In this paper, we examine a novel image-filling algorithm to fill the occluded and partial regions of the iris. In iris recognition, these occlusions naturally appear due to eye-lids being partially closed, eye movements and eye-lashes. Additionally, specular reflections arising from the iris-camera illuminator are present and produce occlusion artifacts on the iris region. In the polar representation of iris images, additional 'nuisance' parameters include image regions containing the pupil and sclera due to inaccurate iris segmentations. Thus, for accurate iris recognition, it is important to reduce the effect of these nuisance parameters so that the performance of the iris recognition system can be maximized. Typically, a mask region (automatic or manual) is defined to ignore occluded or affected iris regions from such nuisance artifacts. However, in the feature extraction stage, especially using Gab or type extraction, the features near the edge of these artifacts are also affected due to the tail supports of these filters that overflow on occluded irises. We propose a novel method for filling in the occluded regions by synthesizing the iris pattern from this current exemplar image and show that a significant improvement on the Iris Challenge Evaluation (ICE) dataset can be made.