A robust iris localization and tracking algorithm based on computer vision is presented. The iris localization algorithm acts as a bootstrap for the tracking algorithm, providing it with a set of multiple hypotheses to restart from in the case of a tracking failure. Tracking is performed with a RANSAC-like robust method for ellipse fitting that incorporates search constraints so as to increase the overall accuracy with respect to the standard RANSAC approach. Experimental results show that the algorithm is fast, accurate and robust enough for applications in the field of human-machine interaction, being particularly suitable for users with severe motor disabilities.