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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.