Open Access

ARTICLE

A Pupil-Positioning Method Based on the Starburst Model

Pingping Yu¹, Wenjie Duan¹, Yi Sun², Ning Cao^{3, *}, Zhenzhou Wang¹, Guojun Lu⁴

1 School of Information Science and Engineering, Hebei University of Science and Technology, Shijiazhuang, 050000, China.
2 Hebei Electric Power Research Institute, Shijiazhuang, 050022, China.
3 School of Internet of Things and Software Technology, Wuxi Vocational College of Science and Technology, Wuxi, 214028, China.
4 School of Science, Engineering and IT, Federation University Australia, Gippsland Campus, Churchill, Australia.

* Corresponding Author: Ning Cao. Email: .

Computers, Materials & Continua 2020, 64(2), 1199-1217. https://doi.org/10.32604/cmc.2020.010384

Received 01 March 2020; Accepted 17 April 2020; Issue published 10 June 2020

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.

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