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 1–50 of 492 papers

TitleStatusHype
Detect Faces Efficiently: A Survey and EvaluationsCode3
MARLIN: Masked Autoencoder for facial video Representation LearnINgCode2
Representation Learning and Identity Adversarial Training for Facial Behavior UnderstandingCode2
Enhancing Zero-Shot Facial Expression Recognition by LLM Knowledge TransferCode2
Frame-level Prediction of Facial Expressions, Valence, Arousal and Action Units for Mobile DevicesCode2
FER-YOLO-Mamba: Facial Expression Detection and Classification Based on Selective State SpaceCode2
Latent-OFER: Detect, Mask, and Reconstruct with Latent Vectors for Occluded Facial Expression RecognitionCode1
Generalizable Facial Expression RecognitionCode1
Learn From All: Erasing Attention Consistency for Noisy Label Facial Expression RecognitionCode1
Face2Exp: Combating Data Biases for Facial Expression RecognitionCode1
Fer2013 Recognition - ResNet18 With TricksCode1
From Static to Dynamic: Adapting Landmark-Aware Image Models for Facial Expression Recognition in VideosCode1
Increasingly Packing Multiple Facial-Informatics Modules in A Unified Deep-Learning Model via Lifelong LearningCode1
Landmark Guidance Independent Spatio-channel Attention and Complementary Context Information based Facial Expression RecognitionCode1
EmoCLIP: A Vision-Language Method for Zero-Shot Video Facial Expression RecognitionCode1
Facial Emotion Recognition: State of the Art Performance on FER2013Code1
Deep Facial Expression Recognition: A SurveyCode1
A Dual-Branch Adaptive Distribution Fusion Framework for Real-World Facial Expression RecognitionCode1
Efficient Facial Feature Learning with Wide Ensemble-based Convolutional Neural NetworksCode1
EfficientFER: EfficientNetv2 Based Deep Learning Approach for Facial Expression RecognitionCode1
ExpLLM: Towards Chain of Thought for Facial Expression RecognitionCode1
Explore Image Deblurring via Blur Kernel SpaceCode1
Facial Expression Recognition in the Wild via Deep Attentive Center LossCode1
Facial Expression Recognition with Deep LearningCode1
DeepFaceFlow: In-the-wild Dense 3D Facial Motion EstimationCode1
Challenges in Representation Learning: A report on three machine learning contestsCode1
Graph Convolution with Low-rank Learnable Local FiltersCode1
Guided Interpretable Facial Expression Recognition via Spatial Action Unit CuesCode1
In Search of a Robust Facial Expressions Recognition Model: A Large-Scale Visual Cross-Corpus StudyCode1
Intensity-Aware Loss for Dynamic Facial Expression Recognition in the WildCode1
Compacting, Picking and Growing for Unforgetting Continual LearningCode1
BReG-NeXt: Facial Affect Computing Using Adaptive Residual Networks With Bounded GradientCode1
A Survey on Facial Expression Recognition of Static and Dynamic EmotionsCode1
Cluster-level pseudo-labelling for source-free cross-domain facial expression recognitionCode1
CAGE: Circumplex Affect Guided Expression InferenceCode1
Consensual Collaborative Training And Knowledge Distillation Based Facial Expression Recognition Under Noisy AnnotationsCode1
Analysis of Semi-Supervised Methods for Facial Expression RecognitionCode1
A Dual-Direction Attention Mixed Feature Network for Facial Expression RecognitionCode1
Cross-Domain Facial Expression Recognition: A Unified Evaluation Benchmark and Adversarial Graph LearningCode1
A novel deep learning approach for facial emotion recognition: application to detecting emotional responses in elderly individuals with Alzheimer’s diseaseCode1
A novel facial emotion recognition model using segmentation VGG-19 architectureCode1
Adversarial Graph Representation Adaptation for Cross-Domain Facial Expression RecognitionCode1
Ada-DF: An Adaptive Label Distribution Fusion Network For Facial Expression RecognitionCode1
Complete Face Recovery GAN: Unsupervised Joint Face Rotation and De-Occlusion From a Single-View ImageCode1
A Facial Expression-Aware Multimodal Multi-task Learning Framework for Emotion Recognition in Multi-party ConversationsCode1
Exploiting Emotional Dependencies with Graph Convolutional Networks for Facial Expression RecognitionCode1
Affect Expression Behaviour Analysis in the Wild using Spatio-Channel Attention and Complementary Context InformationCode1
AU-Expression Knowledge Constrained Representation Learning for Facial Expression RecognitionCode1
Facial Expression Recognition using Residual Masking NetworkCode1
Distract Your Attention: Multi-head Cross Attention Network for Facial Expression RecognitionCode1
Show:102550
← PrevPage 1 of 10Next →

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
1PAtt-LiteAccuracy (7 emotion)100—Unverified
2EmoNeXtAccuracy (8 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