Research on a Multimodal Emotion Perception Model Based on GCN+GIN Hybrid Model

Graph neural networks (GNNs) have demonstrated strong performance in handling graph-structured data in recent years​​, particularly in capturing complex inter-node relationships among data samples, showcasing advantages over traditional neural networks. However, challenges persist, including ​​difficulties in cross-modal information fusion, inadequate modeling of modal relationships, and high computational costs​​. To address these limitations, ​​this paper proposes GGMEN​​, a novel model that integrates the local neighborhood aggregation capability of graph convolutional networks with the global structural expressiveness of graph isomorphic networks (GINs). Leveraging ​​shallow feature extraction via time-frequency joint analysis​​, the paper extracts 14 representative physiological statistical features. Simultaneously, the ​​Transformer model captures spatial features from individual facial expression video frames​​, enabling spatio-temporal modeling of facial expressions. ​​The GCN layer models temporal dependencies in physiological signals and spatial relationships of facial key points​​, while the ​​GIN layer enhances modeling of complex higher-order relationships​​. ​​Multi-modal emotion perception is achieved through attention-based modality fusion​​. Experiments on the DEAP dataset validate the model’s effectiveness across multiple emotion perception benchmarks, achieving an emotion recognition accuracy of 81.25%. Comparative analyses with existing models confirm the accuracy improvement of the proposed framework.