A Pupil-Positioning Method Based on the Starburst Model

Pingping Yu,Wenjie Duan,Ning Cao,Zhenzhou Wang,Yi Sun,Guojun Lu

doi:10.32604/cmc.2020.010384

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.

Highlights

With the rapid development of virtual reality (VR) technology, an increasing number of VR products exist on the market; optimal solutions for human-computer interaction and dizziness caused by the use of VR devices have not been obtained
6 Conclusions A pupil-positioning algorithm, which is based on the starburst model and high-density connected region clustering, is proposed in this paper
We detect reflections on the eyeball and fill them using linear interpolation based on circular models

Summary

Introduction

With the rapid development of virtual reality (VR) technology, an increasing number of VR products exist on the market; optimal solutions for human-computer interaction and dizziness caused by the use of VR devices have not been obtained. One of the main reasons is that VR devices encounter difficulties when responding to of the human head in a timely manner. Eye movement is one of the fastest movements of the human body. Applying eye tracking technology to monitor eye movement in real time will help solve problems associated with VR devices. Eye feature extraction is an important component of eye tracking technology.

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