Depression Recognition by Fuzzy Learning of Facial Movements Based on Graph Neural Networks

Facial movement-based depression recognition has garnered considerable attention in automatic depression detection field in recent years. Current methods typically seek to establish a uniform depression discrimination criterion and often overlook inter-individual differences in facial movements, which lead to a decrease in the accuracy of models. A closer examination of the clinical diagnostic criteria for depression reveals that individuals with depression exhibit a wide variety of symptoms. This suggests that even individuals with similar levels of depression may exhibit significantly different facial movements. To address this challenge, this study introduces a novel framework for depression assessment—Multiscale Video Adaptive Graph Network (MVAGN). By leveraging the information transmission capabilities of Graph Neural Network (GNN) to balance the relationship between individual differences and commonalities to achieve more accurate depression identification. MVAGN consists of three modules: the Multiscale Video Feature Extractor (MVFE), the Hybrid Feature Interaction Graph Module (HFIGM), and the Adaptive Hybrid Graph Network (AHGN). MVFE extracts multi-scale features from the video, HFIGM transforms these features into graph representations, and AHGN facilitates information transfer between nodes to reduce individual feature differences, ultimately providing a more precise estimation of depression severity. Experiments conducted on the AVEC2013 and AVEC2014 datasets demonstrate that MVAGN achieves state-of-the-art performance. This paper proposes that building a fuzzy system with a compact kernel and a loose periphery provides a viable path for recognizing depression.