Challenging eye segmentation using Triplet Markov spatial models

We present a novel implementation of Triplet Markov Fields (TMF) for the unsupervised region segmentation of challenging eye images, representative of the iris recognition context. Results confirm the interest of such models over the classical Hidden Markov Field (HMF) and traditional gradient-based approaches for iris and periocular detection. We show that the precision of the resulting normalization circles is largely improved through the use of such TMF model as well as the quality of the image segmentation, despite of various degradations. These results are promising for further integration of TMF approaches in iris verification systems.