Quantifying dynamic facial expressions under naturalistic conditions
- University of Newcastle Australia, Australia
- University of Western Australia, Australia
- University of New South Wales, Australia
Abstract
Facial affect is expressed dynamically - a giggle, grimace, or an agitated frown. However, the characterization 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.
Data availability
The DISFA dataset is publically available at http://mohammadmahoor.com/disfa/, and can be accessed by application at http://mohammadmahoor.com/disfa-contact-form/. The melancholia dataset is not publically available due to ethical and privacy considerations for patients, and because the original ethics approval does not permit sharing this data.
-
DISFA: A Spontaneous Facial Action Intensity Databasehttp://mohammadmahoor.com/.
Article and author information
Author details
Michael Breakspear
Funding
Health Education and Training Institute Award in Psychiatry and Mental Health
- Jayson Jeganathan
Rainbow Foundation
- Jayson Jeganathan
- Michael Breakspear
National Health and Medical Research Council (1118153,10371296,1095227)
- Michael Breakspear
Australian Research Council (CE140100007)
- Michael Breakspear
The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
Ethics
Human subjects: Participants provided informed consent for the study. Ethics approval was obtained from the University of New South Wales (HREC-08077) and the University of Newcastle (H-2020-0137). Figure 1a shows images of a person's face from the DISFA dataset. Consent to reproduce their image in publications was obtained by the original DISFA authors, and is detailed in the dataset agreement (http://mohammadmahoor.com/disfa-contact-form/) and the original paper (https://ieeexplore.ieee.org/document/6475933).
Copyright
© 2022, Jeganathan et al.
This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.
Metrics
-
- 1,542
- views
-
- 270
- downloads
-
- 8
- citations
Views, downloads and citations are aggregated across all versions of this paper published by eLife.
Download links
A two-part list of links to download the article, or parts of the article, in various formats.
Downloads (link to download the article as PDF)
Open citations (links to open the citations from this article in various online reference manager services)
Cite this article (links to download the citations from this article in formats compatible with various reference manager tools)
Quantifying dynamic facial expressions under naturalistic conditions
eLife 11:e79581.
https://doi.org/10.7554/eLife.79581
Further reading
-
- Computational and Systems Biology
Degree distributions in protein-protein interaction (PPI) networks are believed to follow a power law (PL). However, technical and study biases affect the experimental procedures for detecting PPIs. For instance, cancer-associated proteins have received disproportional attention. Moreover, bait proteins in large-scale experiments tend to have many false-positive interaction partners. Studying the degree distributions of thousands of PPI networks of controlled provenance, we address the question if PL distributions in observed PPI networks could be explained by these biases alone. Our findings are supported by mathematical models and extensive simulations, and indicate that study bias and technical bias suffice to produce the observed PL distribution. It is, hence, problematic to derive hypotheses about the topology of the true biological interactome from the PL distributions in observed PPI networks. Our study casts doubt on the use of the PL property of biological networks as a modeling assumption or quality criterion in network biology.
-
- Computational and Systems Biology
- Immunology and Inflammation
Transcription factor partners can cooperatively bind to DNA composite elements to augment gene transcription. Here, we report a novel protein-DNA binding screening pipeline, termed Spacing Preference Identification of Composite Elements (SPICE), that can systematically predict protein binding partners and DNA motif spacing preferences. Using SPICE, we successfully identified known composite elements, such as AP1-IRF composite elements (AICEs) and STAT5 tetramers, and also uncovered several novel binding partners, including JUN-IKZF1 composite elements. One such novel interaction was identified at CNS9, an upstream conserved noncoding region in the human IL10 gene, which harbors a non-canonical IKZF1 binding site. We confirmed the cooperative binding of JUN and IKZF1 and showed that the activity of an IL10-luciferase reporter construct in primary B and T cells depended on both this site and the AP1 binding site within this composite element. Overall, our findings reveal an unappreciated global association of IKZF1 and AP1 and establish SPICE as a valuable new pipeline for predicting novel transcription binding complexes.
