使用单张图像和散焦深度进行无标记眼动估计
Marker-free eye-gaze estimation using a single image and depth from defocus
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中文总结 AI 辅助
本文提出一种基于单目摄像头和散焦深度的无标记眼动估计方法，利用虹膜定位与头部姿态特征，通过变分贝叶斯多项逻辑回归映射到注视位置，实验验证其有效性。
中文摘要 AI 辅助
本文提出了一种使用单个2D摄像头（如笔记本电脑内置摄像头）的无标记眼动估计方法。通过虹膜定位和利用散焦深度估计的头部姿态来估计与注视相关的特征。采用变分贝叶斯多项逻辑回归框架，基于头部姿态和虹膜位移参数的8维特征向量，将估计的特征映射到注视位置。无需外部标记。实验通过估计不同距离观看电脑屏幕的人的注视方向进行，并与五种现有方法进行了比较。获得的分数证明了所提方法的有效性。
英文摘要
This paper presents a marker-free eye-gaze estimation approach using a single 2D camera, such as an integrated laptop webcam. The gaze-related features are estimated from iris localization and head pose estimated by using depth from defocus. A variational Bayesian multinomial logistic regression framework is used as mapping from the estimated features to the position of regard, based on an 8-dimensional feature vector of head-pose and iris-displacement parameters. No external marker is needed. Experiments were conducted by estimating the gaze of people watching a computer screen at different distances and compared against five existing methods. The obtained scores demonstrate the effectiveness of the proposed approach.
发表机构
- Université de Sherbrooke（舍布鲁克大学）
机构由 AI 辅助整理，请以论文原文为准。
