Frustratingly Easy Personalization for Real-time Affect Interpretation of Facial Expression
Frustratingly Easy Personalization for Real-time Affect Interpretation of Facial Expression
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DOI：
10.1109/acii.2019.8925515
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发表时间：
2019-09
期刊：
影响因子：
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通讯作者：
Samuel Spaulding;C. Breazeal
中科院分区：
文献类型：
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作者：
Samuel Spaulding;C. Breazeal
近年来，研究人员已经开发出以非常高的时间分辨率分析人类面部表情和其他情感数据的技术。这项技术使研究人员能够开发和研究对人类互动伙伴的情感状态越来越敏感的互动机器人。然而，典型的交互规划模型和算法在时间尺度上操作，所述时间尺度通常比感测实时影响数据的时间尺度大几个数量级。为了弥合传感器数据收集和交互建模之间的差距，情感数据必须在更长的时间尺度上进行聚合和解释。在本文中，我们澄清和形式化的计算任务的影响解释的背景下，由一个人和一个机器人，在此期间，面部表情数据的感知，解释，并用于预测互动伙伴的游戏行为的互动教育游戏。我们比较了不同的技术，影响解释，用于生成一组情感标签的交互式建模和推理任务，并评估如何由每个解释技术产生的标签影响模型训练和推理。我们表明，将一个简单的方法的个性化的影响解释过程-动态计算和应用个性化的阈值，以确定随着时间的推移的影响特征标签-导致推理的质量显着改善，从其他数据预处理步骤，如通过中值滤波平滑数据的性能增益。我们讨论了这些研究结果对未来发展的影响意识到互动机器人和使用的影响解释方法在互动的情况下提出了指导方针。
In recent years, researchers have developed technology to analyze human facial expressions and other affective data at very high time resolution. This technology is enabling researchers to develop and study interactive robots that are increasingly sensitive to their human interaction partners' affective states. However, typical interaction planning models and algorithms operate on timescales that are frequently orders of magnitude larger than the timescales at which real-time affect data is sensed. To bridge this gap between the scales of sensor data collection and interaction modeling, affective data must be aggregated and interpreted over longer timescales. In this paper we clarify and formalize the computational task of affect interpretation in the context of an interactive educational game played by a human and a robot, during which facial expression data is sensed, interpreted, and used to predict the interaction partner's gameplay behavior. We compare different techniques for affect interpretation, used to generate sets of affective labels for an interactive modeling and inference task, and evaluate how the labels generated by each interpretation technique impact model training and inference. We show that incorporating a simple method of personalization into the affect interpretation process - dynamically calculating and applying a personalized threshold for determining affect feature labels over time - leads to a significant improvement in the quality of inference, comparable to performance gains from other data pre-processing steps such as smoothing data via median filter. We discuss the implications of these findings for future development of affect-aware interactive robots and propose guidelines for the use of affect interpretation methods in interactive scenarios.