Citation

He L, Guo C, Tiwari P, Pandey HM, Dang W. Int. J. Intell. Syst. 2022; 37(12): 10140-10156.

Copyright

(Copyright © 2022, Hindawi / Wiley Periodicals)

DOI

10.1002/int.22426

PMID

unavailable

Abstract

As a mental disorder, depression has affected people's lives, works, and so on. Researchers have proposed various industrial intelligent systems 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 three-dimensional (3D) convolutional neural network equipped with a module spatiotemporal feature aggregation module (STFAM) is trained from scratch on audio/visual emotion challenge (AVEC)2013 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.

Language: en

Keywords

depression; 3D-CNN; industrial intelligent system; pattern recognition; vector of local aggregated descriptors