Eye-wearable head-mounted tracking and gaze estimation interactive machine system for human–machine interface. Issue 1 (March 2021)

• Record Type: Journal Article
• Authors: Lee, Ko-Feng; Chen, Yen-Lin; Yu, Chao-Wei; Jen, Cheng-Lung; Chin, Kai-Yi; Hung, Chen-Wei; Wen, Chih-Bo
• Is Part Of: Journal of low frequency noise, vibration, and active control. Volume 40:Issue 1(2021)
• Page Start: 18
• Page End: 38
• Publication Date: 2021-03
• Subjects: Eye tracking -- gaze estimation -- machine vision -- head-mounted -- human–machine interface; Vibration -- Periodicals; Noise -- Periodicals; Sound -- Periodicals; Damping (Mechanics) -- Periodicals; Damping (Mechanics); Noise; Sound; Vibration; Periodicals; 620.205
• DOI: 10.1177/1461348419875047
• Languages: English
• ISSNs: 1461-3484

Abstract

In this study, a head-mounted camera was used to track eye behaviors and estimate the gaze point on the user's visual plane. The integration of the elastic mechanism design makes the headset adaptable for various users. The wearable cases were prototyped with low-cost cameras to produce an efficient eye tracking solution. This proposed system can effectively extract and estimate pupil ellipse from a few camera images of an eye and compute the corresponding three-dimensional eye model. The system can match later images of the same pupil ellipse from a head-mounted camera to give the possible visual angles. To estimate the gaze point, the system uses multiple-point calibration to solve the related polynomial formula for future angle-to-gaze mapping. The proposed eye-tracking algorithms can provide a low-complexity solution with high accuracy, precision, and speed. This tracking system is a low-cost and promising system that can be used in headsets for virtual reality, auxiliary equipment, interactive machine, and human–machine interface applications. The proposed eye-tracking algorithm can achieve satisfactory performance without using a high-end high-speed camera and can be detected under different lighting sources, and the average errors of the detection results are stably within 9 pixels and at a distance of 50 cm from the screen; while the average error of the fixation mapping results is within 3°.