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 101–150 of 492 papers

TitleStatusHype
Masked Student Dataset of ExpressionsCode0
Multi-Domain Norm-referenced Encoding Enables Data Efficient Transfer Learning of Facial Expression Recognition—0
A novel facial emotion recognition model using segmentation VGG-19 architectureCode1
FER-former: Multi-modal Transformer for Facial Expression Recognition—0
TempT: Temporal consistency for Test-time adaptation—0
Exploring Large-scale Unlabeled Faces to Enhance Facial Expression Recognition—0
Multi Modal Facial Expression Recognition with Transformer-Based Fusion Networks and Dynamic Sampling—0
Quaternion Orthogonal Transformer for Facial Expression Recognition in the WildCode1
SimFLE: Simple Facial Landmark Encoding for Self-Supervised Facial Expression Recognition in the WildCode0
Using Positive Matching Contrastive Loss with Facial Action Units to mitigate bias in Facial Expression Recognition—0
CLIPER: A Unified Vision-Language Framework for In-the-Wild Facial Expression Recognition—0
Triplet Loss-less Center Loss Sampling Strategies in Facial Expression Recognition Scenarios—0
POSTER++: A simpler and stronger facial expression recognition networkCode1
STPrivacy: Spatio-Temporal Privacy-Preserving Action Recognition—0
Multi-Label Compound Expression Recognition: C-EXPR Database & Network—0
Combating Uncertainty and Class Imbalance in Facial Expression Recognition—0
Uncertain Facial Expression Recognition via Multi-task Assisted Correction—0
Vision Transformer with Attentive Pooling for Robust Facial Expression RecognitionCode1
More comprehensive facial inversion for more effective expression recognitionCode0
Pose-disentangled Contrastive Learning for Self-supervised Facial RepresentationCode1
AU-Aware Vision Transformers for Biased Facial Expression Recognition—0
MARLIN: Masked Autoencoder for facial video Representation LearnINgCode2
Face Emotion Recognization Using Dataset Augmentation Based on Neural Network—0
Learning Diversified Feature Representations for Facial Expression Recognition in the WildCode0
Cluster-level pseudo-labelling for source-free cross-domain facial expression recognitionCode1
Gender Stereotyping Impact in Facial Expression Recognition—0
Self-Paced Label Distribution Learning for In-The-Wild Facial Expression Recognition—0
Revisiting Self-Supervised Contrastive Learning for Facial Expression RecognitionCode1
In Search of a Robust Facial Expressions Recognition Model: A Large-Scale Visual Cross-Corpus StudyCode1
Rethinking the Learning Paradigm for Facial Expression Recognition—0
Optimal Transport-based Identity Matching for Identity-invariant Facial Expression RecognitionCode1
Uncertainty-aware Label Distribution Learning for Facial Expression RecognitionCode1
On Developing Facial Stress Analysis and Expression Recognition Platform—0
FaceTopoNet: Facial Expression Recognition using Face Topology Learning—0
Dynamic Adaptive Threshold based Learning for Noisy Annotations Robust Facial Expression RecognitionCode0
Intensity-Aware Loss for Dynamic Facial Expression Recognition in the WildCode1
Disentangling Identity and Pose for Facial Expression Recognition—0
Facial Expression Recognition and Image Description Generation in Vietnamese—0
CIAO! A Contrastive Adaptation Mechanism for Non-Universal Facial Expression Recognition—0
MAFW: A Large-scale, Multi-modal, Compound Affective Database for Dynamic Facial Expression Recognition in the Wild—0
Analysis of Semi-Supervised Methods for Facial Expression RecognitionCode1
Mid-level Representation Enhancement and Graph Embedded Uncertainty Suppressing for Facial Expression RecognitionCode0
Emotion Separation and Recognition from a Facial Expression by Generating the Poker Face with Vision Transformers—0
Adaptive Graph-Based Feature Normalization for Facial Expression Recognition—0
Affective Behavior Analysis using Action Unit Relation Graph and Multi-task Cross Attention—0
Learn From All: Erasing Attention Consistency for Noisy Label Facial Expression RecognitionCode1
AU-Supervised Convolutional Vision Transformers for Synthetic Facial Expression RecognitionCode0
Learning from Synthetic Data: Facial Expression Classification based on Ensemble of Multi-task Networks—0
Hand-Assisted Expression Recognition Method from Synthetic Images at the Fourth ABAW Challenge—0
Learn-to-Decompose: Cascaded Decomposition Network for Cross-Domain Few-Shot Facial Expression RecognitionCode1
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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