ArticleSensors (Basel, Switzerland)2021
Assessing Automated Facial Action Unit Detection Systems for Analyzing Cross-Domain Facial Expression Databases.
Article in Sensors (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed.
- Artificial intelligence analysis of facial movements and satisfaction in online medical lectures: a cross-sectional study.BMC medical education · 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
- Facial obstructions and baseline correction shape affective computing's detection of emotion-behavior relationships.Frontiers in psychology · 2026Article
- Facial Expression Metrics as Digital Biomarkers of Neurologic Disease.Neurology open access · 2025Article
- AI-Based Facial Emotion Analysis in Infants During Complimentary Feeding: A Descriptive Study of Maternal and Infant Influences.Nutrients · 2025Article
- Emotional mimicry and smiling behaviors in schizophrenia: An ecological approach.Schizophrenia (Heidelberg, Germany) · 2025Article
- Facial representations of complex affective states combining pain and a negative emotion.Scientific reports · 2024Article
- Understanding Naturalistic Facial Expressions with Deep Learning and Multimodal Large Language Models.Sensors (Basel, Switzerland) · 2023Review
- Development of the RIKEN database for dynamic facial expressions with multiple angles.Scientific reports · 2023Article
- Electromyographic Validation of Spontaneous Facial Mimicry Detection Using Automated Facial Action Coding.Sensors (Basel, Switzerland) · 2023Article
- Neural mechanisms for emotional contagion and spontaneous mimicry of live facial expressions.Philosophical transactions of the Royal Society of London. Series B, Biological sciences · 2023Article
- Eye Tracking Research on the Influence of Spatial Frequency and Inversion Effect on Facial Expression Processing in Children with Autism Spectrum Disorder.Brain sciences · 2022Article
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- Computational Process of Sharing Emotion: An Authentic Information Perspective.Frontiers in psychology · 2022Article
- An Android for Emotional Interaction: Spatiotemporal Validation of Its Facial Expressions.Frontiers in psychology · 2021Article
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4 authors.
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Abstract
In the field of affective computing, achieving accurate automatic detection of facial movements is an important issue, and great progress has already been made. However, a systematic evaluation of systems that now have access to the dynamic facial database remains an unmet need. This study compared the performance of three systems (FaceReader, OpenFace, AFARtoolbox) that detect each facial movement corresponding to an action unit (AU) derived from the Facial Action Coding System. All machines could detect the presence of AUs from the dynamic facial database at a level above chance. Moreover, OpenFace and AFAR provided higher area under the receiver operating characteristic curve values compared to FaceReader. In addition, several confusion biases of facial components (e.g., AU12 and AU14) were observed to be related to each automated AU detection system and the static mode was superior to dynamic mode for analyzing the posed facial database. These findings demonstrate the features of prediction patterns for each system and provide guidance for research on facial expressions.
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