Quantifying dynamic facial expressions under naturalistic conditions

Elife. 2022 Aug 31:11:e79581. doi: 10.7554/eLife.79581.

Abstract

Facial affect is expressed dynamically - a giggle, grimace, or an agitated frown. However, the characterisation of human affect has relied almost exclusively on static images. This approach cannot capture the nuances of human communication or support the naturalistic assessment of affective disorders. Using the latest in machine vision and systems modelling, we studied dynamic facial expressions of people viewing emotionally salient film clips. We found that the apparent complexity of dynamic facial expressions can be captured by a small number of simple spatiotemporal states - composites of distinct facial actions, each expressed with a unique spectral fingerprint. Sequential expression of these states is common across individuals viewing the same film stimuli but varies in those with the melancholic subtype of major depressive disorder. This approach provides a platform for translational research, capturing dynamic facial expressions under naturalistic conditions and enabling new quantitative tools for the study of affective disorders and related mental illnesses.

Keywords: computational biology; facial expression; human; major depressive disorder; naturalistic; systems biology.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Communication
  • Depression
  • Depressive Disorder, Major*
  • Emotions
  • Facial Expression*
  • Humans

Grants and funding

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.