Pupil-Contour-Based Gaze Estimation With Real Pupil Axes for Head-Mounted Eye Tracking
Zhonghua Wan, Caihua Xiong, Wenbin Chen, Hanyuan Zhang, Shiqian Wu
Abstract
Accurate gaze estimation that frees from glints and the slippage problem is challenging. Pupil-contour-based gaze estimation methods can meet this challenge, except that the gaze accuracy is low due to neglecting the pupil’s corneal refraction This article proposes a refraction-aware gaze estimation approach using the real pupil axis, which is calculated from the virtual pupil image based on the derived function between the real pupil and the refracted virtual pupil. We present a 2-D gaze estimation method that regresses the real pupil normal’s spherical coordinates to the gaze point. The noise and outliers of calibration data are removed by aggregation filtering and random sample consensus, respectively. Moreover, we propose a 3-D gaze estimation method that transforms the real pupil axis to the gaze direction. Experimental results show that the proposed gaze estimation approach has comparable accuracy to state-of-the-art pupil-center-based gaze estimation methods, which suffer from the slippage problem.