ArticleIEEE transactions on image processing : a publication of the IEEE Signal Processing Society2016
Confidence Preserving Machine for Facial Action Unit Detection.
Article in IEEE transactions on image processing : a publication of the IEEE Signal Processing Society, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
7 citing papers in PubMed, 22 citations in OpenAlex.
- Time to retire F1-binary score for action unit detection.Pattern recognition letters · 2024Article
- Learning Facial Action Units with Spatiotemporal Cues and Multi-label Sampling.Image and vision computing · 2019Article
- Learning Facial Action Units from Web Images with Scalable Weakly Supervised Clustering.Proceedings. IEEE Computer Society Conference on Computer Vision and Pattern Recognition · 2018Article
- Automatic Action Unit Detection in Infants Using Convolutional Neural Network.International Conference on Affective Computing and Intelligent Interaction and workshops : [proceedings]. ACII (Conference) · 2017Article
- Selective Transfer Machine for Personalized Facial Expression Analysis.IEEE transactions on pattern analysis and machine intelligence · 2017Article
- Confidence Preserving Machine for Facial Action Unit Detection.IEEE transactions on image processing : a publication of the IEEE Signal Processing Society · 2016Article
- Sayette Group Formation Task (GFT) Spontaneous Facial Expression Database.Proceedings of the ... International Conference on Automatic Face and Gesture Recognition. IEEE International Conference on Automatic Face & Gesture RecognitionArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors at 3 institutions in 2 countries.
Funding
Abstract
Facial action unit (AU) detection from video has been a long-standing problem in the automated facial expression analysis. While progress has been made, accurate detection of facial AUs remains challenging due to ubiquitous sources of errors, such as inter-personal variability, pose, and low-intensity AUs. In this paper, we refer to samples causing such errors as hard samples, and the remaining as easy samples. To address learning with the hard samples, we propose the confidence preserving machine (CPM), a novel two-stage learning framework that combines multiple classifiers following an "easy-to-hard" strategy. During the training stage, CPM learns two confident classifiers. Each classifier focuses on separating easy samples of one class from all else, and thus preserves confidence on predicting each class. During the test stage, the confident classifiers provide "virtual labels" for easy test samples. Given the virtual labels, we propose a quasi-semi-supervised (QSS) learning strategy to learn a person-specific classifier. The QSS strategy employs a spatio-temporal smoothness that encourages similar predictions for samples within a spatio-temporal neighborhood. In addition, to further improve detection performance, we introduce two CPM extensions: iterative CPM that iteratively augments training samples to train the confident classifiers, and kernel CPM that kernelizes the original CPM model to promote nonlinearity. Experiments on four spontaneous data sets GFT, BP4D, DISFA, and RU-FACS illustrate the benefits of the proposed CPM models over baseline methods and the state-of-the-art semi-supervised learning and transfer learning methods.
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.