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 301325 of 492 papers

TitleStatusHype
Temporal Stochastic Softmax for 3D CNNs: An Application in Facial Expression Recognition0
TempT: Temporal consistency for Test-time adaptation0
The Effect of Model Compression on Fairness in Facial Expression Recognition0
The FaceChannel: A Fast & Furious Deep Neural Network for Facial Expression Recognition0
The Indian Spontaneous Expression Database for Emotion Recognition0
The Intelligent ICU Pilot Study: Using Artificial Intelligence Technology for Autonomous Patient Monitoring0
THIN: THrowable Information Networks and Application for Facial Expression Recognition In The Wild0
This Text Has the Scent of Starbucks: A Laplacian Structured Sparsity Model for Computational Branding Analytics0
TimeConvNets: A Deep Time Windowed Convolution Neural Network Design for Real-time Video Facial Expression Recognition0
TKFNet: Learning Texture Key Factor Driven Feature for Facial Expression Recognition0
Toward Fair Facial Expression Recognition with Improved Distribution Alignment0
Towards a General Deep Feature Extractor for Facial Expression Recognition0
Towards All-around Knowledge Transferring: Learning From Task-irrelevant Labels0
Towards Fair Affective Robotics: Continual Learning for Mitigating Bias in Facial Expression and Action Unit Recognition0
Towards Realistic Landmark-Guided Facial Video Inpainting Based on GANs0
Transfer Learning for Action Unit Recognition0
TransFER: Learning Relation-aware Facial Expression Representations with Transformers0
Tree-gated Deep Regressor Ensemble For Face Alignment In The Wild0
Triplet Loss-less Center Loss Sampling Strategies in Facial Expression Recognition Scenarios0
Uncertain Facial Expression Recognition via Multi-task Assisted Correction0
Uncertain Label Correction via Auxiliary Action Unit Graphs for Facial Expression Recognition0
Understanding and Mitigating Annotation Bias in Facial Expression Recognition0
Using Deep Autoencoders for Facial Expression Recognition0
Using Positive Matching Contrastive Loss with Facial Action Units to mitigate bias in Facial Expression Recognition0
Using Self-Supervised Auxiliary Tasks to Improve Fine-Grained Facial Representation0
Show:102550
← PrevPage 13 of 20Next →

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ResEmoteNetAccuracy (7 emotion)72.93Unverified
2NorfaceAccuracy (8 emotion)68.69Unverified
3EmoAffectNetAccuracy (7 emotion)66.49Unverified
4Emotion-GCNAccuracy (7 emotion)66.46Unverified
5FaceBehaviorNetAccuracy (7 emotion)65.4Unverified
6Ada-DFAccuracy (7 emotion)65.34Unverified
7EACAccuracy (7 emotion)65.32Unverified
8PAENetAccuracy (7 emotion)65.29Unverified
9DACLAccuracy (7 emotion)65.2Unverified
10DDAMFN++Accuracy (8 emotion)65.04Unverified
#ModelMetricClaimedVerifiedStatus
1ResEmoteNetOverall Accuracy94.76Unverified
2FMAEOverall Accuracy93.45Unverified
3QCSOverall Accuracy93.02Unverified
4NorfaceOverall Accuracy92.97Unverified
5S2DOverall Accuracy92.57Unverified
6BTNOverall Accuracy92.54Unverified
7GReFELOverall Accuracy92.47Unverified
8DDAMFN++Overall Accuracy92.34Unverified
9DCJTOverall Accuracy92.24Unverified
10POSTER++Overall Accuracy92.21Unverified
#ModelMetricClaimedVerifiedStatus
1EfficientFERAccuracy82.47Unverified
2FERNeXt-SDAFEAccuracy81.33Unverified
3ResEmoteNetAccuracy79.79Unverified
4Ensemble ResMaskingNet with 6 other CNNsAccuracy76.82Unverified
5Mini-ResEmoteNet (A)Accuracy76.33Unverified
6EmoNeXtAccuracy76.12Unverified
7Segmentation VGG-19Accuracy75.97Unverified
8Local Learning Deep+BOWAccuracy75.42Unverified
9LHC-NetAccuracy74.42Unverified
10Residual Masking NetworkAccuracy74.14Unverified
#ModelMetricClaimedVerifiedStatus
1PAtt-LiteAccuracy95.55Unverified
2GReFELAccuracy93.09Unverified
3QCSAccuracy91.85Unverified
4ResNet18 Dense ArchitectureAccuracy91.41Unverified
5DDAMFNAccuracy90.74Unverified
6KTNAccuracy90.49Unverified
7Vit-base + MAEAccuracy90.18Unverified
8FER-VTAccuracy90.04Unverified
9EACAccuracy89.64Unverified
10LResNet50E-IRAccuracy89.26Unverified
#ModelMetricClaimedVerifiedStatus
1ResNet50Accuracy(on validation set)65.5Unverified
2LResNet50E-IR (5 models with augmentation)Accuracy(on validation set)65.5Unverified
3EACAccuracy(on validation set)65.32Unverified
4LResNet50E-IR (1 model with augmentation)Accuracy(on validation set)63.7Unverified
5LResNet50E-IR (1 model)Accuracy(on validation set)61.1Unverified
6Multi-task EfficientNet-B0Accuracy(on validation set)59.27Unverified
7resnet18_noisyAccuracy(on validation set)55.17Unverified
8resnet18Accuracy(on validation set)51.18Unverified
#ModelMetricClaimedVerifiedStatus
1EmoNeXtAccuracy (8 emotion)100Unverified
2PAtt-LiteAccuracy (7 emotion)100Unverified
3ViT + SEAccuracy (7 emotion)99.8Unverified
4FANAccuracy (7 emotion)99.7Unverified
5Nonlinear eval on SL + SSL puzzling (B0)Accuracy (7 emotion)98.23Unverified
6DeepEmotionAccuracy (7 emotion)98Unverified
7FN2ENAccuracy (8 emotion)96.8Unverified
#ModelMetricClaimedVerifiedStatus
1KTNAccuracy(pretrained)90.49Unverified
2RAN (VGG-16)Accuracy(pretrained)89.16Unverified
3SENet TeacherAccuracy(pretrained)88.88Unverified
4Local Learning Deep + BOWAccuracy(pretrained)87.76Unverified
#ModelMetricClaimedVerifiedStatus
1TLAccuracy99.52Unverified
2GReFELAccuracy96.67Unverified
3ViTAccuracy94.83Unverified
4DeepEmotionAccuracy92.8Unverified
#ModelMetricClaimedVerifiedStatus
1Ada-DFAccuracy60.46Unverified
2RAN (VGG16+ResNet18)Accuracy56.4Unverified
3ViT + SEAccuracy54.29Unverified
4Island LossAccuracy52.52Unverified
#ModelMetricClaimedVerifiedStatus
1GReFELAccuracy72.48Unverified
2EmoAffectNet LSTMUAR52.9Unverified
#ModelMetricClaimedVerifiedStatus
1NorfaceICC0.74Unverified
2Ours (VGG-F)ICC0.72Unverified
#ModelMetricClaimedVerifiedStatus
1NorfaceICC0.67Unverified
2Ours (VGG-F)ICC0.6Unverified
#ModelMetricClaimedVerifiedStatus
1DeepEmotionAccuracy99.3Unverified
2GReFELAccuracy98.18Unverified
#ModelMetricClaimedVerifiedStatus
1DeXpressionAccuracy98.63Unverified
2Facial Motion Prior NetworkAccuracy82.74Unverified
#ModelMetricClaimedVerifiedStatus
1Dynamic MTLAccuracy (10-fold)89.6Unverified
2PPDNAccuracy (10-fold)84.59Unverified
#ModelMetricClaimedVerifiedStatus
1Covariance PoolingAccuracy87Unverified
2Multi Label OutputAccuracy79.26Unverified
#ModelMetricClaimedVerifiedStatus
1Covariance PoolingAccuracy58.14Unverified
2VGG-VD-16Accuracy54.82Unverified
#ModelMetricClaimedVerifiedStatus
1EfficientFaceAccuracy 85.87Unverified
#ModelMetricClaimedVerifiedStatus
1Sequential forward selectionAccuracy88.7Unverified
#ModelMetricClaimedVerifiedStatus
1EmoAffectNet LSTMUAR79Unverified
#ModelMetricClaimedVerifiedStatus
1ResEmoteNetAccuracy75.67Unverified
#ModelMetricClaimedVerifiedStatus
1ViT + SEAccuracy87.22Unverified
#ModelMetricClaimedVerifiedStatus
1EmoAffectNet LSTMUAR69.7Unverified
#ModelMetricClaimedVerifiedStatus
1EmoAffectNet LSTMUAR82.8Unverified