Two-eye model-based gaze estimation from a Kinect sensor

Xiaolong Zhou, Haibin Cai, Youfu Li, Honghai Liu
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引用次数: 31

Abstract

In this paper, we present an effective and accurate gaze estimation method based on two-eye model of a subject with the tolerance of free head movement from a Kinect sensor. To accurately and efficiently determine the point of gaze, i) we employ two-eye model to improve the estimation accuracy; ii) we propose an improved convolution-based means of gradients method to localize the iris center in 3D space; iii) we present a new personal calibration method that only needs one calibration point. The method approximates the visual axis as a line from the iris center to the gaze point to determine the eyeball centers and the Kappa angles. The final point of gaze can be calculated by using the calibrated personal eye parameters. We experimentally evaluate the proposed gaze estimation method on eleven subjects. Experimental results demonstrate that our gaze estimation method has an average estimation accuracy around 1.99°, which outperforms many leading methods in the state-of-the-art.
基于双目模型的Kinect传感器注视估计
在本文中,我们提出了一种有效而准确的基于Kinect传感器的受试者双眼模型的注视估计方法,该模型具有头部自由运动的容忍度。为了准确高效地确定注视点,1)采用双眼模型提高估计精度;ii)提出了一种改进的基于卷积的梯度方法在三维空间中定位虹膜中心;Iii)提出了一种只需要一个定标点的个人定标方法。该方法将视轴近似为从虹膜中心到凝视点的一条线,以确定眼球中心和卡帕角。利用标定后的人眼参数计算最终注视点。我们在11个实验对象上对所提出的注视估计方法进行了实验评估。实验结果表明,我们的注视估计方法的平均估计精度在1.99°左右,优于目前许多领先的方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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