Iris segmentation is driven by three different quality factors: accuracy, usability and speed. Unfortunately the deeply analysis of the literature shows that the greatest efforts of the researchers mainly focus on accuracy and speed. Proposed solutions, in fact, do not meet the usability requirement since they are based on specific optimizations related to the operating context and they impose binding conditions on the sensors to be used for the acquisition of periocular images. This paper tries to fill this gap by introducing an innovative iris segmentation technique that can be used in unconstrained environments, under non-ideal imaging conditions and, above all, that does not require any interaction for adaptation to different operating conditions. Experimental results, carried out on challenging databases, demonstrate that the high usability of the proposed solution does not penalize segmentation accuracy which, in some respects, outperforms that of the leading approaches in the literature.