Model-based eye tracking has been a dominant approach for eye gaze tracking because of its ability to generalize to different subjects, without the need of any training data and eye gaze annotations. Model-based eye tracking, however, is susceptible to eye feature detection errors, in particular for eye tracking in the wild. To address this issue, we propose a Bayesian framework for model-based eye tracking. The proposed system consists of a cascade-Bayesian Convolutional Neural Network (c-BCNN) to capture the probabilistic relationships between eye appearance and its landmarks, and a geometric eye model to estimate eye gaze from the eye landmarks. Given a testing eye image, the Bayesian framework can generate, through Bayesian inference, the eye gaze distribution without explicit landmark detection and model training, based on which it not only estimates the most likely eye gaze but also its uncertainty. Furthermore, with Bayesian inference instead of point-based inference, our model can not only generalize better to different sub-jects, head poses, and environments but also is robust to image noise and landmark detection errors. Finally, with the estimated gaze uncertainty, we can construct a cascade architecture that allows us to progressively improve gaze estimation accuracy. Compared to state-of-the-art model-based and learning-based methods, the proposed Bayesian framework demonstrates significant improvement in generalization capability across several benchmark datasets and in accuracy and robustness under challenging real-world conditions.

翻译：以模型为基础的眼睛跟踪是眼视跟踪的主要方法,因为它能够推广到不同的主题,而不需要任何培训数据和眼视说明。但是,以模型为基础的眼睛跟踪容易出现眼睛特征检测错误,特别是野生眼睛跟踪。为了解决这个问题,我们提议建立贝叶斯框架,用于以模型为基础的眼睛跟踪。拟议的系统包括级联-拜耶色共进神经网络(c-BCNN),以捕捉眼貌及其标志性之间的概率关系,以及从眼界标志性的角度估计眼睛眼视的几何性眼睛模型。根据测试性眼象图像,贝伊西亚框架可以通过贝伊斯推断产生眼睛眼睛的分布,而没有明确的里程碑性检测和模型培训,根据这一框架,我们不仅估计最可能的眼睛视力,而且其不确定性。此外,由于贝伊斯推论而不是点性的推断,我们的模型不仅能够更好地概括不同的子点、头部和环境,而且能够准确地估计图像的噪音和标志性检测错误。最后,在估计的准确性方面,我们可以通过估计的准确性来逐步地评估基准性,从而推算出一种基于结构的精确性,从而推估测测测测测测测测的系统。