A Regression-Based User Calibration Framework for Real-Time Gaze Estimation
Eye movements play a very significant role in human–computer interaction (HCI) as they are natural and fast, and contain important cues for human cognitive state and visual attention. Over the last two decades, many techniques have been proposed to accurately estimate the gaze. Among these, video-based remote eye trackers have attracted much interest, since they enable nonintrusive gaze estimation. To achieve high estimation accuracies for remote systems, user calibration is inevitable in order to compensate for the estimation bias caused by person-specific eye parameters. Although several explicit and implicit user calibration methods have been proposed to ease the calibration burden, the procedure is still cumbersome and needs further improvement. In this paper, we present a comprehensive analysis of regression-based user calibration techniques. We propose a novel weighted least squares regression-based user calibration method together with a real-time cross-ratio based gaze estimation framework. The proposed system enables to obtain high estimation accuracy with minimum user effort, which leads to user-friendly HCI applications. Experimental results conducted on both simulations and user experiments show that our framework achieves a significant performance improvement over the state-of-the-art user calibration methods when only a few points are available for the calibration.