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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 151–200 of 492 papers

TitleStatusHype
Facial Expression Recognition at the Edge: CPU vs GPU vs VPU vs TPU—0
Continual Facial Expression Recognition: A Benchmark—0
Class adaptive threshold and negative class guided noisy annotation robust Facial Expression Recognition—0
ARBEx: Attentive Feature Extraction with Reliability Balancing for Robust Facial Expression LearningCode0
Spiking-Fer: Spiking Neural Network for Facial Expression Recognition With Event Cameras—0
Masked Student Dataset of ExpressionsCode0
Multi-Domain Norm-referenced Encoding Enables Data Efficient Transfer Learning of Facial Expression Recognition—0
FER-former: Multi-modal Transformer for Facial Expression Recognition—0
TempT: Temporal consistency for Test-time adaptation—0
Multi Modal Facial Expression Recognition with Transformer-Based Fusion Networks and Dynamic Sampling—0
Exploring Large-scale Unlabeled Faces to Enhance Facial Expression Recognition—0
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
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
More comprehensive facial inversion for more effective expression recognitionCode0
AU-Aware Vision Transformers for Biased Facial Expression Recognition—0
Face Emotion Recognization Using Dataset Augmentation Based on Neural Network—0
Learning Diversified Feature Representations for Facial Expression Recognition in the WildCode0
Gender Stereotyping Impact in Facial Expression Recognition—0
Self-Paced Label Distribution Learning for In-The-Wild Facial Expression Recognition—0
Rethinking the Learning Paradigm for Facial Expression Recognition—0
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
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
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
AU-Supervised Convolutional Vision Transformers for Synthetic Facial Expression RecognitionCode0
Hand-Assisted Expression Recognition Method from Synthetic Images at the Fourth ABAW Challenge—0
Learning from Synthetic Data: Facial Expression Classification based on Ensemble of Multi-task Networks—0
Classifying emotions and engagement in online learning based on a single facial expression recognition neural network—0
Identifying the Context Shift between Test Benchmarks and Production Data—0
Hybrid Facial Expression Recognition (FER2013) Model for Real-Time Emotion Classification and Prediction—0
Video-Based Frame-Level Facial Analysis of Affective Behavior on Mobile Devices Using EfficientNets—0
NR-DFERNet: Noise-Robust Network for Dynamic Facial Expression Recognition—0
Fine-tuning of Convolutional Neural Networks for the Recognition of Facial Expressions in Sign Language Video Samples—0
Boosting Facial Expression Recognition by A Semi-Supervised Progressive Teacher—0
AFNet-M: Adaptive Fusion Network with Masks for 2D+3D Facial Expression Recognition—0
Assessing Demographic Bias Transfer from Dataset to Model: A Case Study in Facial Expression RecognitionCode0
A Peek at Peak Emotion Recognition—0
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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