Automated depression diagnosis based on facial expression and deep dual-stream convolutional neural networks

Yangyang Chen; Yingyu Chen; Mi Li

doi:10.1117/12.2684211

1 August 2023 Automated depression diagnosis based on facial expression and deep dual-stream convolutional neural networks

Yangyang Chen, Yingyu Chen, Mi Li

Author Affiliations +

Proceedings Volume 12754, Third International Conference on Computer Vision and Pattern Analysis (ICCPA 2023); 127541R (2023) https://doi.org/10.1117/12.2684211
Event: 2023 3rd International Conference on Computer Vision and Pattern Analysis (ICCPA 2023), 2023, Hangzhou, China

Abstract

Depression is a common mental illness characterized by symptoms such as low mood, pessimism, and insomnia. In this study, we developed a deep Dual-Stream CNN to automatically diagnose and classify depression in expression video sequences. The network has two branches that extract static features and dynamic features from static and dynamic expressions, respectively, which are then fused for depression classification. The experiments were performed on the AVEC2014 database, and the results showed that the Dual-Stream model significantly improved the classification performance of depression, achieving an accuracy of 69.08% in depression categorization.

Citation Download Citation

Yangyang Chen, Yingyu Chen, and Mi Li "Automated depression diagnosis based on facial expression and deep dual-stream convolutional neural networks", Proc. SPIE 12754, Third International Conference on Computer Vision and Pattern Analysis (ICCPA 2023), 127541R (1 August 2023); https://doi.org/10.1117/12.2684211

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