The impact of pupil dilation on the matching accuracy of iris recognition algorithms has been demonstrated in the biometrics literature. However, the current literature does not model the various states of pupil behavior with respect to the underlying dynamics. Consequently, most existing work on this topic is empirical in nature. Our work uses concepts of transition processes and limiting distributions to describe the relationship between the state of the input iris image and a countable number of enrolled dilation states from an iris recognition standpoint. We also investigate a special case where a closed form expression is obtained that directly relates the various states to overall pupil behavior. Numerical evaluations demonstrate the feasibility of our proposed model where the results of our work can be directly used by iris recognition algorithms to account for pupil dilation artifacts.
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