用人脸动态实时预测学生注意力，无需参考组数据。
Real-time estimation of overt attention from dynamic features of the face using deep-learning
- 基于单个学生的面部动作训练模型预测注意力
- 在未见数据上达到R²=0.38，跨被试达R²=0.26-0.30
- 轻量级模型可本地运行，适合隐私敏感场景
学生上课时常分心。传统教学中教师可通过视觉反馈及时干预，但远程学习中这一能力缺失。本文提出利用前置摄像头视频，通过分析眼动、头部和面部动态来推断注意力水平。具体采用十秒窗口内学生观看相同教育视频时的跨被试眼动相关性（Inter-Subject Correlation, ISC）作为注意力指标。在三个实验（N=83）中，训练好的深度学习模型在未见数据上对注意力的预测准确率为R²=0.38，跨被试预测也达到R²=0.26-0.30。模型主要依赖眼动信息，同时部分受眉部、面颊和头部动作影响。与传统依赖参照组的方法不同，该模型仅需个体自身动态即可估计注意力，支持更广泛在线应用。系统轻量，可在客户端本地运行，缓解隐私顾虑。代码已开源：https://github.com/asortubay/timeISC。
原文摘要 · Abstract (English)
Students often drift in and out of focus during class. Effective teachers recognize this and re-engage them when necessary. With the shift to remote learning, teachers have lost the visual feedback needed to adapt to varying student engagement. We propose using readily available front-facing video to infer attention levels based on movements of the eyes, head, and face. We train a deep learning model to predict a measure of attention based on overt eye movements. Specifically, we measure Inter-Subject Correlation of eye movements in ten-second intervals while students watch the same educational videos. In 3 different experiments (N=83) we show that the trained model predicts this objective metric of attention on unseen data with $R^2$=0.38, and on unseen subjects with $R^2$=0.26-0.30. The deep network relies mostly on a student's eye movements, but to some extent also on movements of the brows, cheeks, and head. In contrast to Inter-Subject Correlation of the eyes, the model can estimate attentional engagement from individual students' movements without needing reference data from an attentive group. This enables a much broader set of online applications. The solution is lightweight and can operate on the client side, which mitigates some of the privacy concerns associated with online attention monitoring. GitHub implementation is available at https://github.com/asortubay/timeISC
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