ArticlePloS one2019
Using computer-vision and machine learning to automate facial coding of positive and negative affect intensity.
Article in PloS one, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed.
- The effect of human ratings reliability on machine learning model performance: a case study in infant pain assessment.Scientific reports · 2026Article
- Article
- Py-Feat: Python Facial Expression Analysis Toolbox.Affective science · 2023Article
- Facial Expressions as an Index of Listening Difficulty and Emotional Response.Seminars in hearing · 2023Review
- Novel Method for Three-Dimensional Facial Expression Recognition Using Self-Normalizing Neural Networks and Mobile Devices.Geburtshilfe und Frauenheilkunde · 2022Article
- Classification of elderly pain severity from automated video clip facial action unit analysis: A study from a Thai data repository.Frontiers in artificial intelligence · 2022Article
- What's in a face: Automatic facial coding of untrained study participants compared to standardized inventories.PloS one · 2022Article
- Similarities and disparities between visual analysis and high-resolution electromyography of facial expressions.PloS one · 2022Article
- Machine learning for sperm selection.Nature reviews. Urology · 2021Review
- Correlations Between Facial Expressivity and Apathy in Elderly People With Neurocognitive Disorders: Exploratory Study.JMIR formative research · 2021Article
- Automated Detection of Enhanced DBS Device Settings.Companion Publication of the 2020 International Conference on Multimodal Interaction · 2020Article
- Read My Face: Automatic Facial Coding Versus Psychophysiological Indicators of Emotional Valence and Arousal.Frontiers in psychology · 2020Article
- Using automated computer vision and machine learning to code facial expressions of affect and arousal: Implications for emotion dysregulation research.Development and psychopathology · 2019Article
- Correction: Using computer-vision and machine learning to automate facial coding of positive and negative affect intensity.PloS one · 2019Article
Corrections and comments
- Erratum issued
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Facial expressions are fundamental to interpersonal communication, including social interaction, and allow people of different ages, cultures, and languages to quickly and reliably convey emotional information. Historically, facial expression research has followed from discrete emotion theories, which posit a limited number of distinct affective states that are represented with specific patterns of facial action. Much less work has focused on dimensional features of emotion, particularly positive and negative affect intensity. This is likely, in part, because achieving inter-rater reliability for facial action and affect intensity ratings is painstaking and labor-intensive. We use computer-vision and machine learning (CVML) to identify patterns of facial actions in 4,648 video recordings of 125 human participants, which show strong correspondences to positive and negative affect intensity ratings obtained from highly trained coders. Our results show that CVML can both (1) determine the importance of different facial actions that human coders use to derive positive and negative affective ratings when combined with interpretable machine learning methods, and (2) efficiently automate positive and negative affect intensity coding on large facial expression databases. Further, we show that CVML can be applied to individual human judges to infer which facial actions they use to generate perceptual emotion ratings from facial expressions.
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