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

Showing 51100 of 492 papers

TitleStatusHype
Efficient Facial Feature Learning with Wide Ensemble-based Convolutional Neural NetworksCode1
Uncertainty-aware Label Distribution Learning for Facial Expression RecognitionCode1
Robust Lightweight Facial Expression Recognition Network with Label Distribution TrainingCode1
A Survey on Facial Expression Recognition of Static and Dynamic EmotionsCode1
Affect Expression Behaviour Analysis in the Wild using Spatio-Channel Attention and Complementary Context InformationCode1
POSTER++: A simpler and stronger facial expression recognition networkCode1
A Facial Expression-Aware Multimodal Multi-task Learning Framework for Emotion Recognition in Multi-party ConversationsCode1
AU-Expression Knowledge Constrained Representation Learning for Facial Expression RecognitionCode1
Norface: Improving Facial Expression Analysis by Identity NormalizationCode1
EmoCLIP: A Vision-Language Method for Zero-Shot Video Facial Expression RecognitionCode1
Cluster-level pseudo-labelling for source-free cross-domain facial expression recognitionCode1
Adversarial Graph Representation Adaptation for Cross-Domain Facial Expression RecognitionCode1
Deep Facial Expression Recognition: A SurveyCode1
Distract Your Attention: Multi-head Cross Attention Network for Facial Expression RecognitionCode1
Facial Expression Recognition using Residual Masking NetworkCode1
EfficientFER: EfficientNetv2 Based Deep Learning Approach for Facial Expression RecognitionCode1
ExpLLM: Towards Chain of Thought for Facial Expression RecognitionCode1
Exploiting Emotional Dependencies with Graph Convolutional Networks for Facial Expression RecognitionCode1
Face2Exp: Combating Data Biases for Facial Expression RecognitionCode1
Facial Emotion Recognition: State of the Art Performance on FER2013Code1
A Dual-Branch Adaptive Distribution Fusion Framework for Real-World Facial Expression RecognitionCode1
Facial Expression Recognition with Deep LearningCode1
Intensity-Aware Loss for Dynamic Facial Expression Recognition in the WildCode1
QCS: Feature Refining from Quadruplet Cross Similarity for Facial Expression RecognitionCode1
CAGE: Circumplex Affect Guided Expression InferenceCode1
Analysis of Semi-Supervised Methods for Facial Expression RecognitionCode1
A Dual-Direction Attention Mixed Feature Network for Facial Expression RecognitionCode1
Graph Convolution with Low-rank Learnable Local FiltersCode1
Cross-Domain Facial Expression Recognition: A Unified Evaluation Benchmark and Adversarial Graph LearningCode1
Challenges in Representation Learning: A report on three machine learning contestsCode1
Automatic 4D Facial Expression Recognition via Collaborative Cross-domain Dynamic Image Network0
Automated Pain Detection from Facial Expressions using FACS: A Review0
A Generative Restricted Boltzmann Machine Based Method for High-Dimensional Motion Data Modeling0
AFNet-M: Adaptive Fusion Network with Masks for 2D+3D Facial Expression Recognition0
AU-Guided Unsupervised Domain Adaptive Facial Expression Recognition0
Adaptively Enhancing Facial Expression Crucial Regions via Local Non-Local Joint Network0
Deep Metric Structured Learning For Facial Expression Recognition0
AU-Aware Vision Transformers for Biased Facial Expression Recognition0
A Fine-Grained Facial Expression Database for End-to-End Multi-Pose Facial Expression Recognition0
Critically examining the Domain Generalizability of Facial Expression Recognition models0
A survey on Graph Deep Representation Learning for Facial Expression Recognition0
Adaptively Learning Facial Expression Representation via C-F Labels and Distillation0
Achieving 3D Attention via Triplet Squeeze and Excitation Block0
Deep Multi-Facial Patches Aggregation Network For Facial Expression Recognition0
A Survey of the Trends in Facial and Expression Recognition Databases and Methods0
AffectNet+: A Database for Enhancing Facial Expression Recognition with Soft-Labels0
A Sub-Layered Hierarchical Pyramidal Neural Architecture for Facial Expression Recognition0
A Study of Local Binary Pattern Method for Facial Expression Detection0
Affective Processes: stochastic modelling of temporal context for emotion and facial expression recognition0
Adaptively learning facial expression representation via cf labels and distillation.0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ResEmoteNetAccuracy (7 emotion)72.93Unverified
2NorfaceAccuracy (8 emotion)68.69Unverified
3EmoAffectNetAccuracy (7 emotion)66.49Unverified
4Emotion-GCNAccuracy (7 emotion)66.46Unverified
5FaceBehaviorNetAccuracy (7 emotion)65.4Unverified
6Ada-DFAccuracy (7 emotion)65.34Unverified
7EACAccuracy (7 emotion)65.32Unverified
8PAENetAccuracy (7 emotion)65.29Unverified
9DACLAccuracy (7 emotion)65.2Unverified
10DDAMFN++Accuracy (8 emotion)65.04Unverified
#ModelMetricClaimedVerifiedStatus
1ResEmoteNetOverall Accuracy94.76Unverified
2FMAEOverall Accuracy93.45Unverified
3QCSOverall Accuracy93.02Unverified
4NorfaceOverall Accuracy92.97Unverified
5S2DOverall Accuracy92.57Unverified
6BTNOverall Accuracy92.54Unverified
7GReFELOverall Accuracy92.47Unverified
8DDAMFN++Overall Accuracy92.34Unverified
9DCJTOverall Accuracy92.24Unverified
10POSTER++Overall Accuracy92.21Unverified
#ModelMetricClaimedVerifiedStatus
1EfficientFERAccuracy82.47Unverified
2FERNeXt-SDAFEAccuracy81.33Unverified
3ResEmoteNetAccuracy79.79Unverified
4Ensemble ResMaskingNet with 6 other CNNsAccuracy76.82Unverified
5Mini-ResEmoteNet (A)Accuracy76.33Unverified
6EmoNeXtAccuracy76.12Unverified
7Segmentation VGG-19Accuracy75.97Unverified
8Local Learning Deep+BOWAccuracy75.42Unverified
9LHC-NetAccuracy74.42Unverified
10Residual Masking NetworkAccuracy74.14Unverified
#ModelMetricClaimedVerifiedStatus
1PAtt-LiteAccuracy95.55Unverified
2GReFELAccuracy93.09Unverified
3QCSAccuracy91.85Unverified
4ResNet18 Dense ArchitectureAccuracy91.41Unverified
5DDAMFNAccuracy90.74Unverified
6KTNAccuracy90.49Unverified
7Vit-base + MAEAccuracy90.18Unverified
8FER-VTAccuracy90.04Unverified
9EACAccuracy89.64Unverified
10LResNet50E-IRAccuracy89.26Unverified
#ModelMetricClaimedVerifiedStatus
1ResNet50Accuracy(on validation set)65.5Unverified
2LResNet50E-IR (5 models with augmentation)Accuracy(on validation set)65.5Unverified
3EACAccuracy(on validation set)65.32Unverified
4LResNet50E-IR (1 model with augmentation)Accuracy(on validation set)63.7Unverified
5LResNet50E-IR (1 model)Accuracy(on validation set)61.1Unverified
6Multi-task EfficientNet-B0Accuracy(on validation set)59.27Unverified
7resnet18_noisyAccuracy(on validation set)55.17Unverified
8resnet18Accuracy(on validation set)51.18Unverified
#ModelMetricClaimedVerifiedStatus
1EmoNeXtAccuracy (8 emotion)100Unverified
2PAtt-LiteAccuracy (7 emotion)100Unverified
3ViT + SEAccuracy (7 emotion)99.8Unverified
4FANAccuracy (7 emotion)99.7Unverified
5Nonlinear eval on SL + SSL puzzling (B0)Accuracy (7 emotion)98.23Unverified
6DeepEmotionAccuracy (7 emotion)98Unverified
7FN2ENAccuracy (8 emotion)96.8Unverified
#ModelMetricClaimedVerifiedStatus
1KTNAccuracy(pretrained)90.49Unverified
2RAN (VGG-16)Accuracy(pretrained)89.16Unverified
3SENet TeacherAccuracy(pretrained)88.88Unverified
4Local Learning Deep + BOWAccuracy(pretrained)87.76Unverified
#ModelMetricClaimedVerifiedStatus
1TLAccuracy99.52Unverified
2GReFELAccuracy96.67Unverified
3ViTAccuracy94.83Unverified
4DeepEmotionAccuracy92.8Unverified
#ModelMetricClaimedVerifiedStatus
1Ada-DFAccuracy60.46Unverified
2RAN (VGG16+ResNet18)Accuracy56.4Unverified
3ViT + SEAccuracy54.29Unverified
4Island LossAccuracy52.52Unverified
#ModelMetricClaimedVerifiedStatus
1GReFELAccuracy72.48Unverified
2EmoAffectNet LSTMUAR52.9Unverified
#ModelMetricClaimedVerifiedStatus
1NorfaceICC0.74Unverified
2Ours (VGG-F)ICC0.72Unverified
#ModelMetricClaimedVerifiedStatus
1NorfaceICC0.67Unverified
2Ours (VGG-F)ICC0.6Unverified
#ModelMetricClaimedVerifiedStatus
1DeepEmotionAccuracy99.3Unverified
2GReFELAccuracy98.18Unverified
#ModelMetricClaimedVerifiedStatus
1DeXpressionAccuracy98.63Unverified
2Facial Motion Prior NetworkAccuracy82.74Unverified
#ModelMetricClaimedVerifiedStatus
1Dynamic MTLAccuracy (10-fold)89.6Unverified
2PPDNAccuracy (10-fold)84.59Unverified
#ModelMetricClaimedVerifiedStatus
1Covariance PoolingAccuracy87Unverified
2Multi Label OutputAccuracy79.26Unverified
#ModelMetricClaimedVerifiedStatus
1Covariance PoolingAccuracy58.14Unverified
2VGG-VD-16Accuracy54.82Unverified
#ModelMetricClaimedVerifiedStatus
1EfficientFaceAccuracy 85.87Unverified
#ModelMetricClaimedVerifiedStatus
1Sequential forward selectionAccuracy88.7Unverified
#ModelMetricClaimedVerifiedStatus
1EmoAffectNet LSTMUAR79Unverified
#ModelMetricClaimedVerifiedStatus
1ResEmoteNetAccuracy75.67Unverified
#ModelMetricClaimedVerifiedStatus
1ViT + SEAccuracy87.22Unverified
#ModelMetricClaimedVerifiedStatus
1EmoAffectNet LSTMUAR69.7Unverified
#ModelMetricClaimedVerifiedStatus
1EmoAffectNet LSTMUAR82.8Unverified