BSOM network for pupil segmentation

Segmentation is a preliminary step in many computer vision systems. In most of pupil segmentation algorithms it is assumed that the pupil has a predefined shape, usually circular. This parametrization might lead to errors when the eye image is distorted or deformed and when the pupil is partially occluded by eyelids or eyelashes. In this work, we propose a new method for pupil segmentation based on a batch-SOM (BSOM) neural network composed by three steps: (1) definition of the initial neurons position; (2) use BSOM to extract the contour; and (3) perform a contour adjustment. The method is capable of finding the pupil contour in a flexible manner, independently of a predefined shape. We modified the BSOM algorithm in three points: (1) in the update process, introducing the neighborhood constraint; (2) removal of the neurons, and (3) in the convergence criteria. Experiments were performed using Casia-IrisV3 Interval, Casia-IrisV4 Syn, and MMU1 iris image databases.