ArticleeLife2022
Quantifying dynamic facial expressions under naturalistic conditions.
Article in eLife, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
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Who cites it
5 citing papers in PubMed.
- Functional and Morphological Differences in the Play Face and Full Play Face in Lowland Gorillas, a Hominid Species: Implications for the Evolutionary Roots of Smile and Laugh Face.American journal of biological anthropology · 2025Article
- Markers of positive affect and brain state synchrony discriminate melancholic from non-melancholic depression using naturalistic stimuli.Molecular psychiatry · 2025Article
- Decoding pain through facial expressions: a study of patients with migraine.The journal of headache and pain · 2024Article
- Cultural facial expressions dynamically convey emotion category and intensity information.Current biology : CB · 2024Article
- Crossing fibers may underlie the dynamic pulling forces of muscles that attach to cartilage at the tip of the nose.Scientific reports · 2023Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
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.
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Registered trials
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