3D Gaze Estimation using Eye Vergence

Abstract

We propose a fast and robust method to estimate the 3D gaze position based on the eye vergence information extracted from eye-tracking data. This method is specially designed for Point-of-Regard (PoR) estimation in non-virtual environments with the aim to make it applicable to the study of human visual attention deployment in natural scenarios. Our approach starts with a calibration step at different depth distances in order to achieve the best depth approximation. In addition, we investigate the distance range, for which state-of-the-art eyetracking technology allows 3D gaze estimation based on eye vergence. Our method provides a mean accuracy of 1.2◦ at a working distance between 200 mm and 400 mm from the user without requiring calibrated lights or cameras.