Recent developments in the field of nonideal iris recognition have shown that the presence of the degradations such as insufficient contrast, unbalanced illumination, out-of-focus, motion blur, specular reflections, and partial area affect performance of iris recognition systems. Most iris recognition systems are designed to implement a number of processing steps with iris segmentation being one of the first steps. If segmentation is not performed at a certain precision, the error of segmentation will further propagate and will be amplified during the proceeding processing, encoding, and matching steps. This emphasizes a critical need in designing robust iris segmentation algorithms and together with it a need of automatic algorithms evaluating the precision (accuracy) of iris segmentation. Automatic algorithm evaluating the precision of segmentation plays important role for two reasons: (1) it can be placed into a feedback loop to enforce another run of segmentation algorithm that may include more sophisticated steps for high precision segmentation and (2) the outcome of this evaluation can be treated as a quality factor and thus can be used to design a quality driven adaptive iris recognition system. This work analyzes effects of degradations on iris segmentation and proposes and tests an automatic algorithm evaluating the precision of iris segmentation.
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