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A robust eye gaze estimation using geometric eye features

机译:使用几何眼特征的强大眼睛凝视估计

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摘要

Gaze estimation is the process of determining the point of gaze in the space, or the visual axis of an eye. It plays an important role in representing human attention; therefore, it can be most appropriately used in Human Computer Interaction as a means of an advance computer input. Here, the focus is to develop a gaze estimation method for Human Computer Interaction using an ordinary webcam mounted on the top of the computer screen without any additional or specialized hardware. The eye center coordinates are obtained with the geometrical eye model and edge gradients. To improve the reliability, the estimates from two eye centers are combined to reduce the noise and improve the accuracy. Facial land marking is done to identify a precise reference point on the face between the nose. The ellipse fitting and RANSAC method is used to estimate the gaze coordinates and to reject the outliers. This approach can estimate the gaze coordinates with high degree of accuracy even when significant numbers of outliers are present in the data set. Several refinements such as feedback and masking, queuing and averaging are proposed to make the system more stable and useful practically. The results show that the proposed method can be successfully applied to commercial gaze tracking systems using ordinary webcams.
机译:凝视估计是确定空间中的凝视点的过程,或眼睛的视觉轴线。它在代表人类注意中起着重要作用;因此,可以在人机交互中最适当地使用,作为提前计算机输入的手段。这里,焦点是使用安装在计算机屏幕顶部的普通网络摄像头的人机交互来开发凝视估计方法,而无需任何额外的或专用的硬件。眼中心坐标是用几何眼模型和边缘梯度获得的。为了提高可靠性,两个眼中心的估计结合以降低噪音并提高准确性。面部土地标记是为了识别鼻子之间的面部的精确参考点。椭圆拟合和RANSAC方法用于估计凝视坐标并拒绝异常值。即使数据集中存在大量的异常值,这种方法也可以通过高精度估计凝视坐标。提出了几种改进,例如反馈和掩蔽,排队和平均,使系统更稳定并且实际上有用。结果表明,该方法可以成功应用于使用普通网络摄像头的商业凝视跟踪系统。

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