ArticleBehavior research methods2023
Infant AFAR: Automated facial action recognition in infants.
Article in Behavior research methods, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
10 citing papers in PubMed, 25 citations in OpenAlex.
- AI-Assisted Video Monitoring for Tracheostomy-Dependent Infants: A Proof-Of-Concept Study.The Laryngoscope · 2026Article
- Smiling in time: the interactive development of infant smiling with mothers, fathers and strangers.Philosophical transactions of the Royal Society of London. Series B, Biological sciences · 2026Article
- Evaluating Open-Source Solutions for Computerized Inference of Infant Facial Affect.Developmental science · 2026Article
- Automated detection of mouth opening in newborn infants.Behavior research methods · 2025Article
- Comparative analysis of artificial intelligence and expert assessments in detecting neonatal procedural pain.Scientific reports · 2024Article
- Article
- Human and machine recognition of dynamic and static facial expressions: prototypicality, ambiguity, and complexity.Frontiers in psychology · 2023Review
- Reconstructing 3D Face of Infants in Social Interactions Using Morphable Models of Non-Infants.Eurographics ... Workshop on 3D Object Retrieval : EG 3DOR. Eurographics Workshop on 3D Object Retrieval · 2022Article
- Computational approaches to understanding interaction and development.Advances in child development and behavior · 2022Article
- Automated measurement of infant and mother Duchenne facial expressions in the Face-to-Face/Still-Face.Infancy : the official journal of the International Society on Infant StudiesArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors at 4 institutions in 2 countries.
Funding
Abstract
Automated detection of facial action units in infants is challenging. Infant faces have different proportions, less texture, fewer wrinkles and furrows, and unique facial actions relative to adults. For these and related reasons, action unit (AU) detectors that are trained on adult faces may generalize poorly to infant faces. To train and test AU detectors for infant faces, we trained convolutional neural networks (CNN) in adult video databases and fine-tuned these networks in two large, manually annotated, infant video databases that differ in context, head pose, illumination, video resolution, and infant age. AUs were those central to expression of positive and negative emotion. AU detectors trained in infants greatly outperformed ones trained previously in adults. Training AU detectors across infant databases afforded greater robustness to between-database differences than did training database specific AU detectors and outperformed previous state-of-the-art in infant AU detection. The resulting AU detection system, which we refer to as Infant AFAR (Automated Facial Action Recognition), is available to the research community for further testing and applications in infant emotion, social interaction, and related topics.
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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.