"The rich texture of the iris is being used as a biometric cue in several human recognition systems. Iris recognition systems are fairly robust to small changes in illumination and pose. However there are a number of factors that still adversely affect the performance of an iris matcher. These include occlusion, large deviation in gaze, low image resolution, long acquisition distance and pupil dilation. Large differences in pupil size increases the dissimilarity between iris images of the same eye. In this work, the degradation of match scores due to pupil dilation is systematically studied using Hamming Distance histograms. A novel rule-based fusion technique based on the aforementioned study is proposed to alleviate the effect of pupil dilation. The proposed method computes a new distance score at every pixel location based on the similarities between IrisCode bits that were generated using Gabor Filters at different resolutions. Experiments show that the proposed method increases the genuine accept rate from 76% to 90% at 0.0001% false accept rate when comparing images with large differences in pupil sizes in the WVU-PLR dataset. The proposed method is also shown to improve the performance of iris recognition on other non-ideal iris datasets. In summary, the use of multi-resolution Gabor Filters in conjunction with a rule-based integration of decisions at the pixel (bit) level is observed to improve the resilience of iris recognition to differences in pupil size."--Page ii.
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