论文标题
眼睛凝视估计模型分析
Eye Gaze Estimation Model Analysis
论文作者
论文摘要
我们探索使用机器学习的技术估算技术。眼目光估计是各种行为分析和人类计算机界面的常见问题。这项工作的目的是讨论各种模型类型，以进行眼目光估计，并通过在不受约束的环境中使用眼标的凝视方向来预测凝视方向的结果。在不受限制的现实环境中，由于照明变化和其他视觉伪影等因素，基于特征的方法和基于模型的方法的表现要胜过最新的基于外观的方法。我们讨论了一种基于学习的基于学习的方法，该方法专门针对合成数据培训。我们讨论了如何使用检测到的地标作为迭代模型拟合和轻巧学习的凝视估计方法的输入，以及如何将模型用于与人独立和个性化的凝视估计。
We explore techniques for eye gaze estimation using machine learning. Eye gaze estimation is a common problem for various behavior analysis and human-computer interfaces. The purpose of this work is to discuss various model types for eye gaze estimation and present the results from predicting gaze direction using eye landmarks in unconstrained settings. In unconstrained real-world settings, feature-based and model-based methods are outperformed by recent appearance-based methods due to factors like illumination changes and other visual artifacts. We discuss a learning-based method for eye region landmark localization trained exclusively on synthetic data. We discuss how to use detected landmarks as input to iterative model-fitting and lightweight learning-based gaze estimation methods and how to use the model for person-independent and personalized gaze estimations.
