A pixel-wise, learning-based approach for occlusion estimation of iris images in polar domain

On normalized iris images, there are many kinds of noises, such as eyelids, eyelashes, shadows or specular reflections, that often occlude the true iris texture. If high recognition rate is desired, those occluded areas must be estimated accurately in order for them to be excluded during the matching stage. In this paper, we propose a unified, probabilistic and learning-based approach to estimate all kinds of occlusions within one unified model. Experiments have shown that our method not only estimates occlusion very accurately, but also does it with high speed, which makes it useful for practical iris recognition systems.