Depression Recognition using Remote Photoplethysmography from Facial Videos

Constantino Alvarez Casado, Manuel Lage Canellas, Miguel Bordallo Lopez

Research output: Contribution to journalArticleScientificpeer-review

12 Citations (Scopus)

Abstract

Depression is a mental illness that may be harmful to an individual's health. The detection of mental health disorders in the early stages and a precise diagnosis are critical to avoid social, physiological, or psychological side effects. This work analyzes physiological signals to observe if different depressive states have a noticeable impact on the blood volume pulse (BVP) and the heart rate variability (HRV) response. Although typically, HRV features are calculated from biosignals obtained with contact-based sensors such as wearables, we propose instead a novel scheme that directly extracts them from facial videos, just based on visual information, removing the need for any contact-based device. Our solution is based on a pipeline that is able to extract complete remote photoplethysmography signals (rPPG) in a fully unsupervised manner. We use these rPPG signals to calculate over 60 statistical, geometrical, and physiological features that are further used to train several machine learning regressors to recognize different levels of depression. Experiments on two benchmark datasets indicate that this approach offers comparable results to other audiovisual modalities based on voice or facial expression, potentially complementing them. In addition, the results achieved for the proposed method show promising and solid performance that outperforms hand-engineered methods and is comparable to deep learning-based approaches.

Original languageEnglish
Pages (from-to)3305-3316
Number of pages13
JournalIEEE Transactions on Affective Computing
Volume14
Issue number4
DOIs
Publication statusPublished - 2023
MoE publication typeA1 Journal article-refereed

Keywords

  • Affective Computing
  • Biomedical monitoring
  • Depression
  • Depression Detection
  • Faces
  • Feature extraction
  • Heart rate variability
  • HRV Features
  • Image Processing
  • Machine Learning
  • Remote Photoplethysmography
  • rPPG
  • Signal Processing
  • Skin
  • Videos

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