Toward Use of Facial Thermal Features in Dynamic Assessment of Affect and Arousal Level
Toward Use of Facial Thermal Features in Dynamic Assessment of Affect and Arousal Level
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DOI：
10.1109/taffc.2016.2535291
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发表时间：
2017-07
影响因子：
11.2
通讯作者：
M. Khan;R. Ward;M. Ingleby
中科院分区：
文献类型：
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作者：
M. Khan;R. Ward;M. Ingleby
情感和唤醒水平的自动评估可以帮助心理学家和精神病学家进行临床诊断;并可能实现情感感知的机器人-人类互动。这项工作确定了自动化的影响和唤醒评估的主要困难，并试图克服其中的一些。我们首先分析热红外图像，并研究如何影响和/或唤醒水平的变化会导致hædynamic变化，集中沿着某些面部肌肉。这些浓度用于测量情感/唤醒引起的面部热变化。在2步模式识别方案的步骤-1中，使用“情感之间”和“唤醒水平之间”变化来导出面部热特征作为面部热测量的主成分（PC）。这些PC中最具影响力的PC用于对不同影响的特征空间进行聚类，并随后将一组热特征分配给影响聚类。在步骤2中，情感聚类被划分为高、中和轻度唤醒水平。测试面部向量与从步骤-1识别的属于单个情感状态的三个唤醒水平处的子聚类的质心之间的距离用于确定所识别的情感状态的唤醒水平。
Automated assessment of affect and arousal level can help psychologists and psychiatrists in clinical diagnoses; and may enable affect-aware robot-human interaction. This work identifies major difficulties in automating affect and arousal assessment and attempts to overcome some of them. We first analyze thermal infrared images and examine how changes in affect and/or arousal level would cause hæmodynamic variations, concentrated along certain facial muscles. These concentrations are used to measure affect/arousal induced facial thermal variations. In step-1 of a 2-step pattern recognition schema, ‘between-affect’ and ‘between-arousal-level’ variations are used to derive facial thermal features as Principal Components (PCs) of the facial thermal measurements. The most influential of these PCs are used to cluster the feature space for different affects and subsequently assign a set of thermal features to an affect cluster. In step-2, affect clusters are partitioned into high, medium and mild arousal levels. The distance between a test face vector and the centroids of sub-clusters at three arousal levels belonging to a single affective state, identified from step-1, is used to determine the arousal level of the identified affective state.