ArticleMolecular autism2020
Children with autism spectrum disorder produce more ambiguous and less socially meaningful facial expressions: an experimental study using random forest classifiers.
Article in Molecular autism, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 2 of them syntheses that pooled it.
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Who cites it
17 citing papers in PubMed, 2 syntheses or guidelines pooled it, 36 citations in OpenAlex.
- Automatic Emotion Recognition in Children with Autism: A Systematic Literature Review.Sensors (Basel, Switzerland) · 2022Pooled it
- New Technologies as Promising Tools for Assessing Facial Emotion Expressions Impairments in ASD: A Systematic Review.Frontiers in psychiatry · 2021Pooled it
- Comparing three algorithms of automated facial expression analysis in autistic children: different sensitivities but consistent proportions.Molecular autism · 2025Article
- Screening autism spectrum disorder in children using machine learning on speech transcripts.Scientific reports · 2025Article
- Feasibility of internet-based multimodal emotion recognition training in adolescents with and without autism: A pilot study.Internet interventions · 2025Review
- Autism Digital Phenotyping in Preschool- and School-Age Children.Autism research : official journal of the International Society for Autism Research · 2025Article
- Positive emotional valence in spontaneous facial expressions of autistic adolescents.Scientific reports · 2025Article
- Prefrontal Cortex Responses to Social Video Stimuli in Young Children with and without Autism Spectrum Disorder.Brain sciences · 2024Article
- In-the-Wild Affect Analysis of Children with ASD Using Heart Rate.Sensors (Basel, Switzerland) · 2023Article
- Positive, negative, neutral-or unknown? The perceived valence of emotions expressed by young autistic children in a novel context suited to autism.Autism : the international journal of research and practice · 2022Article
- Facial Features Detection System To Identify Children With Autism Spectrum Disorder: Deep Learning Models.Computational and mathematical methods in medicine · 2022Article
- Can Autism Be Diagnosed with Artificial Intelligence? A Narrative Review.Diagnostics (Basel, Switzerland) · 2021Review
- Déficits précoces de l'empathie et psychopathologie Early empathy deficits and psychopathology.Neuropsychiatrie de l'enfance et de l'adolescence · 2021Article
- Is Smiling the Key? Machine Learning Analytics Detect Subtle Patterns in Micro-Expressions of Infants with ASD.Journal of clinical medicine · 2021Article
- Automatic Classification of Adult Males With and Without Autism Spectrum Disorder by Non-contact Measurement of Autonomic Nervous System Activation.Frontiers in psychiatry · 2021Article
- Face and Gesture Analysis for Health Informatics.Proceedings of the ... ACM International Conference on Multimodal Interaction. ICMI (Conference) · 2020Article
- Exploring Complexity of Facial Dynamics in Autism Spectrum Disorder.IEEE transactions on affective computingArticle
Corrections and comments
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Authors and funding
12 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Computer vision combined with human annotation could offer a novel method for exploring facial expression (FE) dynamics in children with autism spectrum disorder (ASD). Methods: We recruited 157 children with typical development (TD) and 36 children with ASD in Paris and Nice to perform two experimental tasks to produce FEs with emotional valence. FEs were explored by judging ratings and by random forest (RF) classifiers. To do so, we located a set of 49 facial landmarks in the task videos, we generated a set of geometric and appearance features and we used RF classifiers to explore how children with ASD differed from TD children when producing FEs. Results: Using multivariate models including other factors known to predict FEs (age, gender, intellectual quotient, emotion subtype, cultural background), ratings from expert raters showed that children with ASD had more difficulty producing FEs than TD children. In addition, when we explored how RF classifiers performed, we found that classification tasks, except for those for sadness, were highly accurate and that RF classifiers needed more facial landmarks to achieve the best classification for children with ASD. Confusion matrices showed that when RF classifiers were tested in children with ASD, anger was often confounded with happiness. Limitations: The sample size of the group of children with ASD was lower than that of the group of TD children. By using several control calculations, we tried to compensate for this limitation. Conclusion: Children with ASD have more difficulty producing socially meaningful FEs. The computer vision methods we used to explore FE dynamics also highlight that the production of FEs in children with ASD carries more ambiguity.
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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.