A Probabilistic Approach to Online Eye Gaze Tracking Without Explicit Personal Calibration

• Published in: IEEE transactions on image processing Vol. 24; no. 3; pp. 1076 - 1086
• Main Authors: Chen, Jixu, Ji, Qiang
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.03.2015; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Animals; Bayes Theorem; Calibration; dynamic Bayesian network; Estimation; Fixation, Ocular - physiology; gaze calibration; Gaze estimation; Humans; Image Processing, Computer-Assisted - methods; Optical imaging; Probabilistic logic; Probability distribution; Three-dimensional displays; Visualization; gaze calibration; dynamic Bayesian network; Gaze estimation
• ISSN: 1057-7149; 1941-0042; 1941-0042
• DOI: 10.1109/TIP.2014.2383326

Existing eye gaze tracking systems typically require an explicit personal calibration process in order to estimate certain person-specific eye parameters. For natural human computer interaction, such a personal calibration is often inconvenient and unnatural. In this paper, we propose a new probabilistic eye gaze tracking system without explicit personal calibration. Unlike the conventional eye gaze tracking methods, which estimate the eye parameter deterministically using known gaze points, our approach estimates the probability distributions of the eye parameter and eye gaze. Using an incremental learning framework, the subject does not need personal calibration before using the system. His/her eye parameter estimation and gaze estimation can be improved gradually when he/she is naturally interacting with the system. The experimental result shows that the proposed system can achieve <;3° accuracy for different people without explicit personal calibration.