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The richness and apparent stability of the iris texture make it a robust biometric trait for personal authentication. The performance of an automated iris recognition system is affected by the accuracy of the segmentation process used to localize the iris structure. Most segmentation models in the literature assume that the pupillary, limbic, and eyelid boundaries are circular or elliptical in shape. Hence, they focus on determining model parameters that best fit these hypotheses. However, it is difficult to segment iris images acquired under nonideal conditions using such conic models. In this paper, we describe a novel iris segmentation scheme employing geodesic active contours (GACs) to extract the iris from the surrounding structures. Since active contours can 1) assume any shape and 2) segment multiple objects simultaneously, they mitigate some of the concerns associated with traditional iris segmentation models. The proposed scheme elicits the iris texture in an iterative fashion and is guided by both local and global properties of the image. The matching accuracy of an iris recognition system is observed to improve upon application of the proposed segmentation algorithm. Experimental results on the CASIA v3.0 and WVU nonideal iris databases indicate the efficacy of the proposed technique.