ArticleInternational Conference on Affective Computing and Intelligent Interaction and workshops : [proceedings]. ACII (Conference)2017
Automatic Action Unit Detection in Infants Using Convolutional Neural Network.
Article in International Conference on Affective Computing and Intelligent Interaction and workshops : [proceedings]. ACII (Conference), 2017. 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.
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
10 citing papers in PubMed, 32 citations in OpenAlex.
- Comparative analysis of artificial intelligence and expert assessments in detecting neonatal procedural pain.Scientific reports · 2024Article
- Infant AFAR: Automated facial action recognition in infants.Behavior research methods · 2023Article
- The Clinical Suitability of an Artificial Intelligence-Enabled Pain Assessment Tool for Use in Infants: Feasibility and Usability Evaluation Study.Journal of medical Internet research · 2023Article
- Experience with an Affective Robot Assistant for Children with Hearing Disabilities.International journal of social robotics · 2023Article
- 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
- Crossing Domains for AU Coding: Perspectives, Approaches, and Measures.IEEE transactions on biometrics, behavior, and identity science · 2020Article
- D-PAttNet: Dynamic Patch-Attentive Deep Network for Action Unit Detection.Frontiers in computer science · 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
- Dynamics of Face and Head Movement in Infants with and without Craniofacial Microsomia: An Automatic Approach.Plastic and reconstructive surgery. Global open · 2019Article
- Relations between Automatically Extracted Motion Features and the Quality of Mother-Infant Interactions at 4 and 13 Months.Frontiers in psychology · 2017Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors at 3 institutions in 1 country.
Funding
Abstract
Action unit detection in infants relative to adults presents unique challenges. Jaw contour is less distinct, facial texture is reduced, and rapid and unusual facial movements are common. To detect facial action units in spontaneous behavior of infants, we propose a multi-label Convolutional Neural Network (CNN). Eighty-six infants were recorded during tasks intended to elicit enjoyment and frustration. Using an extension of FACS for infants (Baby FACS), over 230,000 frames were manually coded for ground truth. To control for chance agreement, inter-observer agreement between Baby-FACS coders was quantified using free-margin kappa. Kappa coefficients ranged from 0.79 to 0.93, which represents high agreement. The multi-label CNN achieved comparable agreement with manual coding. Kappa ranged from 0.69 to 0.93. Importantly, the CNN-based AU detection revealed the same change in findings with respect to infant expressiveness between tasks. While further research is needed, these findings suggest that automatic AU detection in infants is a viable alternative to manual coding of infant facial expression.
Identifiers
What OpenQuestion holds
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.