A Probabilistic Approach to Online Eye Gaze Tracking Without Personal Calibration

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 cumbersome and unnatural. In this paper, we propose a new probabilistic eye gaze tracking system without explicit personal calibration. Unlike the traditional eye gaze tracking methods, which estimate the eye parameter deterministically, our approach estimates the probability distributions of the eye parameter and eye gaze. By using an incremental learning framework, the subject doesn’t 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 less than three degrees accuracy for different people without calibration.