FreeGaze:使用面部视频中的外观线索进行3D目光估计的框架
Shang Tian1,2, Haiyan Tu1,2, Ling He3
1College of Electrical Engineering, Sichuan University, Chengdu 610065, China.
Sensors (Basel, Switzerland)
|December 9, 2023
概括
这项研究引入了一个新的框架,用于从面部视频中估计3D目光. FG-Net模型实现了高精度和效率,通过凝视跟踪推进了注意力分析领域.
科学领域:
- 计算机视觉 计算机视觉
- 人与计算机的交互
- 行为神经科学 行为神经科学
背景情况:
- 凝视行为是人类注意力的关键指标.
- 从面部视频中估计目光是具有挑战性的,因为外观和头部姿势的变化.
- 精确的目光估计在各种领域都有应用,包括用户体验研究和辅助技术.
研究的目的:
- 开发一个强大的框架,用于使用外观线索进行3D目光估计.
- 提高视线估计方法的准确性和计算效率.
- 为此任务引入和评估一种新的双分支卷积神经网络 (FG-Net).
主要方法:
- 一个端到端的面部地标检测方法.
- 使用直角矩阵进行改进的规范化方法,以提高准确性和减少计算时间.
- 一个双分支卷积神经网络 (FG-Net) 整合眼睛和面部特征用于3D视线向量估计.
主要成果:
- 与现有方法相比,改进的规范化方法显示出更高的准确性和更短的计算时间.
- 在MPIIGaze数据集上,FG-Net获得了3.11°的精度,在EyeDiap数据集上达到2.75°的精度.
- 十次交叉验证证实了框架的有效性和最先进的性能.
结论:
- 拟议的框架为3D目光估计提供了一个高度有效的解决方案.
- FG-Net代表了从面部视频中准确估计目光方向的重大进步.
- 这项研究强调了基于外表的线索和深度学习的潜力,用于复杂的目光分析.
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