DepNet: An automated industrial intelligent system using deep learning for video-based depression analysis

Lang He, Chenguang Guo, Prayag Tiwari, Rui Su, Hari Mohan Pandey*, Wei Dang

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

20 Citations (Scopus)
260 Downloads (Pure)

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 languageEnglish
Pages (from-to)3815-3835
Number of pages21
JournalInternational Journal of Intelligent Systems
Volume37
Issue number7
Early online date6 Oct 2021
DOIs
Publication statusPublished - Jul 2022
MoE publication typeA1 Journal article-refereed

Keywords

  • deep learning
  • depression
  • feature aggregation module
  • industrial intelligent system
  • pattern recognition

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