frames from video
IEEE Transactions on Information Forensics and Security - Special issue on electronic voting
A computational efficient Iris extraction approach in unconstrained environments
BTAS'09 Proceedings of the 3rd IEEE international conference on Biometrics: Theory, applications and systems
Spatiotemporal analysis of human activities for biometric authentication
Computer Vision and Image Understanding
A review of information fusion techniques employed in iris recognition systems
International Journal of Advanced Intelligence Paradigms
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Practical iris-based identification systems are easily accessible for data collection at the matching score level. In a typical setting, a video camera is used to collect a single frontal view image of good quality. The image is then preprocessed, encoded, and compared with all entries in the biometric database resulting in a single highest matching score. In this paper, we assume that multiple scans from the same iris are available and design the decision rules based on this assumption. We consider the cases where vectors of matching scores may be described by a Gaussian model with dependent components under both genuine and imposter hypotheses. Two test statistics: the plug-in loglikelihood ratio and the average Hamming distance are designed. We further analyze the performance of filter-based iris recognition systems. The model fit is verified using the Shapiro-Wilk test for normality. We show that the loglikelihood ratio with well-estimated maximum-likelihood parameters in it often outperforms the average Hamming distance statistic. The problem of identification with M iris classes is further stated as an (M+1)ary hypothesis testing problem. We use empirical approach, Chernoff bound, and Large Deviations approach to predict the performance of the iris-based identification system. The bound on the probability of error is evaluated as a function of the number of classes and the number of iris scans per class.