Sclera segmentation for gaze estimation and iris localization in unconstrained images

Accurate localization of different eye's parts from videos or still images is a crucial step in many image processing applications that range from iris recognition in Biometrics to gaze estimation for Human Computer Interaction (HCI), impaired people aid or, even, marketing analysis for products attractiveness. Notwithstanding this, actually, most of available implementations for eye's parts segmentation are quite invasive, imposing a set of constraints both on the environment and on the user itself limiting their applicability to high security Biometrics or to cumbersome interfaces. In this paper we propose a novel approach to segment the sclera, the white part of the eye. We concentrated on this area since, thanks to the dissimilarity with other eye's parts, its identification can be performed in a robust way against light variations, reflections and glasses lens flare. An accurate sclera segmentation is a fundamental step in iris and pupil localization with respect to the eyeball center and to its relative rotation with respect to the head orientation. Once the sclera is correctly defined, iris, pupil and relative eyeball rotation can be found with high accuracy even in non-frontal noisy images. Furthermore its particular geometry, resembling in most of cases a triangle with bent sides, both on the left and on the right of the iris, can be fruitfully used for accurate eyeball rotation estimation. The proposed technique is based on a statistical approach (supported by some heuristic assumptions) to extract discriminating descriptors for sclera and non-sclera pixels. A Support Vector Machine (SVM) is then used as a final supervised classifier. © 2012 Taylor & Francis Group.