Human behaviour-based automatic depression analysis using hand-crafted statistics and deep learned spectral features

Siyang Song, Linlin Shen, Michel Valstar

Research output: Chapter in Book/Conference proceedingConference contributionpeer-review

54 Citations (Scopus)

Abstract

Depression is a serious mental disorder that affects millions of people all over the world. Traditional clinical diagnosis methods are subjective, complicated and need extensive participation of experts. Audio-visual automatic depression analysis systems predominantly base their predictions on very brief sequential segments, sometimes as little as one frame. Such data contains much redundant information, causes a high computational load, and negatively affects the detection accuracy. Final decision making at the sequence level is then based on the fusion of frame or segment level predictions. However, this approach loses longer term behavioural correlations, as the behaviours themselves are abstracted away by the frame-level predictions. We propose to on the one hand use automatically detected human behaviour primitives such as Gaze directions, Facial action units (AU), etc. as low-dimensional multi-channel time series data, which can then be used to create two sequence descriptors. The first calculates the sequence-level statistics of the behaviour primitives and the second casts the problem as a Convolutional Neural Network problem operating on a spectral representation of the multichannel behaviour signals. The results of depression detection (binary classification) and severity estimation (regression) experiments conducted on the AVEC 2016 DAIC-WOZ database show that both methods achieved significant improvement compared to the previous state of the art in terms of the depression severity estimation.

Original languageEnglish
Title of host publicationProceedings - 13th IEEE International Conference on Automatic Face and Gesture Recognition, FG 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages158-165
Number of pages8
ISBN (Electronic)9781538623350
DOIs
Publication statusPublished - 5 Jun 2018
Externally publishedYes
Event13th IEEE International Conference on Automatic Face and Gesture Recognition, FG 2018 - Xi'an, China
Duration: 15 May 201819 May 2018

Publication series

NameProceedings - 13th IEEE International Conference on Automatic Face and Gesture Recognition, FG 2018

Conference

Conference13th IEEE International Conference on Automatic Face and Gesture Recognition, FG 2018
Country/TerritoryChina
CityXi'an
Period15/05/1819/05/18

Keywords

  • Automatic depression analysis
  • Convolution Nerual Networks
  • Human behaviour signals
  • Spectrum maps
  • Statistic

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition
  • Control and Optimization

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