Abstract-A recent report from the National Institute of Standards and Technology (NIST) showed that changes in pupil dilation affect the performance of iris recognition algorithms. Hence, there is a need to explore the effects of pupil motion from a biological standpoint. Our work looks at the pupil's response to light, otherwise known as the pupil light reflex (PLR). By modeling the PLR using a nonlinear delay differential equation while considering images acquired in the near infrared (NIR) spectral band, we study both average and subject-specific pupil dilation effects. Experiments conducted on the WVU iris video dataset 1 convey the efficacy of our work in describing and evaluating pupillary response for both general and individual responses. The results of this work can be used to develop robust iris recognition algorithms that handle the effects of pupil dilation.
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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