论文标题
认识论精神状态的强大建模
Robust Modeling of Epistemic Mental States
论文作者
论文摘要
这项工作确定并提出了一些研究挑战，分析面部特征及其时间动态，并在二元对话中具有认知性精神状态。认知状态是：一致，集中，周到，确定和利益。在本文中，我们进行了许多统计分析和模拟，以确定面部特征与认知状态之间的关系。发现非线性关系更为普遍，而从原始面部特征得出的时间特征表现出与强度变化的密切相关性。然后，我们提出了一个新颖的预测框架，该框架将面部特征及其非线性关系得分作为输入，并预测视频中不同的认知状态。当将情绪变化区域（例如上升，下降或稳态）分类与时间特征相结合时，对认知状态的预测将得到提高。提出的预测模型可以以明显提高的精度预测认知状态：一致性的相关系数（COERR）为0.827，浓度为0.901，对于某些0.854，浓度为0.794，并为0.913。
This work identifies and advances some research challenges in the analysis of facial features and their temporal dynamics with epistemic mental states in dyadic conversations. Epistemic states are: Agreement, Concentration, Thoughtful, Certain, and Interest. In this paper, we perform a number of statistical analyses and simulations to identify the relationship between facial features and epistemic states. Non-linear relations are found to be more prevalent, while temporal features derived from original facial features have demonstrated a strong correlation with intensity changes. Then, we propose a novel prediction framework that takes facial features and their nonlinear relation scores as input and predict different epistemic states in videos. The prediction of epistemic states is boosted when the classification of emotion changing regions such as rising, falling, or steady-state are incorporated with the temporal features. The proposed predictive models can predict the epistemic states with significantly improved accuracy: correlation coefficient (CoERR) for Agreement is 0.827, for Concentration 0.901, for Thoughtful 0.794, for Certain 0.854, and for Interest 0.913.
