Faculty of Electronics, Telecommunications and Informatics and Digital Technologies Center, Gdańsk University of Technology, 80-233 Gdańsk, Poland.
Faculty of Electronics, Telecommunications and Informatics, Gdańsk University of Technology, 80-233 Gdańsk, Poland.
Sensors (Basel). 2022 Apr 13;22(8):2980. doi: 10.3390/s22082980.
In recent years, a number of new research papers have emerged on the application of neural networks in affective computing. One of the newest trends observed is the utilization of graph neural networks (GNNs) to recognize emotions. The study presented in the paper follows this trend. Within the work, GraphSleepNet (a GNN for classifying the stages of sleep) was adjusted for emotion recognition and validated for this purpose. The key assumption of the validation was to analyze its correctness for the Circumplex model to further analyze the solution for emotion recognition in the Ekman modal. The novelty of this research is not only the utilization of a GNN network with GraphSleepNet architecture for emotion recognition, but also the analysis of the potential of emotion recognition based on differential entropy features in the Ekman model with a neutral state and a special focus on continuous emotion recognition during the performance of an activity The GNN was validated against the AMIGOS dataset. The research shows how the use of various modalities influences the correctness of the recognition of basic emotions and the neutral state. Moreover, the correctness of the recognition of basic emotions is validated for two configurations of the GNN. The results show numerous interesting observations for Ekman's model while the accuracy of the Circumplex model is similar to the baseline methods.
近年来，出现了许多关于神经网络在情感计算中应用的新研究论文。观察到的最新趋势之一是利用图神经网络 (GNN) 来识别情感。本文所呈现的研究就遵循了这一趋势。在这项工作中，对 GraphSleepNet（用于分类睡眠阶段的 GNN）进行了调整，以进行情感识别，并为此进行了验证。验证的关键假设是分析其对 Circumplex 模型的正确性，以进一步分析在 Ekman 模态中识别情感的解决方案。这项研究的新颖之处不仅在于利用具有 GraphSleepNet 架构的 GNN 网络进行情感识别，还在于分析基于差分熵特征的情感识别的潜力，其中包括 Ekman 模型中的中性状态，以及特别关注在执行活动时的连续情感识别。该 GNN 是针对 AMIGOS 数据集进行验证的。该研究展示了使用各种模态如何影响基本情绪和中性状态识别的正确性。此外，还对 GNN 的两种配置进行了基本情绪识别的正确性验证。结果表明，在 Ekman 模型中观察到了许多有趣的现象，而 Circumplex 模型的准确性与基线方法相似。
