Intelligent System for Depression Scale Estimation with Facial Expressions and Case Study in Industrial Intelligence

HARI MOHAN PANDEY*, Lang He, Chenguang Guo, Prayag Tiwari, Wei Dang

*Corresponding author for this work

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As a mental disorder, depression has affected people's lives, works, and so on. Researchers have proposed various industrial intelligent systems (IIS) in the pattern recognition field for audiovisual depression detection. This paper presents an end-to-end trainable intelligent system to generate high-level representations over the entire video clip. Specifically, a 3D-CNN equipped with a module Spatiotemporal Feature Aggregation Module (STFAM) is trained from scratch on AVEC2013 and AVEC2014 data, which can model the discriminative patterns closely related to depression. In the STFAM, channel and spatial attention mechanism and an aggregation method, namely 3D DEP-NetVLAD, are integrated to learn the compact characteristic based on the feature maps. Extensive experiments on the two databases (i.e., AVEC2013 and AVEC2014) are illustrated that the proposed intelligent system can efficiently model the underlying depression patterns and obtain better performances over the most video-based depression recognition approaches. Case studies are presented to describes the applicability of the proposed intelligent system for industrial intelligence.
Original languageEnglish
Article numberINT2.20210090R1
JournalInternational Journal of Intelligent Systems
Early online date8 Apr 2021
Publication statusE-pub ahead of print - 8 Apr 2021


  • Depression
  • Industrial intelligent system (IIS)
  • 3D-CNN
  • pattern recognition
  • Vector of local aggregated descriptors (VLAD)


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