Facial Expression Recognition Edit Task
Computer Vision • Facial Recognition and Modelling
About
Facial expression recognition is the task of classifying the expressions on face images into various categories such as anger, fear, surprise, sadness, happiness and so on.
( Image credit: DeXpression )
Benchmarks
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Subtasks
Datasets
Greatest papers with code
Training Deep Networks for Facial Expression Recognition with Crowd-Sourced Label Distribution
Crowd sourcing has become a widely adopted scheme to collect ground truth labels.
Suppressing Uncertainties for Large-Scale Facial Expression Recognition
CVPR 2020
• kaiwang960112/Self-Cure-Network
• 
Annotating a qualitative large-scale facial expression dataset is extremely difficult due to the uncertainties caused by ambiguous facial expressions, low-quality facial images, and the subjectiveness of annotators.
Frame attention networks for facial expression recognition in videos
The feature embedding module is a deep Convolutional Neural Network (CNN) which embeds face images into feature vectors.
Ranked #1 on
Facial Expression Recognition
on CK+
Region Attention Networks for Pose and Occlusion Robust Facial Expression Recognition
• kaiwang960112/Challenge-condition-FER-dataset
•
Extensive experiments show that our RAN and region biased loss largely improve the performance of FER with occlusion and variant pose.
Ranked #1 on
Facial Expression Recognition
on SFEW
MicroExpNet: An Extremely Small and Fast Model For Expression Recognition From Face Images
On the other hand, KD is proved to be useful for model compression for the FER problem, and we discovered that its effects gets more and more significant with the decreasing model size.
Ranked #1 on
Facial Expression Recognition
on Oulu-CASIA
FACIAL EXPRESSION RECOGNITION KNOWLEDGE DISTILLATION MODEL COMPRESSION
Challenges in Representation Learning: A report on three machine learning contests
• phamquiluan/ResidualMaskingNetwork
•
The ICML 2013 Workshop on Challenges in Representation Learning focused on three challenges: the black box learning challenge, the facial expression recognition challenge, and the multimodal learning challenge.
Ranked #1 on
Facial Expression Recognition
on FER2013
(using extra training data)
Efficient Facial Feature Learning with Wide Ensemble-based Convolutional Neural Networks
• siqueira-hc/Efficient-Facial-Feature-Learning-with-Wide-Ensemble-based-Convolutional-Neural-Networks
•
Experiments on large-scale datasets suggest that ESRs reduce the remaining residual generalization error on the AffectNet and FER+ datasets, reach human-level performance, and outperform state-of-the-art methods on facial expression recognition in the wild using emotion and affect concepts.
Ranked #1 on
Facial Expression Recognition
on FER+
(using extra training data)
Covariance Pooling For Facial Expression Recognition
In this work, we explore the benefits of using a man- ifold network structure for covariance pooling to improve facial expression recognition.
Facial Motion Prior Networks for Facial Expression Recognition
Deep learning based facial expression recognition (FER) has received a lot of attention in the past few years.
Ranked #2 on
Facial Expression Recognition
on MMI
DeepFaceFlow: In-the-wild Dense 3D Facial Motion Estimation
Dense 3D facial motion capture from only monocular in-the-wild pairs of RGB images is a highly challenging problem with numerous applications, ranging from facial expression recognition to facial reenactment.
3D RECONSTRUCTION FACIAL EXPRESSION RECOGNITION MOTION CAPTURE MOTION ESTIMATION