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Due to the less constrained setup in standoff iris recognition systems, it is likely to capture nonideal iris images with gaze angle, pupil dilation, reflections, and occlusions. The combined effect of pupil dilation and gaze angle on iris recognition is examined. We first highlight the effects on synthetic images generated with a biometric eye model using a ray-tracing algorithm. Then, we quantify the effects of pupil dilation and gaze angle on the real frontal and off-angle images at different dilation levels. Our experiments reveal that the larger differences in dilation levels and gaze angles between the compared iris images increase the Hamming distance. Even if the linear rubber-sheet normalization helps to minimize the dilation effect in frontal images, it cannot fully eliminate it in off-angle iris images because of not only the pupil dilation and three-dimensional iris texture but also the corneal refraction distortion and limbus occlusion. We also observe that the gaze angle is the main reason for the performance degradation in steeper off-angle images, where the effect of the dilation is limited. In addition, since the iris region in off-angle and dilated iris images is smaller than that in frontal and constricted iris images, their interclass Hamming distance distribution is shifted toward the intraclass distribution, which may increase the false match rate.

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