Application of iris recognition for human identification has significant potential for developing a robust identification system. This is due to the fact that iris pattern of individuals are unique, differentiable from left to right eye and is almost stable over the time. However, performance of the existing iris recognition systems depends on the signal processing algorithms they use for iris segmentation, feature extraction and template matching. Like any other signal processing system, the performance of the iris recognition system is depend on the existing level of noise in the image and can be deteriorated as the level of noise increases. The building block of the iris recognition systems, techniques to mitigate the effect of the noise in each stages, criteria to assess the performance of different iris recognition techniques and publicly available iris datasets are discussed in this chapter.
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