Abstract
Depression is a mental illness that may be harmful to an individual's health. Using deep learning models to recognize the facial expressions of individuals captured in videos has shown promising results for automatic depression detection. Typically, depression levels are recognized using 2D-Convolutional Neural Networks (CNNs) that are trained to extract static features from video frames, which impairs the capture of dynamic spatio-temporal relations. As an alternative, 3D-CNNs may be employed to extract spatiotemporal features from short video clips, although the risk of overfitting increases due to the limited availability of labeled depression video data. To address these issues, we propose a novel temporal pooling method to capture and encode the spatio-temporal dynamic of video clips into an image map. This approach allows fine-tuning a pre-trained 2D CNN to model facial variations, and thereby improving the training process and model accuracy. Our proposed method is based on two-stream model that performs late fusion of appearance and dynamic information. Extensive experiments on two benchmark AVEC datasets indicate that the proposed method is efficient and outperforms the state-of-the-art schemes.
Original language | English |
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Title of host publication | ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech, and Signal Processing |
Publisher | IEEE Institute of Electrical and Electronic Engineers |
Pages | 1080-1084 |
ISBN (Electronic) | 978-1-5090-6631-5 |
ISBN (Print) | 978-1-5090-6632-2 |
DOIs | |
Publication status | Published - May 2020 |
MoE publication type | A4 Article in a conference publication |
Event | 2020 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2020 - Barcelona, Spain Duration: 4 May 2020 → 8 May 2020 |
Publication series
Series | Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing |
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Volume | 2020 |
ISSN | 1520-6149 |
Conference
Conference | 2020 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2020 |
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Country/Territory | Spain |
City | Barcelona |
Period | 4/05/20 → 8/05/20 |
Funding
∗The financial support of the Academy of Finland, Infotech Oulu and State University of Amazonas is acknowledged.
Keywords
- Affective Computing
- Depression Detection
- Expression Recognition
- Temporal Pooling
- Two-stream Model