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 301–350 of 492 papers

TitleStatusHype
Temporal Stochastic Softmax for 3D CNNs: An Application in Facial Expression Recognition—0
TempT: Temporal consistency for Test-time adaptation—0
The Effect of Model Compression on Fairness in Facial Expression Recognition—0
The FaceChannel: A Fast & Furious Deep Neural Network for Facial Expression Recognition—0
The Indian Spontaneous Expression Database for Emotion Recognition—0
The Intelligent ICU Pilot Study: Using Artificial Intelligence Technology for Autonomous Patient Monitoring—0
THIN: THrowable Information Networks and Application for Facial Expression Recognition In The Wild—0
This Text Has the Scent of Starbucks: A Laplacian Structured Sparsity Model for Computational Branding Analytics—0
TimeConvNets: A Deep Time Windowed Convolution Neural Network Design for Real-time Video Facial Expression Recognition—0
TKFNet: Learning Texture Key Factor Driven Feature for Facial Expression Recognition—0
Toward Fair Facial Expression Recognition with Improved Distribution Alignment—0
Towards a General Deep Feature Extractor for Facial Expression Recognition—0
Towards All-around Knowledge Transferring: Learning From Task-irrelevant Labels—0
Towards Fair Affective Robotics: Continual Learning for Mitigating Bias in Facial Expression and Action Unit Recognition—0
Towards Realistic Landmark-Guided Facial Video Inpainting Based on GANs—0
Transfer Learning for Action Unit Recognition—0
TransFER: Learning Relation-aware Facial Expression Representations with Transformers—0
Tree-gated Deep Regressor Ensemble For Face Alignment In The Wild—0
Triplet Loss-less Center Loss Sampling Strategies in Facial Expression Recognition Scenarios—0
Uncertain Facial Expression Recognition via Multi-task Assisted Correction—0
Uncertain Label Correction via Auxiliary Action Unit Graphs for Facial Expression Recognition—0
Understanding and Mitigating Annotation Bias in Facial Expression Recognition—0
Using Deep Autoencoders for Facial Expression Recognition—0
Using Positive Matching Contrastive Loss with Facial Action Units to mitigate bias in Facial Expression Recognition—0
Using Self-Supervised Auxiliary Tasks to Improve Fine-Grained Facial Representation—0
Variable-state Latent Conditional Random Fields for Facial Expression Recognition and Action Unit Detection—0
Verifying Deep Learning-based Decisions for Facial Expression Recognition—0
VGAN-Based Image Representation Learning for Privacy-Preserving Facial Expression Recognition—0
Video-Based Facial Expression Recognition Using Local Directional Binary Pattern—0
Video-based Facial Expression Recognition using Graph Convolutional Networks—0
Video-Based Frame-Level Facial Analysis of Affective Behavior on Mobile Devices Using EfficientNets—0
Vision Transformer Equipped with Neural Resizer on Facial Expression Recognition Task—0
Visual Saliency Maps Can Apply to Facial Expression Recognition—0
What happens in Face during a facial expression? Using data mining techniques to analyze facial expression motion vectors—0
Impact of facial landmark localization on facial expression recognition—0
When Facial Expression Recognition Meets Few-Shot Learning: A Joint and Alternate Learning Framework—0
100,000 Podcasts: A Spoken English Document Corpus—0
Your "Attention" Deserves Attention: A Self-Diversified Multi-Channel Attention for Facial Action Analysis—0
2D+3D Facial Expression Recognition via Discriminative Dynamic Range Enhancement and Multi-Scale Learning—0
2D+3D facial expression recognition via embedded tensor manifold regularization—0
4DFAB: A Large Scale 4D Database for Facial Expression Analysis and Biometric Applications—0
Accurate Facial Parts Localization and Deep Learning for 3D Facial Expression Recognition—0
Achieving 3D Attention via Triplet Squeeze and Excitation Block—0
Adaptive Graph-Based Feature Normalization for Facial Expression Recognition—0
Adaptively learning facial expression representation via cf labels and distillation.—0
Adaptively Learning Facial Expression Representation via C-F Labels and Distillation—0
Adaptively Enhancing Facial Expression Crucial Regions via Local Non-Local Joint Network—0
A Deeper Look at Facial Expression Dataset Bias—0
Advanced local motion patterns for macro and micro facial expression recognition—0
AEGIS: A real-time multimodal augmented reality computer vision based system to assist facial expression recognition for individuals with autism spectrum disorder—0
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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