We address the problem of facial expression analysis. The proposed approach predicts both basic emotion and valence/arousal values as a continuous measure for the emotional state. Experimental results including cross-database evaluation on the AffectNet, Aff-Wild, and AFEW dataset shows that our approach predicts emotion categories and valence/arousal values with high accuracies and that the simultaneous learning of discrete categories and continuous values improves the prediction of both. In addition, we use our approach to measure the emotional states of users in an Human-Robot-Collaboration scenario (HRC), show how these emotional states are affected by multiple difficulties that arise for the test subjects, and examine how different feedback mechanisms counteract negative emotions users experience while interacting with a robot system.

我们解决了面部表情分析的问题。所提出的方法预测基本情绪和化合价/配比值作为情绪状态的连续量度。包括对AffectNet，Aff-Wild和AFEW数据集的跨数据库评估在内的实验结果表明，我们的方法可以高精度地预测情感类别和价/含价值，同时学习离散类别和连续值可以改善两者的预测。此外，我们使用我们的方法来测量人机协作场景（HRC）中用户的情绪状态，显示这些情绪状态如何受到测试对象的多重困难的影响，并研究不同的反馈机制如何抵消用户在与机器人系统进行交互时会遇到的负面情绪。