Pupil localization algorithm combining convex area voting and model constraint

Abstract

Locating the center of the eyes plays a significant role in many computer vision applications and research, such as face alignment, face recognition, human-computer interaction, control devices for disabled people, user attention and gaze estimation. The disturbances such as occlusions by eyelashes or eyelids, uneven spots and spectacle frames of glasses affect the accuracy and stability of eye center location. This paper presents a hybrid eye center locating methodology for infrared eye images. The pupil edge points are extracted by Starburst algorithm, and when we get the position and the gradient of the edge points, the approximate pupil boundary is determined by a convex region voting methods. After that, the boundary edge points are iteratively optimized by fitting an ellipses modeling constraint. Finally, the pupil is located correctly. Experiment shows that this algorithm has performance advantages compared with some state of the art approaches in pupil localization accuracy, iteration times and their performance. This algorithm combining convex area voting and model constraint has strong robustness, high accuracy and speed in real environments with occlusions and distortion pupil.