Depression recognition based on text and facial expression

Yameng Hao; Yongzhong Cao; Bin Li; Muhammad Rahman

doi:10.1117/12.2606315

28 October 2021 Depression recognition based on text and facial expression

Yameng Hao, Yongzhong Cao, Bin Li, Muhammad Rahman

Proceedings Volume 11884, International Symposium on Artificial Intelligence and Robotics 2021; 118841J (2021) https://doi.org/10.1117/12.2606315
Event: International Symposium on Artificial Intelligence and Robotics 2021, 2021, Fukuoka, Japan

Abstract

Depression is a kind of mood disorder disease characterized by significant and lasting depression, which seriously affects people's physical and mental health. In recent years, the number of people suffering from depression has gradually increased. In order to improve the recognition rate of depression and reduce the workload of doctors, this paper proposes to apply the deep learning algorithm BiLSTM (Bi-directional Long Short-Term Memory) and Attention to recognize depression. Among them, BiLSTM is used to extract contextual temporal information of text features and facial features. Attention is used to learn the correlation between vision and text modalities. This paper undertakes extensive experiments to demonstrate the network's effect. The experimental results show that this method has certain practical application value for depression recognition.

Citation Download Citation

Yameng Hao, Yongzhong Cao, Bin Li, and Muhammad Rahman "Depression recognition based on text and facial expression", Proc. SPIE 11884, International Symposium on Artificial Intelligence and Robotics 2021, 118841J (28 October 2021); https://doi.org/10.1117/12.2606315

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