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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 101–150 of 492 papers

TitleStatusHype
Bridging the Gaps: Utilizing Unlabeled Face Recognition Datasets to Boost Semi-Supervised Facial Expression RecognitionCode0
GReFEL: Geometry-Aware Reliable Facial Expression Learning under Bias and Imbalanced Data Distribution—0
Regularized Xception for facial expression recognition with extra training data and step decay learning rateCode0
EmojiHeroVR: A Study on Facial Expression Recognition under Partial Occlusion from Head-Mounted DisplaysCode0
Knowledge-Enhanced Facial Expression Recognition with Emotional-to-Neutral Transformation—0
ResEmoteNet: Bridging Accuracy and Loss Reduction in Facial Emotion RecognitionCode0
From Bias to Balance: Detecting Facial Expression Recognition Biases in Large Multimodal Foundation Models—0
EMO-LLaMA: Enhancing Facial Emotion Understanding with Instruction TuningCode0
LLDif: Diffusion Models for Low-light Emotion Recognition—0
A Lightweight Model Enhancing Facial Expression Recognition with Spatial Bias and Cosine-Harmony LossCode0
Real Time Emotion Analysis Using Deep Learning for Education, Entertainment, and Beyond—0
Batch Transformer: Look for Attention in Batch—0
GCF: Graph Convolutional Networks for Facial Expression RecognitionCode0
Less can be more: representational vs. stereotypical gender bias in facial expression recognition—0
Multi-threshold Deep Metric Learning for Facial Expression Recognition—0
FaceMixup: Enhancing Facial Expression Recognition through Mixed Face Regularization—0
LEAF: Unveiling Two Sides of the Same Coin in Semi-supervised Facial Expression RecognitionCode0
Cross-Task Multi-Branch Vision Transformer for Facial Expression and Mask Wearing Classification—0
eMotion-GAN: A Motion-based GAN for Photorealistic and Facial Expression Preserving Frontal View SynthesisCode0
Dynamic Resolution Guidance for Facial Expression Recognition—0
Music Recommendation Based on Facial Emotion Recognition—0
Joint Training on Multiple Datasets With Inconsistent Labeling Criteria for Facial Expression RecognitionCode0
A Lightweight Attention-based Deep Network via Multi-Scale Feature Fusion for Multi-View Facial Expression RecognitionCode0
Emotic Masked Autoencoder with Attention Fusion for Facial Expression Recognition—0
Exploring Facial Expression Recognition through Semi-Supervised Pretraining and Temporal Modeling—0
DrFER: Learning Disentangled Representations for 3D Facial Expression Recognition—0
Towards Realistic Landmark-Guided Facial Video Inpainting Based on GANs—0
LRDif: Diffusion Models for Under-Display Camera Emotion Recognition—0
Realtime Facial Expression Recognition: Neuromorphic Hardware vs. Edge AI Accelerators—0
Expression-aware video inpainting for HMD removal in XR applicationsCode0
Open-Set Facial Expression Recognition—0
MIMIC: Mask Image Pre-training with Mix Contrastive Fine-tuning for Facial Expression Recognition—0
Distribution Matching for Multi-Task Learning of Classification Tasks: a Large-Scale Study on Faces & Beyond—0
FER-C: Benchmarking Out-of-Distribution Soft Calibration for Facial Expression Recognition—0
Multi-Energy Guided Image Translation with Stochastic Differential Equations for Near-Infrared Facial Expression Recognition—0
Hypergraph-Guided Disentangled Spectrum Transformer Networks for Near-Infrared Facial Expression Recognition—0
Subject-Based Domain Adaptation for Facial Expression RecognitionCode0
Contrastive Learning of View-Invariant Representations for Facial Expressions Recognition—0
Benchmarking Deep Facial Expression Recognition: An Extensive Protocol with Balanced Dataset in the Wild—0
Multi Loss-based Feature Fusion and Top Two Voting Ensemble Decision Strategy for Facial Expression Recognition in the Wild—0
Leave No Stone Unturned: Mine Extra Knowledge for Imbalanced Facial Expression Recognition—0
FerNeXt: Facial Expression Recognition Using ConvNeXt with Channel AttentionCode0
ASM: Adaptive Sample Mining for In-The-Wild Facial Expression RecognitionCode0
InFER: A Multi-Ethnic Indian Facial Expression Recognition Dataset—0
A Generative Framework for Self-Supervised Facial Representation LearningCode0
LA-Net: Landmark-Aware Learning for Reliable Facial Expression Recognition under Label Noise—0
Active Learning with Contrastive Pre-training for Facial Expression RecognitionCode0
Toward Fair Facial Expression Recognition with Improved Distribution Alignment—0
Exploring the Boundaries of Semi-Supervised Facial Expression Recognition using In-Distribution, Out-of-Distribution, and Unconstrained DataCode0
ReSup: Reliable Label Noise Suppression for Facial Expression RecognitionCode0
Show:102550
← PrevPage 3 of 10Next →
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
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