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 151200 of 492 papers

TitleStatusHype
Classifying emotions and engagement in online learning based on a single facial expression recognition neural networkCode0
Identifying the Context Shift between Test Benchmarks and Production Data0
Hybrid Facial Expression Recognition (FER2013) Model for Real-Time Emotion Classification and Prediction0
Video-Based Frame-Level Facial Analysis of Affective Behavior on Mobile Devices Using EfficientNetsCode0
NR-DFERNet: Noise-Robust Network for Dynamic Facial Expression Recognition0
Fine-tuning of Convolutional Neural Networks for the Recognition of Facial Expressions in Sign Language Video Samples0
Boosting Facial Expression Recognition by A Semi-Supervised Progressive Teacher0
AFNet-M: Adaptive Fusion Network with Masks for 2D+3D Facial Expression Recognition0
Assessing Demographic Bias Transfer from Dataset to Model: A Case Study in Facial Expression RecognitionCode0
A Peek at Peak Emotion Recognition0
Spatio-Temporal Transformer for Dynamic Facial Expression Recognition in the Wild0
MixAugment & Mixup: Augmentation Methods for Facial Expression Recognition0
Uncertain Label Correction via Auxiliary Action Unit Graphs for Facial Expression Recognition0
POSTER: A Pyramid Cross-Fusion Transformer Network for Facial Expression RecognitionCode1
Vision Transformer Equipped with Neural Resizer on Facial Expression Recognition Task0
Adaptively Enhancing Facial Expression Crucial Regions via Local Non-Local Joint Network0
Clip-aware expressive feature learning for video-based facial expression recognitionCode0
Frame-level Prediction of Facial Expressions, Valence, Arousal and Action Units for Mobile DevicesCode2
Facial Expression Recognition with Swin Transformer0
Privileged Attribution Constrained Deep Networks for Facial Expression Recognition0
Facial Expression Recognition based on Multi-head Cross Attention Network0
Coarse-to-Fine Cascaded Networks with Smooth Predicting for Video Facial Expression RecognitionCode0
Your "Attention" Deserves Attention: A Self-Diversified Multi-Channel Attention for Facial Action Analysis0
Towards Semi-Supervised Deep Facial Expression Recognition with An Adaptive Confidence MarginCode1
Assessing Gender Bias in Predictive Algorithms using eXplainable AI0
FERV39k: A Large-Scale Multi-Scene Dataset for Facial Expression Recognition in Videos0
Robust facial expression recognition with global‑local joint representation learning0
Ad-Corre: Adaptive Correlation-Based Loss for Facial Expression Recognition in the WildCode0
Real-Time Facial Expression Recognition using Facial Landmarks and Neural Networks0
2D+3D facial expression recognition via embedded tensor manifold regularization0
Explore the Expression: Facial Expression Generation using Auxiliary Classifier Generative Adversarial NetworkCode0
Towards a General Deep Feature Extractor for Facial Expression Recognition0
When Facial Expression Recognition Meets Few-Shot Learning: A Joint and Alternate Learning Framework0
Cross-Centroid Ripple Pattern for Facial Expression Recognition0
Complete Face Recovery GAN: Unsupervised Joint Face Rotation and De-Occlusion From a Single-View ImageCode1
The Effect of Model Compression on Fairness in Facial Expression Recognition0
Multi-Dimensional, Nuanced and Subjective - Measuring the Perception of Facial Expressions0
Face2Exp: Combating Data Biases for Facial Expression RecognitionCode1
Fer2013 Recognition - ResNet18 With TricksCode1
Face Trees for Expression Recognition0
Detect Faces Efficiently: A Survey and EvaluationsCode3
Relative Uncertainty Learning for Facial Expression RecognitionCode1
Teacher-Student Training and Triplet Loss to Reduce the Effect of Drastic Face Occlusion0
Local Multi-Head Channel Self-Attention for Facial Expression RecognitionCode1
Deep Convolution Network Based Emotion Analysis for Automatic Detection of Mild Cognitive Impairment in the Elderly0
Facial Emotion Recognition: A multi-task approach using deep learningCode0
Domain Generalisation for Apparent Emotional Facial Expression Recognition across Age-Groups0
Comparing Facial Expression Recognition in Humans and Machines: Using CAM, GradCAM, and Extremal Perturbation0
Label quality in AffectNet: results of crowd-based re-annotation0
Research on facial expression recognition based on Multimodal data fusion and neural network0
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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
1PAtt-LiteAccuracy (7 emotion)100Unverified
2EmoNeXtAccuracy (8 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