Realtime gaze estimation with online calibration

For an eye gaze estimation system, calibration is an unavoidable procedure to determine certain person-specific parameters, either explicitly or implicitly. Although several offline implicit calibration methods have been proposed to ease the calibration burden, the calibration procedure is still cumbersome and the gaze estimation accuracy needs further improvement. In this paper, we propose a novel 3D gaze estimation system with online calibration. The proposed system uses a new 3D model-based gaze estimation method with a single consumer camera (Kinect). Unlike previous gaze estimation methods using explicit offline calibration with fixed number of calibration points or implicit calibration, our approach constantly improves person-specific eye parameters through online calibration, which enables the system to adapt gradually to a new user. The experimental results and the human-computer interaction (HCI) application show that the proposed system can work in realtime with superior gaze estimation accuracy (<; 2°) and minimal calibration burden.