ArticlePloS one2020
A performance comparison of eight commercially available automatic classifiers for facial affect recognition.
Article in PloS one, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 45 papers, 1 of them a synthesis that pooled it.
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Who cites it
45 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Application of facial neuromuscular electrical stimulation (fNMES) in psychophysiological research: Practical recommendations based on a systematic review of the literature.Behavior research methods · 2024Guideline
- More than just smiling: how positive emotions are expressed on the face.Philosophical transactions of the Royal Society of London. Series B, Biological sciences · 2026Article
- Detecting Young Children's Deception Using Facial Expression Analysis With High Dimensional Statistical Methods.Developmental science · 2026Article
- Continuous Emotion Recognition Using EDA-Graphs: A Graph Signal Processing Approach for Affective Dimension Estimation.Applied sciences (Basel, Switzerland) · 2026Article
- Discrepancies between feeling and expressing: Perceptions of autistic and non-autistic emotional expressions by non-autistic observers.Autism : the international journal of research and practice · 2026Article
- Children's affect: Automated coding, context effects, relations with maternal affect, and heritability.Child development · 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
- Evaluating the robustness of specialized and general-purpose facial expression recognition systems across varied scenarios.Frontiers in artificial intelligence · 2026Article
- Facial obstructions and baseline correction shape affective computing's detection of emotion-behavior relationships.Frontiers in psychology · 2026Article
- Facial Emotion Recognition of 16 Distinct Emotions From Smartphone Videos: Comparative Study of Machine Learning and Human Performance.Journal of medical Internet research · 2025Article
- A Review of 25 Spontaneous and Dynamic Facial Expression Databases of Basic Emotions.Affective science · 2025Review
- An Artificial Intelligence Model for Sensing Affective Valence and Arousal from Facial Images.Sensors (Basel, Switzerland) · 2025Article
- The influence of conscientiousness and context on the emotional response during understanding.Frontiers in psychology · 2025Article
- Emotional contagion in dyadic online video conferences-empirical evidence based on self-report and facial expression data.Frontiers in psychology · 2025Article
- Structural neuroanatomy of human facial behaviors.Social cognitive and affective neuroscience · 2024Article
- Sensing emotional valence and arousal dynamics through automated facial action unit analysis.Scientific reports · 2024Article
- Emotion Classification Based on Pulsatile Images Extracted from Short Facial Videos via Deep Learning.Sensors (Basel, Switzerland) · 2024Article
- Automating the analysis of facial emotion expression dynamics: A computational framework and application in psychotic disorders.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- "Show Me What You Got": The Nomological Network of the Ability to Pose Facial Emotion Expressions.Journal of Intelligence · 2024Article
- The relationship between charitable giving and emotional facial expressions: Results from affective computing.Heliyon · 2024Article
Corrections and comments
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Authors and funding
4 authors.
Funding
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Abstract
In the wake of rapid advances in automatic affect analysis, commercial automatic classifiers for facial affect recognition have attracted considerable attention in recent years. While several options now exist to analyze dynamic video data, less is known about the relative performance of these classifiers, in particular when facial expressions are spontaneous rather than posed. In the present work, we tested eight out-of-the-box automatic classifiers, and compared their emotion recognition performance to that of human observers. A total of 937 videos were sampled from two large databases that conveyed the basic six emotions (happiness, sadness, anger, fear, surprise, and disgust) either in posed (BU-4DFE) or spontaneous (UT-Dallas) form. Results revealed a recognition advantage for human observers over automatic classification. Among the eight classifiers, there was considerable variance in recognition accuracy ranging from 48% to 62%. Subsequent analyses per type of expression revealed that performance by the two best performing classifiers approximated those of human observers, suggesting high agreement for posed expressions. However, classification accuracy was consistently lower (although above chance level) for spontaneous affective behavior. The findings indicate potential shortcomings of existing out-of-the-box classifiers for measuring emotions, and highlight the need for more spontaneous facial databases that can act as a benchmark in the training and testing of automatic emotion recognition systems. We further discuss some limitations of analyzing facial expressions that have been recorded in controlled environments.
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