Alone versus In-a-group: A Comparative Analysis of Facial Affect Recognition
Alone versus In-a-group: A Comparative Analysis of Facial Affect Recognition
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单独与团体：面部表情识别的比较分析
DOI：
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发表时间：
2016
期刊：
影响因子：
--
通讯作者：
I. Patras
中科院分区：
文献类型：
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作者：
Wenxuan Mou;H. Gunes;I. Patras
在过去的二十年里，自动情感分析和理解已经成为一个成熟的研究领域。最近的作品已经开始从个人场景转移到群体场景。然而，很少有人注意比较个体和群体环境中表现出来的影响。本文提出了一个框架来考察个体和群体环境中情绪识别模型在唤醒维度和效价维度上的差异。我们分析了基于从个人设置收集的数据训练的模型如何在从组设置收集的测试数据上执行，反之亦然。第三种模型结合了来自个人和群体设置的数据也被研究了。在两个新收集的数据库上进行了一组实验，分别从唤醒和效价两个维度预测情绪状态，该数据库包含16名在个人和群体环境中观看情感电影刺激的参与者。实验结果表明：(1)用群体数据训练的情感模型在个体测试数据上比用个体数据训练的模型在个体测试数据上表现得更好，表明在群体环境中表达的面部行为比在个体环境中表现出的变化更多；(2)组合模型没有表现出比用特定类型的数据(即个体或群体)训练的情感模型更好的性能，但证明了一种很好的折衷方案。这些结果表明，在用不同类型的数据训练的多个情感模型不可用的情况下，使用用组数据训练的情感模型是一个可行的解决方案。
Automatic affect analysis and understanding has become a well established research area in the last two decades. Recent works have started moving from individual to group scenarios. However, little attention has been paid to comparing the affect expressed in individual and group settings. This paper presents a framework to investigate the differences in affect recognition models along arousal and valence dimensions in individual and group settings. We analyse how a model trained on data collected from an individual setting performs on test data collected from a group setting, and vice versa. A third model combining data from both individual and group settings is also investigated. A set of experiments is conducted to predict the affective states along both arousal and valence dimensions on two newly collected databases that contain sixteen participants watching affective movie stimuli in individual and group settings, respectively. The experimental results show that (1) the affect model trained with group data performs better on individual test data than the model trained with individual data tested on group data, indicating that facial behaviours expressed in a group setting capture more variation than in an individual setting; and (2) the combined model does not show better performance than the affect model trained with a specific type of data (i.e., individual or group), but proves a good compromise. These results indicate that in settings where multiple affect models trained with different types of data are not available, using the affect model trained with group data is a viable solution.
影响因子：
4.7
作者：
Koelstra, Sander;Patras, Ioannis
通讯作者：
Patras, Ioannis