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
Representation Learning and Identity Adversarial Training for Facial Behavior UnderstandingCode2
MARLIN: Masked Autoencoder for facial video Representation LearnINgCode2
Enhancing Zero-Shot Facial Expression Recognition by LLM Knowledge TransferCode2
FER-YOLO-Mamba: Facial Expression Detection and Classification Based on Selective State SpaceCode2
Frame-level Prediction of Facial Expressions, Valence, Arousal and Action Units for Mobile DevicesCode2
Latent-OFER: Detect, Mask, and Reconstruct with Latent Vectors for Occluded Facial Expression RecognitionCode1
Increasingly Packing Multiple Facial-Informatics Modules in A Unified Deep-Learning Model via Lifelong LearningCode1
Learn From All: Erasing Attention Consistency for Noisy Label Facial Expression RecognitionCode1
Facial Expression Recognition in the Wild via Deep Attentive Center LossCode1
From Static to Dynamic: Adapting Landmark-Aware Image Models for Facial Expression Recognition in VideosCode1
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
Landmark Guidance Independent Spatio-channel Attention and Complementary Context Information based Facial Expression RecognitionCode1
Facial Emotion Recognition: State of the Art Performance on FER2013Code1
Facial Expression Recognition using Residual Masking NetworkCode1
Exploiting Emotional Dependencies with Graph Convolutional Networks for Facial Expression RecognitionCode1
ExpLLM: Towards Chain of Thought for Facial Expression RecognitionCode1
Complete Face Recovery GAN: Unsupervised Joint Face Rotation and De-Occlusion From a Single-View ImageCode1
CAGE: Circumplex Affect Guided Expression InferenceCode1
Compacting, Picking and Growing for Unforgetting Continual LearningCode1
Facial Emotion Recognition Using Transfer Learning in the Deep CNNCode1
Fer2013 Recognition - ResNet18 With TricksCode1
Explore Image Deblurring via Blur Kernel SpaceCode1
Generalizable Facial Expression RecognitionCode1
Graph Convolution with Low-rank Learnable Local FiltersCode1
AU-Expression Knowledge Constrained Representation Learning for Facial Expression RecognitionCode1
Deep Facial Expression Recognition: A SurveyCode1
Distract Your Attention: Multi-head Cross Attention Network for Facial Expression RecognitionCode1
Intensity-Aware Loss for Dynamic Facial Expression Recognition in the WildCode1
EmoCLIP: A Vision-Language Method for Zero-Shot Video Facial Expression RecognitionCode1
Efficient Facial Feature Learning with Wide Ensemble-based Convolutional Neural NetworksCode1
Consensual Collaborative Training And Knowledge Distillation Based Facial Expression Recognition Under Noisy AnnotationsCode1
A Survey on Facial Expression Recognition of Static and Dynamic EmotionsCode1
A Dual-Branch Adaptive Distribution Fusion Framework for Real-World Facial Expression RecognitionCode1
BReG-NeXt: Facial Affect Computing Using Adaptive Residual Networks With Bounded GradientCode1
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
Challenges in Representation Learning: A report on three machine learning contestsCode1
A Facial Expression-Aware Multimodal Multi-task Learning Framework for Emotion Recognition in Multi-party ConversationsCode1
Cluster-level pseudo-labelling for source-free cross-domain facial expression recognitionCode1
Affect Expression Behaviour Analysis in the Wild using Spatio-Channel Attention and Complementary Context InformationCode1
DeepFaceFlow: In-the-wild Dense 3D Facial Motion EstimationCode1
EfficientFER: EfficientNetv2 Based Deep Learning Approach for Facial Expression RecognitionCode1
Face2Exp: Combating Data Biases for Facial Expression RecognitionCode1
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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