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Eye gaze estimation from the elliptical features of one iris

机译:从一个虹膜的椭圆形特征估计眼睛视线

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摘要

The accuracy of eye gaze estimation using image information is affected by several factors which include image resolution, anatomical structure of the eye, and posture changes. The irregular movements of the head and eye create issues that are currently being researched to enable better use of this key technology. In this paper, we describe an effective way of estimating eye gaze from the elliptical features of one iris under the conditions of not using an auxiliary light source, a head fixing equip-ment, or multiple cameras. First, we provide preliminary estimation of the gaze direction, and then we obtain the vectors which describe the translation and rotation of the eyeball, by applying a central projection method on the plane which passes through the line-of-sight. This helps us avoid the complex computations involved in previous methods. We also disambiguate the solution based on experimental findings. Second, error correction is conducted on a back propagation neural network trained by a sample collection of translation and rotation vectors. Extensive exper-imental studies are conducted to assess the efficiency, and robustness of our method. Results reveal that our method has a better performance compared to a typical previous method.
机译:使用图像信息进行视线估计的准确性受多个因素的影响,这些因素包括图像分辨率,眼睛的解剖结构和姿势变化。头部和眼睛的不规则运动会产生一些问题,目前正在研究这些问题,以便更好地使用此关键技术。在本文中,我们描述了一种在不使用辅助光源,头部固定设备或多个摄像头的条件下,根据一个虹膜的椭圆形特征估计眼睛凝视的有效方法。首先,我们提供对视线方向的初步估计,然后,通过在通过视线的平面上应用中心投影方法,获得描述眼球平移和旋转的向量。这有助于我们避免先前方法中涉及的复杂计算。我们还根据实验结果消除了歧义。其次,在由平移和旋转矢量的样本集合训练的反向传播神经网络上进行纠错。进行了大量的实验研究,以评估我们方法的效率和鲁棒性。结果表明,与典型的先前方法相比,我们的方法具有更好的性能。

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