ArticleFrontiers in psychology2021
Automatic Facial Expression Recognition in Standardized and Non-standardized Emotional Expressions.
Article in Frontiers in psychology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 38 papers, 1 of them a synthesis that pooled it.
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Who cites it
38 citing papers in PubMed, 1 synthesis or guideline pooled it, 126 citations in OpenAlex.
- New Trends in Emotion Recognition Using Image Analysis by Neural Networks, A Systematic Review.Sensors (Basel, Switzerland) · 2023Pooled it
- Reduced task adaptation and contextual awareness in autistic adults during facial emotion recognition: evidence from mixed-effects modeling and automated facial analysis.Molecular autism · 2026Article
- Evoked emotions in anorexia nervosa: neural and behavioural correlates of social-emotional processing.Translational psychiatry · 2026Article
- Emotion-specific vocabulary is associated with preschoolers' emotion knowledge and behavioral emotion regulation.Scientific reports · 2026Article
- Mismatching Expressions: Spatiotemporal and Kinematic Differences in Autistic and Non-Autistic Facial Expressions.Autism research : official journal of the International Society for Autism Research · 2026Article
- Subtle impairments of facial emotion expressions in individuals at ultra-high risk for psychosis.Frontiers in psychiatry · 2026Article
- Facial obstructions and baseline correction shape affective computing's detection of emotion-behavior relationships.Frontiers in psychology · 2026Article
- Evaluating the robustness of specialized and general-purpose facial expression recognition systems across varied scenarios.Frontiers in artificial intelligence · 2026Article
- Neural signals gradually shift towards inferior frontal gyrus and long-term effects following facial imitation-based social learning.NPJ science of learning · 2025Article
- Comparing three algorithms of automated facial expression analysis in autistic children: different sensitivities but consistent proportions.Molecular autism · 2025Article
- Application of Artificial Intelligence Software to Identify Emotions of Lung Cancer Patients in Preoperative Health Education: A Cross-Sectional Study.Journal of nursing scholarship : an official publication of Sigma Theta Tau International Honor Society of Nursing · 2025Article
- Mobile Game Evaluation Method Based on Data Mining of Affective Time Series.Sensors (Basel, Switzerland) · 2025Article
- Emotional contagion in dyadic online video conferences-empirical evidence based on self-report and facial expression data.Frontiers in psychology · 2025Article
- Facial expression recognition through muscle synergies and estimation of facial keypoint displacements through a skin-musculoskeletal model using facial sEMG signals.Frontiers in bioengineering and biotechnology · 2025Article
- Scoring facial attractiveness with deep convolutional neural networks: How training on standardized images reduces the bias of facial expressions.Orthodontics & craniofacial research · 2024Article
- Wireless high-resolution surface facial electromyography mask for discrimination of standardized facial expressions in healthy adults.Scientific reports · 2024Article
- Measuring dynamic emotional experiences in response to media stimuli.Frontiers in psychology · 2024Review
- Electromyographic Validation of Spontaneous Facial Mimicry Detection Using Automated Facial Action Coding.Sensors (Basel, Switzerland) · 2023Article
- High-resolution surface electromyographic activities of facial muscles during the six basic emotional expressions in healthy adults: a prospective observational study.Scientific reports · 2023Observational
- Development of webcam-collected and artificial-intelligence-derived social and cognitive performance measures for neurodevelopmental genetic syndromes.American journal of medical genetics. Part C, Seminars in medical genetics · 2023Article
Corrections and comments
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Authors and funding
3 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Emotional facial expressions can inform researchers about an individual's emotional state. Recent technological advances open up new avenues to automatic Facial Expression Recognition (FER). Based on machine learning, such technology can tremendously increase the amount of processed data. FER is now easily accessible and has been validated for the classification of standardized prototypical facial expressions. However, applicability to more naturalistic facial expressions still remains uncertain. Hence, we test and compare performance of three different FER systems (Azure Face API, Microsoft; Face++, Megvii Technology; FaceReader, Noldus Information Technology) with human emotion recognition (A) for standardized posed facial expressions (from prototypical inventories) and (B) for non-standardized acted facial expressions (extracted from emotional movie scenes). For the standardized images, all three systems classify basic emotions accurately (FaceReader is most accurate) and they are mostly on par with human raters. For the non-standardized stimuli, performance drops remarkably for all three systems, but Azure still performs similarly to humans. In addition, all systems and humans alike tend to misclassify some of the non-standardized emotional facial expressions as neutral. In sum, emotion recognition by automated facial expression recognition can be an attractive alternative to human emotion recognition for standardized and non-standardized emotional facial expressions. However, we also found limitations in accuracy for specific facial expressions; clearly there is need for thorough empirical evaluation to guide future developments in computer vision of emotional facial expressions.
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