Robust and accurate eye-gaze tracking and its applications

Abstract

First, existing eye-gaze tracking systems typically require an explicit personal calibration that not only degrades the user experience, but also makes it difficult to perform natural eye-gaze tracking. To eliminate this requirement, we introduce a novel approach that combines a top-down saliency map with a bottom-up gaze distribution map. By minimizing the KL-divergence between the two maps, personal calibration can be implicitly performed without the user's explicit collaboration. Next, we further eliminate the usage of saliency map by leveraging several constraints during natural eye-gaze tracking to estimate the personal eye parameters.