ArticleBehavior research methods2021
Human and machine validation of 14 databases of dynamic facial expressions.
Article in Behavior research methods, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers.
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28 citing papers in PubMed.
- Detecting Young Children's Deception Using Facial Expression Analysis With High Dimensional Statistical Methods.Developmental science · 2026Article
- Double dissociation of dynamic and static face perception provides causal evidence for a third visual pathway.Nature communications · 2025Article
- Facial Emotion Recognition of 16 Distinct Emotions From Smartphone Videos: Comparative Study of Machine Learning and Human Performance.Journal of medical Internet research · 2025Article
- The portrait of Dorian Gray: spontaneous expression of happiness is an invariant kinematic marker.Frontiers in psychology · 2025Article
- Identification of smile events using automated facial expression recognition during the Autism Diagnostic Observation Schedule (ADOS-2): a proof-of-principle study.Frontiers in psychiatry · 2025Article
- Diverse Face Images (DFI): Validated for racial representation and eye gaze.Behavior research methods · 2024Article
- Can Perceivers Differentiate Intense Facial Expressions? Eye Movement Patterns.Behavioral sciences (Basel, Switzerland) · 2024Article
- "Show Me What You Got": The Nomological Network of the Ability to Pose Facial Emotion Expressions.Journal of Intelligence · 2024Article
- Understanding Naturalistic Facial Expressions with Deep Learning and Multimodal Large Language Models.Sensors (Basel, Switzerland) · 2023Review
- Py-Feat: Python Facial Expression Analysis Toolbox.Affective science · 2023Article
- Characteristics of healthy Japanese young adults with respect to recognition of facial expressions: a preliminary study.BMC psychology · 2023Article
- Article
- Automatic facial coding predicts self-report of emotion, advertisement and brand effects elicited by video commercials.Frontiers in neuroscience · 2023Article
- Adults with a history of childhood maltreatment with and without mental disorders show alterations in the recognition of facial expressions.European journal of psychotraumatology · 2023Article
- Human and machine recognition of dynamic and static facial expressions: prototypicality, ambiguity, and complexity.Frontiers in psychology · 2023Review
- PDSTD - The Portsmouth Dynamic Spontaneous Tears Database.Behavior research methods · 2022Article
- Automatic Identification of a Depressive State in Primary Care.Healthcare (Basel, Switzerland) · 2022Article
- A systematic survey of face stimuli used in psychological research 2000-2020.Behavior research methods · 2022Article
- Commercial Use of Emotion Artificial Intelligence (AI): Implications for Psychiatry.Current psychiatry reports · 2022Review
- The emotion-facial expression link: evidence from human and automatic expression recognition.Psychological research · 2021Article
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4 authors.
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Abstract
With a shift in interest toward dynamic expressions, numerous corpora of dynamic facial stimuli have been developed over the past two decades. The present research aimed to test existing sets of dynamic facial expressions (published between 2000 and 2015) in a cross-corpus validation effort. For this, 14 dynamic databases were selected that featured facial expressions of the basic six emotions (anger, disgust, fear, happiness, sadness, surprise) in posed or spontaneous form. In Study 1, a subset of stimuli from each database (N = 162) were presented to human observers and machine analysis, yielding considerable variance in emotion recognition performance across the databases. Classification accuracy further varied with perceived intensity and naturalness of the displays, with posed expressions being judged more accurately and as intense, but less natural compared to spontaneous ones. Study 2 aimed for a full validation of the 14 databases by subjecting the entire stimulus set (N = 3812) to machine analysis. A FACS-based Action Unit (AU) analysis revealed that facial AU configurations were more prototypical in posed than spontaneous expressions. The prototypicality of an expression in turn predicted emotion classification accuracy, with higher performance observed for more prototypical facial behavior. Furthermore, technical features of each database (i.e., duration, face box size, head rotation, and motion) had a significant impact on recognition accuracy. Together, the findings suggest that existing databases vary in their ability to signal specific emotions, thereby facing a trade-off between realism and ecological validity on the one end, and expression uniformity and comparability on the other.
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