认知心理状态的鲁棒建模
Robust Modeling of Epistemic Mental States
摘要：
这项工作确定并提出了一些研究的挑战，在分析面部特征及其时间动态与认知心理状态的二元对话。认知状态是：一致、专注、理解、确定和兴趣。在本文中，我们进行了一些统计分析和模拟，以确定面部特征和认知状态之间的关系。非线性关系被发现是更普遍的，而来自原始面部特征的时间特征已被证明与强度变化有很强的相关性。然后，我们提出了一种新的预测框架，以人脸特征及其非线性关系得分为输入，预测视频中的不同认知状态。当情绪变化区域如上升、下降或稳态的分类与时间特征相结合时，认知状态的预测得到提升。所提出的预测模型可以预测的认知状态显着提高的准确性：相关系数（CoERR）协议为0.827，浓度为0.901，清晰度为0.794，某些0.854，和兴趣0.913。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
