SOTAVerified

Facial Expression Recognition (FER)

Facial Expression Recognition (FER) is a computer vision task aimed at identifying and categorizing emotional expressions depicted on a human face. The goal is to automate the process of determining emotions in real-time, by analyzing the various features of a face such as eyebrows, eyes, mouth, and other features, and mapping them to a set of emotions such as anger, fear, surprise, sadness and happiness.

( Image credit: DeXpression )

Papers

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Showing 201–250 of 492 papers

TitleStatusHype
Spatio-Temporal Transformer for Dynamic Facial Expression Recognition in the Wild—0
MixAugment & Mixup: Augmentation Methods for Facial Expression Recognition—0
Uncertain Label Correction via Auxiliary Action Unit Graphs for Facial Expression Recognition—0
Vision Transformer Equipped with Neural Resizer on Facial Expression Recognition Task—0
Adaptively Enhancing Facial Expression Crucial Regions via Local Non-Local Joint Network—0
Facial Expression Recognition with Swin Transformer—0
Clip-aware expressive feature learning for video-based facial expression recognitionCode0
Coarse-to-Fine Cascaded Networks with Smooth Predicting for Video Facial Expression Recognition—0
Privileged Attribution Constrained Deep Networks for Facial Expression Recognition—0
Facial Expression Recognition based on Multi-head Cross Attention Network—0
Your "Attention" Deserves Attention: A Self-Diversified Multi-Channel Attention for Facial Action Analysis—0
Assessing Gender Bias in Predictive Algorithms using eXplainable AI—0
FERV39k: A Large-Scale Multi-Scene Dataset for Facial Expression Recognition in Videos—0
Robust facial expression recognition with global‑local joint representation learning—0
Ad-Corre: Adaptive Correlation-Based Loss for Facial Expression Recognition in the WildCode0
Real-Time Facial Expression Recognition using Facial Landmarks and Neural Networks—0
2D+3D facial expression recognition via embedded tensor manifold regularization—0
Explore the Expression: Facial Expression Generation using Auxiliary Classifier Generative Adversarial NetworkCode0
Towards a General Deep Feature Extractor for Facial Expression Recognition—0
When Facial Expression Recognition Meets Few-Shot Learning: A Joint and Alternate Learning Framework—0
Cross-Centroid Ripple Pattern for Facial Expression Recognition—0
The Effect of Model Compression on Fairness in Facial Expression Recognition—0
Multi-Dimensional, Nuanced and Subjective - Measuring the Perception of Facial Expressions—0
Face Trees for Expression Recognition—0
Teacher-Student Training and Triplet Loss to Reduce the Effect of Drastic Face Occlusion—0
Deep Convolution Network Based Emotion Analysis for Automatic Detection of Mild Cognitive Impairment in the Elderly—0
Facial Emotion Recognition: A multi-task approach using deep learningCode0
Domain Generalisation for Apparent Emotional Facial Expression Recognition across Age-Groups—0
Comparing Facial Expression Recognition in Humans and Machines: Using CAM, GradCAM, and Extremal Perturbation—0
Label quality in AffectNet: results of crowd-based re-annotation—0
Research on facial expression recognition based on Multimodal data fusion and neural network—0
MFEViT: A Robust Lightweight Transformer-based Network for Multimodal 2D+3D Facial Expression Recognition—0
Expression Snippet Transformer for Robust Video-based Facial Expression Recognition—0
Generating Dataset For Large-scale 3D Facial Emotion Recognition—0
What happens in Face during a facial expression? Using data mining techniques to analyze facial expression motion vectors—0
TransFER: Learning Relation-aware Facial Expression Representations with Transformers—0
Facial expression recognition with grid-wise attention and visual transformerCode0
Understanding and Mitigating Annotation Bias in Facial Expression Recognition—0
Self-supervised Contrastive Learning of Multi-view Facial Expressions—0
Spatiotemporal Contrastive Learning of Facial Expressions in Videos—0
Label Distribution Amendment with Emotional Semantic Correlations for Facial Expression Recognition—0
Spatial and Temporal Networks for Facial Expression Recognition in the Wild VideosCode0
Causal affect prediction model using a facial image sequenceCode0
Affect Expression Behaviour Analysis in the Wild using Consensual Collaborative TrainingCode0
Learning Vision Transformer with Squeeze and Excitation for Facial Expression Recognition—0
Recognizing Facial Expressions in the Wild using Multi-Architectural Representations based Ensemble Learning with Distillation—0
Critically examining the Domain Generalizability of Facial Expression Recognition models—0
MVT: Mask Vision Transformer for Facial Expression Recognition in the wild—0
Domain Adaptation for Facial Expression Classifier via Domain Discrimination and Gradient Reversal—0
An optimized Capsule-LSTM model for facial expression recognition with video sequences—0
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All datasetsAffectNetRAF-DBFER2013FER+Acted Facial Expressions In The Wild (AFEW)CK+FERPlusJAFFESFEWAff-Wild2BP4DDISFA

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ResEmoteNetAccuracy (7 emotion)72.93—Unverified
2NorfaceAccuracy (8 emotion)68.69—Unverified
3EmoAffectNetAccuracy (7 emotion)66.49—Unverified
4Emotion-GCNAccuracy (7 emotion)66.46—Unverified
5FaceBehaviorNetAccuracy (7 emotion)65.4—Unverified
6Ada-DFAccuracy (7 emotion)65.34—Unverified
7EACAccuracy (7 emotion)65.32—Unverified
8PAENetAccuracy (7 emotion)65.29—Unverified
9DACLAccuracy (7 emotion)65.2—Unverified
10DDAMFN++Accuracy (8 emotion)65.04—Unverified
#ModelMetricClaimedVerifiedStatus
1ResEmoteNetOverall Accuracy94.76—Unverified
2FMAEOverall Accuracy93.45—Unverified
3QCSOverall Accuracy93.02—Unverified
4NorfaceOverall Accuracy92.97—Unverified
5S2DOverall Accuracy92.57—Unverified
6BTNOverall Accuracy92.54—Unverified
7GReFELOverall Accuracy92.47—Unverified
8DDAMFN++Overall Accuracy92.34—Unverified
9DCJTOverall Accuracy92.24—Unverified
10POSTER++Overall Accuracy92.21—Unverified
#ModelMetricClaimedVerifiedStatus
1EfficientFERAccuracy82.47—Unverified
2FERNeXt-SDAFEAccuracy81.33—Unverified
3ResEmoteNetAccuracy79.79—Unverified
4Ensemble ResMaskingNet with 6 other CNNsAccuracy76.82—Unverified
5Mini-ResEmoteNet (A)Accuracy76.33—Unverified
6EmoNeXtAccuracy76.12—Unverified
7Segmentation VGG-19Accuracy75.97—Unverified
8Local Learning Deep+BOWAccuracy75.42—Unverified
9LHC-NetAccuracy74.42—Unverified
10Residual Masking NetworkAccuracy74.14—Unverified
#ModelMetricClaimedVerifiedStatus
1PAtt-LiteAccuracy95.55—Unverified
2GReFELAccuracy93.09—Unverified
3QCSAccuracy91.85—Unverified
4ResNet18 Dense ArchitectureAccuracy91.41—Unverified
5DDAMFNAccuracy90.74—Unverified
6KTNAccuracy90.49—Unverified
7Vit-base + MAEAccuracy90.18—Unverified
8FER-VTAccuracy90.04—Unverified
9EACAccuracy89.64—Unverified
10LResNet50E-IRAccuracy89.26—Unverified
#ModelMetricClaimedVerifiedStatus
1ResNet50Accuracy(on validation set)65.5—Unverified
2LResNet50E-IR (5 models with augmentation)Accuracy(on validation set)65.5—Unverified
3EACAccuracy(on validation set)65.32—Unverified
4LResNet50E-IR (1 model with augmentation)Accuracy(on validation set)63.7—Unverified
5LResNet50E-IR (1 model)Accuracy(on validation set)61.1—Unverified
6Multi-task EfficientNet-B0Accuracy(on validation set)59.27—Unverified
7resnet18_noisyAccuracy(on validation set)55.17—Unverified
8resnet18Accuracy(on validation set)51.18—Unverified
#ModelMetricClaimedVerifiedStatus
1EmoNeXtAccuracy (8 emotion)100—Unverified
2PAtt-LiteAccuracy (7 emotion)100—Unverified
3ViT + SEAccuracy (7 emotion)99.8—Unverified
4FANAccuracy (7 emotion)99.7—Unverified
5Nonlinear eval on SL + SSL puzzling (B0)Accuracy (7 emotion)98.23—Unverified
6DeepEmotionAccuracy (7 emotion)98—Unverified
7FN2ENAccuracy (8 emotion)96.8—Unverified
#ModelMetricClaimedVerifiedStatus
1KTNAccuracy(pretrained)90.49—Unverified
2RAN (VGG-16)Accuracy(pretrained)89.16—Unverified
3SENet TeacherAccuracy(pretrained)88.88—Unverified
4Local Learning Deep + BOWAccuracy(pretrained)87.76—Unverified
#ModelMetricClaimedVerifiedStatus
1TLAccuracy99.52—Unverified
2GReFELAccuracy96.67—Unverified
3ViTAccuracy94.83—Unverified
4DeepEmotionAccuracy92.8—Unverified
#ModelMetricClaimedVerifiedStatus
1Ada-DFAccuracy60.46—Unverified
2RAN (VGG16+ResNet18)Accuracy56.4—Unverified
3ViT + SEAccuracy54.29—Unverified
4Island LossAccuracy52.52—Unverified
#ModelMetricClaimedVerifiedStatus
1GReFELAccuracy72.48—Unverified
2EmoAffectNet LSTMUAR52.9—Unverified
#ModelMetricClaimedVerifiedStatus
1NorfaceICC0.74—Unverified
2Ours (VGG-F)ICC0.72—Unverified
#ModelMetricClaimedVerifiedStatus
1NorfaceICC0.67—Unverified
2Ours (VGG-F)ICC0.6—Unverified
#ModelMetricClaimedVerifiedStatus
1DeepEmotionAccuracy99.3—Unverified
2GReFELAccuracy98.18—Unverified
#ModelMetricClaimedVerifiedStatus
1DeXpressionAccuracy98.63—Unverified
2Facial Motion Prior NetworkAccuracy82.74—Unverified
#ModelMetricClaimedVerifiedStatus
1Dynamic MTLAccuracy (10-fold)89.6—Unverified
2PPDNAccuracy (10-fold)84.59—Unverified
#ModelMetricClaimedVerifiedStatus
1Covariance PoolingAccuracy87—Unverified
2Multi Label OutputAccuracy79.26—Unverified
#ModelMetricClaimedVerifiedStatus
1Covariance PoolingAccuracy58.14—Unverified
2VGG-VD-16Accuracy54.82—Unverified
#ModelMetricClaimedVerifiedStatus
1EfficientFaceAccuracy 85.87—Unverified
#ModelMetricClaimedVerifiedStatus
1Sequential forward selectionAccuracy88.7—Unverified
#ModelMetricClaimedVerifiedStatus
1EmoAffectNet LSTMUAR79—Unverified
#ModelMetricClaimedVerifiedStatus
1ResEmoteNetAccuracy75.67—Unverified
#ModelMetricClaimedVerifiedStatus
1ViT + SEAccuracy87.22—Unverified
#ModelMetricClaimedVerifiedStatus
1EmoAffectNet LSTMUAR69.7—Unverified
#ModelMetricClaimedVerifiedStatus
1EmoAffectNet LSTMUAR82.8—Unverified