ArticleProceedings of the ... International Conference on Automatic Face and Gesture Recognition. IEEE International Conference on Automatic Face & Gesture Recognition2019
Cross-domain AU Detection: Domains, Learning Approaches, and Measures.
Article in Proceedings of the ... International Conference on Automatic Face and Gesture Recognition. IEEE International Conference on Automatic Face & Gesture Recognition, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
9 citing papers in PubMed.
- LibreFace 2.0: A Generalizable Facial Expression Analysis Toolkit Leveraging Synthetic Data.Proceedings of the ... International Conference on Automatic Face and Gesture Recognition. IEEE International Conference on Automatic Face & Gesture Recognition · 2026Article
- Time to retire F1-binary score for action unit detection.Pattern recognition letters · 2024Article
- Infant AFAR: Automated facial action recognition in infants.Behavior research methods · 2023Article
- Novel Method for Three-Dimensional Facial Expression Recognition Using Self-Normalizing Neural Networks and Mobile Devices.Geburtshilfe und Frauenheilkunde · 2022Article
- Assessing Automated Facial Action Unit Detection Systems for Analyzing Cross-Domain Facial Expression Databases.Sensors (Basel, Switzerland) · 2021Article
- Crossing Domains for AU Coding: Perspectives, Approaches, and Measures.IEEE transactions on biometrics, behavior, and identity science · 2020Article
- D-PAttNet: Dynamic Patch-Attentive Deep Network for Action Unit Detection.Frontiers in computer science · 2019Article
- AFAR: A Deep Learning Based Tool for Automated Facial Affect Recognition.Proceedings of the ... International Conference on Automatic Face and Gesture Recognition. IEEE International Conference on Automatic Face & Gesture Recognition · 2019Article
- Automated measurement of infant and mother Duchenne facial expressions in the Face-to-Face/Still-Face.Infancy : the official journal of the International Society on Infant StudiesArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
Abstract
Facial action unit (AU) detectors have performed well when trained and tested within the same domain. Do AU detectors transfer to new domains in which they have not been trained? To answer this question, we review literature on cross-domain transfer and conduct experiments to address limitations of prior research. We evaluate both deep and shallow approaches to AU detection (CNN and SVM, respectively) in two large, well-annotated, publicly available databases, Expanded BP4D+ and GFT. The databases differ in observational scenarios, participant characteristics, range of head pose, video resolution, and AU base rates. For both approaches and databases, performance decreased with change in domain, often to below the threshold needed for behavioral research. Decreases were not uniform, however. They were more pronounced for GFT than for Expanded BP4D+ and for shallow relative to deep learning. These findings suggest that more varied domains and deep learning approaches may be better suited for promoting generalizability. Until further improvement is realized, caution is warranted when applying AU classifiers from one domain to another.
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.