ArticlePeerJ. Computer science2021
Comparing supervised and unsupervised approaches to multimodal emotion recognition.
Article in PeerJ. Computer science, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Article
- MindTS-MMD: A Chinese Multimodal Dataset for Emotion Expression Analysis in Children with Tourette Syndrome.Scientific data · 2026Article
- Dynamic multimodal expressions support inference of both presence and relative salience of blended emotions.Scientific reports · 2026Article
- Music and body motion contribute asymmetrically to emotion perception in traditional Chinese plucked-instrument performance.Scientific reports · 2026Article
- Non-verbal effecting - animal research sheds light on human emotion communication.Biological reviews of the Cambridge Philosophical Society · 2025Review
- Refining outcome prediction after traumatic brain injury with machine learning algorithms.Scientific reports · 2024Article
- Article
- Multimodal machine learning for analysing multifactorial causes of disease-The case of childhood overweight and obesity in Mexico.Frontiers in public health · 2024Article
- Predictive models for clinical decision making: Deep dives in practical machine learning.Journal of internal medicine · 2022Review
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Authors and funding
3 authors.
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Abstract
We investigated emotion classification from brief video recordings from the GEMEP database wherein actors portrayed 18 emotions. Vocal features consisted of acoustic parameters related to frequency, intensity, spectral distribution, and durations. Facial features consisted of facial action units. We first performed a series of person-independent supervised classification experiments. Best performance (AUC = 0.88) was obtained by merging the output from the best unimodal vocal (Elastic Net, AUC = 0.82) and facial (Random Forest, AUC = 0.80) classifiers using a late fusion approach and the product rule method. All 18 emotions were recognized with above-chance recall, although recognition rates varied widely across emotions (
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