Facial Expression Recognition
52 papers with code • 18 benchmarks • 17 datasets
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 )
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CK+
AffectNet
DISFA
FER2013
Oulu-CASIA
RaFD
JAFFE
BP4D
MMI
FER+
Subtasks
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
kaiwang960112/Self-Cure-Network
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CVPR 2020
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 #2 on
Facial Expression Recognition
on CK+
Region Attention Networks for Pose and Occlusion Robust Facial Expression Recognition
kaiwang960112/Challenge-condition-FER-dataset
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Extensive experiments show that our RAN and region biased loss largely improve the performance of FER with occlusion and variant pose.
Ranked #2 on
Facial Expression Recognition
on SFEW
Challenges in Representation Learning: A report on three machine learning contests
phamquiluan/ResidualMaskingNetwork
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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
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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 #2 on
Facial Expression Recognition
on FER+
(using extra training data)
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.
Deep-Emotion: Facial Expression Recognition Using Attentional Convolutional Network
In recent years, several works proposed an end-to-end framework for facial expression recognition, using deep learning models.
Ranked #1 on
Facial Expression Recognition
on FERG
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.
Compacting, Picking and Growing for Unforgetting Continual Learning
First, it can avoid forgetting (i. e., learn new tasks while remembering all previous tasks).
