ArticleProceedings of the ... International Conference on Automatic Face and Gesture Recognition. IEEE International Conference on Automatic Face & Gesture Recognition
Sayette Group Formation Task (GFT) Spontaneous Facial Expression Database.
Article in Proceedings of the ... International Conference on Automatic Face and Gesture Recognition. IEEE International Conference on Automatic Face & Gesture Recognition. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
What it found
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
17 citing papers in PubMed.
- Facial phenotypes in Alzheimer's disease: from neurobiology to artificial intelligence.Alzheimer's research & therapy · 2026Review
- Machine and deep learning in facial expression recognition: a survey based on facial action units.Scientific reports · 2026Review
- Three-dimensional facial features of suicide risk in females with depression.Frontiers in psychiatry · 2025Article
- Understanding Naturalistic Facial Expressions with Deep Learning and Multimodal Large Language Models.Sensors (Basel, Switzerland) · 2023Review
- Development of the RIKEN database for dynamic facial expressions with multiple angles.Scientific reports · 2023Article
- Infant AFAR: Automated facial action recognition in infants.Behavior research methods · 2023Article
- Multimodal Feature Selection for Detecting Mothers' Depression in Dyadic Interactions with their Adolescent Offspring.IEEE transactions on affective computing · 2023Article
- SHAP-based Prediction of Mother's History of Depression to Understand the Influence on Child Behavior.Proceedings of the ... ACM International Conference on Multimodal Interaction. ICMI (Conference) · 2023Article
- Macro- and Micro-Expressions Facial Datasets: A Survey.Sensors (Basel, Switzerland) · 2022Review
- Assessing Automated Facial Action Unit Detection Systems for Analyzing Cross-Domain Facial Expression Databases.Sensors (Basel, Switzerland) · 2021Article
- Toward Multimodal Modeling of Emotional Expressiveness.Proceedings of the ... ACM International Conference on Multimodal Interaction. ICMI (Conference) · 2020Article
- Crossing Domains for AU Coding: Perspectives, Approaches, and Measures.IEEE transactions on biometrics, behavior, and identity science · 2020Article
- Predicting Group Contribution Behaviour in a Public Goods Game from Face-to-Face Communication.Sensors (Basel, Switzerland) · 2019Article
- Cross-domain AU Detection: Domains, Learning Approaches, and Measures.Proceedings of the ... International Conference on Automatic Face and Gesture Recognition. IEEE International Conference on Automatic Face & Gesture Recognition · 2019Article
- Learning Facial Action Units with Spatiotemporal Cues and Multi-label Sampling.Image and vision computing · 2019Article
- Automatic Action Unit Detection in Infants Using Convolutional Neural Network.International Conference on Affective Computing and Intelligent Interaction and workshops : [proceedings]. ACII (Conference) · 2017Article
- Multimodal Prediction of Obsessive-Compulsive Disorder and Comorbid Depression Severity and Energy Delivered by Deep Brain Electrodes.IEEE transactions on affective computingArticle
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
Despite the important role that facial expressions play in interpersonal communication and our knowledge that interpersonal behavior is influenced by social context, no currently available facial expression database includes multiple interacting participants. The Sayette Group Formation Task (GFT) database addresses the need for well-annotated video of multiple participants during unscripted interactions. The database includes 172,800 video frames from 96 participants in 32 three-person groups. To aid in the development of automated facial expression analysis systems, GFT includes expert annotations of FACS occurrence and intensity, facial landmark tracking, and baseline results for linear SVM, deep learning, active patch learning, and personalized classification. Baseline performance is quantified and compared using identical partitioning and a variety of metrics (including means and confidence intervals). The highest performance scores were found for the deep learning and active patch learning methods. Learn more at http://osf.io/7wcyz.
Identifiers
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.