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Facial Action Unit Detection

Facial action unit detection is the task of detecting action units from a video of a face - for example, lip tightening and cheek raising.

( Image credit: Self-supervised Representation Learning from Videos for Facial Action Unit Detection )

Papers

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Showing 51–96 of 96 papers

TitleStatusHype
Computational efficient deep neural network with difference attention maps for facial action unit detection—0
HiCOMEX: Facial Action Unit Recognition Based on Hierarchy Intensity Distribution and COMEX Relation Learning—0
Facial Action Unit Detection using 3D Facial Landmarks—0
Facial Action Unit Detection on ICU Data for Pain Assessment—0
JAA-Net: Joint Facial Action Unit Detection and Face Alignment via Adaptive AttentionCode1
GeoConv: Geodesic Guided Convolution for Facial Action Unit Recognition—0
Upper, Middle and Lower Region Learning for Facial Action Unit Detection—0
Multitask Emotion Recognition with Incomplete LabelsCode1
Facial Action Unit Detection via Adaptive Attention and Relation—0
Multi-label Co-regularization for Semi-supervised Facial Action Unit RecognitionCode0
Relation Modeling with Graph Convolutional Networks for Facial Action Unit Detection—0
Fast and Effective Adaptation of Facial Action Unit Detection Deep Model—0
Local Relationship Learning With Person-Specific Shape Regularization for Facial Action Unit Detection—0
Self-Supervised Representation Learning From Videos for Facial Action Unit DetectionCode0
Semantic Relationships Guided Representation Learning for Facial Action Unit Recognition—0
Learned 3D Shape Representations Using Fused Geometrically Augmented Images: Application to Facial Expression and Action Unit Detection—0
Unconstrained Facial Action Unit Detection via Latent Feature DomainCode0
The Truth and Nothing but the Truth: Multimodal Analysis for Deception Detection—0
Facial Action Unit Detection Using Attention and Relation Learning—0
Transfer Learning for Action Unit Recognition—0
Expression Empowered ResiDen Network for Facial Action Unit Detection—0
Classifier Learning With Prior Probabilities for Facial Action Unit Recognition—0
Weakly Supervised Facial Action Unit Recognition Through Adversarial Training—0
The Intelligent ICU Pilot Study: Using Artificial Intelligence Technology for Autonomous Patient Monitoring—0
Deep Adaptive Attention for Joint Facial Action Unit Detection and Face AlignmentCode0
Deep Structure Inference Network for Facial Action Unit Recognition—0
Accurate Facial Parts Localization and Deep Learning for 3D Facial Expression Recognition—0
Deep Facial Action Unit Recognition From Partially Labeled Data—0
Constrained Joint Cascade Regression Framework for Simultaneous Facial Action Unit Recognition and Facial Landmark Detection—0
Optimizing Filter Size in Convolutional Neural Networks for Facial Action Unit Recognition—0
Incremental Boosting Convolutional Neural Network for Facial Action Unit Recognition—0
Improving Speech Related Facial Action Unit Recognition by Audiovisual Information Fusion—0
Multi-View Dynamic Facial Action Unit DetectionCode0
Learning with Privileged Information for Multi-Label Classification—0
FERA 2017 - Addressing Head Pose in the Third Facial Expression Recognition and Analysis Challenge—0
EAC-Net: A Region-based Deep Enhancing and Cropping Approach for Facial Action Unit Detection—0
Linear Disentangled Representation Learning for Facial ActionsCode0
Modeling Spatial and Temporal Cues for Multi-label Facial Action Unit Detection—0
Deep Region and Multi-Label Learning for Facial Action Unit DetectionCode0
Confidence Preserving Machine for Facial Action Unit Detection—0
Multi-Conditional Latent Variable Model for Joint Facial Action Unit Detection—0
Learning to Transfer: Transferring Latent Task Structures and Its Application to Person-Specific Facial Action Unit Detection—0
Time Series Classification using the Hidden-Unit Logistic Model—0
Joint Patch and Multi-Label Learning for Facial Action Unit Detection—0
Relative Facial Action Unit Detection—0
Selective Transfer Machine for Personalized Facial Action Unit Detection—0
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