ArticleConference on Computer Vision and Pattern Recognition Workshops. IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Workshops2018
FACSCaps: Pose-Independent Facial Action Coding with Capsules.
Article in Conference on Computer Vision and Pattern Recognition Workshops. IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Workshops, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
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The trial behind it
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Who cites it
3 citing papers in PubMed.
- Crossing Domains for AU Coding: Perspectives, Approaches, and Measures.IEEE transactions on biometrics, behavior, and identity science · 2020Article
- Unmasking the Devil in the Details: What Works for Deep Facial Action Coding?BMVC : proceedings of the British Machine Vision Conference. British Machine Vision Conference · 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
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
Most automated facial expression analysis methods treat the face as a 2D object, flat like a sheet of paper. That works well provided images are frontal or nearly so. In real- world conditions, moderate to large head rotation is common and system performance to recognize expression degrades. Multi-view Convolutional Neural Networks (CNNs) have been proposed to increase robustness to pose, but they require greater model sizes and may generalize poorly across views that are not included in the training set. We propose FACSCaps architecture to handle multi-view and multi-label facial action unit (AU) detection within a single model that can generalize to novel views. Additionally, FACSCaps's ability to synthesize faces enables insights into what is leaned by the model. FACSCaps models video frames using matrix capsules, where hierarchical pose relationships between face parts are built into internal representations. The model is trained by jointly optimizing a multi-label loss and the reconstruction accuracy. FACSCaps was evaluated using the FERA 2017 facial expression dataset that includes spontaneous facial expressions in a wide range of head orientations. FACSCaps outperformed both state-of-the-art CNNs and their temporal extensions.
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.