An Automatic Algorithm for Evaluating the Precision of Iris Segmentation

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.