@Article{cmc.2020.010384,
AUTHOR = {Pingping Yu, Wenjie Duan, Yi Sun, Ning Cao, Zhenzhou Wang, Guojun Lu},
TITLE = {A Pupil-Positioning Method Based on the Starburst Model},
JOURNAL = {Computers, Materials \& Continua},
VOLUME = {64},
YEAR = {2020},
NUMBER = {2},
PAGES = {1199--1217},
URL = {http://www.techscience.com/cmc/v64n2/39355},
ISSN = {1546-2226},
ABSTRACT = {Human eye detection has become an area of interest in the field of computer 
vision with an extensive range of applications in human-computer interaction, disease 
diagnosis, and psychological and physiological studies. Gaze-tracking systems are an 
important research topic in the human-computer interaction field. As one of the core 
modules of the head-mounted gaze-tracking system, pupil positioning affects the 
accuracy and stability of the system. By tracking eye movements to better locate the 
center of the pupil, this paper proposes a method for pupil positioning based on the 
starburst model. The method uses vertical and horizontal coordinate integral projections 
in the rectangular region of the human eye for accurate positioning and applies a linear 
interpolation method that is based on a circular model to the reflections in the human eye. 
In this paper, we propose a method for detecting the feature points of the pupil edge 
based on the starburst model, which clusters feature points and uses the RANdom 
SAmple Consensus (RANSAC) algorithm to perform ellipse fitting of the pupil edge to 
accurately locate the pupil center. Our experimental results show that the algorithm has 
higher precision, higher efficiency and more robustness than other algorithms and 
excellent accuracy even when the image of the pupil is incomplete.},
DOI = {10.32604/cmc.2020.010384}
}