Projects per year
Abstract
As a common mental disorder, depression has attracted many researchers from affective computing field to estimate the depression severity. However, existing approaches based on Deep Learning (DL) are mainly focused on single facial image without considering the sequence information for predicting the depression scale. In this paper, an integrated framework, termed DepNet, for automatic diagnosis of depression that adopts facial images sequence from videos is proposed. Specifically, several pretrained models are adopted to represent the low-level features, and Feature Aggregation Module is proposed to capture the high-level characteristic information for depression analysis. More importantly, the discriminative characteristic of depression on faces can be mined to assist the clinicians to diagnose the severity of the depressed subjects. Multiscale experiments carried out on AVEC2013 and AVEC2014 databases have shown the excellent performance of the intelligent approach. The root mean-square error between the predicted values and the Beck Depression Inventory-II scores is 9.17 and 9.01 on the two databases, respectively, which are lower than those of the state-of-the-art video-based depression recognition methods.
Original language | English |
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Pages (from-to) | 3815-3835 |
Number of pages | 21 |
Journal | International Journal of Intelligent Systems |
Volume | 37 |
Issue number | 7 |
Early online date | 6 Oct 2021 |
DOIs | |
Publication status | Published - Jul 2022 |
MoE publication type | A1 Journal article-refereed |
Keywords
- deep learning
- depression
- feature aggregation module
- industrial intelligent system
- pattern recognition
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INTERVENE: International consortium for integrative genomics prediction
Kaski, S. (Principal investigator)
01/01/2021 → 31/12/2025
Project: EU: Framework programmes funding
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DATALIT: Data Literacy for Responsible Decision-Making
Marttinen, P. (Principal investigator), Ji, S. (Project Member), Gröhn, T. (Project Member), Honkamaa, J. (Project Member), Kumar, Y. (Project Member), Pöllänen, A. (Project Member), Ojala, F. (Project Member), Raj, V. (Project Member) & Tiwari, P. (Project Member)
01/10/2020 → 30/09/2023
Project: Academy of Finland: Strategic research funding
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eMOM: CleverHealth Network: eMOM GDM -Project
Marttinen, P. (Principal investigator), Ashrafi, R. (Project Member), Hizli, C. (Project Member) & Zhang, G. (Project Member)
05/02/2018 → 31/01/2023
Project: Business Finland: Other research funding