Real-Time Gaze Estimation with Online Calibration

Gaze-tracking technology is highly valuable in many interactive and diagnostic applications. For many gaze estimation systems, calibration is an unavoidable procedure necessary to determine certain person-specific parameters, either explicitly or implicitly. Recently, several offline implicit calibration methods have been proposed to ease the calibration burden. However, the calibration procedure is still cumbersome, and gaze estimation accuracy needs further improvement. In this article, the authors present a novel 3D gaze estimation system with online calibration. The proposed system is based on a new 3D model-based gaze estimation method using a single consumer depth camera sensor (via Kinect). Unlike previous gaze estimation methods using explicit offline calibration with fixed number of calibration points or implicit calibration, their 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 real time with superior gaze estimation accuracy and minimal calibration burden.