Calibration-free gaze tracking using particle filter

This paper presents a novel approach for gaze estimation using only a low-cost camera and requiring no calibration. The main idea is based on the center-bias property of human gaze distribution to get a coarse estimate of the current gaze position as well as benefit from temporal information to enhance this rough gaze estimate. Firstly, we propose a method for detecting the eye center location and a mapping model based on the center-bias effect to convert it to gaze position. This initial gaze estimate then serves to construct the likelihood model of the eye-appearance. The final gaze position is estimated by fusing the likelihood model with the prior information obtained from previous observations on the basis of the particle filtering framework. Extensive experiments demonstrate the good performance of the proposed system with an average estimation error of 3.43° which outperforms state-of-the-art methods. Furthermore, the low complexity of the proposed system makes it suitable for real-time applications.