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

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Showing 401–450 of 492 papers

TitleStatusHype
Continuous Emotion Recognition via Deep Convolutional Autoencoder and Support Vector Regressor—0
Continuous Emotion Recognition with Spatiotemporal Convolutional Neural Networks—0
Contrastive Learning of View-Invariant Representations for Facial Expressions Recognition—0
Convolution with Logarithmic Filter Groups for Efficient Shallow CNN—0
Cross-Centroid Ripple Pattern for Facial Expression Recognition—0
Cross-database non-frontal facial expression recognition based on transductive deep transfer learning—0
Cross-Task Multi-Branch Vision Transformer for Facial Expression and Mask Wearing Classification—0
Deep and Shallow Covariance Feature Quantization for 3D Facial Expression Recognition—0
Deep Convolutional Neural Networks for Smile Recognition—0
Deep Convolution Network Based Emotion Analysis for Automatic Detection of Mild Cognitive Impairment in the Elderly—0
Deep Covariance Descriptors for Facial Expression Recognition—0
Deep Evolution for Facial Emotion Recognition—0
Deep generative-contrastive networks for facial expression recognition—0
Deep Learning For Smile Recognition—0
Deep Metric Structured Learning For Facial Expression Recognition—0
Deep Multi-Facial Patches Aggregation Network For Facial Expression Recognition—0
Deep Multi-task Learning for Facial Expression Recognition and Synthesis Based on Selective Feature Sharing—0
Deep Multi-Task Learning to Recognise Subtle Facial Expressions of Mental States—0
Deep Representation of Facial Geometric and Photometric Attributes for Automatic 3D Facial Expression Recognition—0
Deep Temporal Appearance-Geometry Network for Facial Expression Recognition—0
Detection and Localization of Facial Expression Manipulations—0
DFEW: A Large-Scale Database for Recognizing Dynamic Facial Expressions in the Wild—0
Disentanglement for Discriminative Visual Recognition—0
Disentangling Identity and Pose for Facial Expression Recognition—0
Distribution Matching for Heterogeneous Multi-Task Learning: a Large-scale Face Study—0
Distribution Matching for Multi-Task Learning of Classification Tasks: a Large-Scale Study on Faces & Beyond—0
Do Deep Neural Networks Forget Facial Action Units? -- Exploring the Effects of Transfer Learning in Health Related Facial Expression Recognition—0
Interpretable Image Emotion Recognition: A Domain Adaptation Approach Using Facial Expressions—0
Domain Adaptation for Facial Expression Classifier via Domain Discrimination and Gradient Reversal—0
Domain Generalisation for Apparent Emotional Facial Expression Recognition across Age-Groups—0
Domain-Incremental Continual Learning for Mitigating Bias in Facial Expression and Action Unit Recognition—0
DrFER: Learning Disentangled Representations for 3D Facial Expression Recognition—0
Dynamic Facial Expression Recognition under Partial Occlusion with Optical Flow Reconstruction—0
Dynamic Model of Facial Expression Recognition based on Eigen-face Approach—0
Dynamic Pose-Robust Facial Expression Recognition by Multi-View Pairwise Conditional Random Forests—0
Dynamic Resolution Guidance for Facial Expression Recognition—0
E2-Capsule Neural Networks for Facial Expression Recognition Using AU-Aware Attention—0
E-FCNN for tiny facial expression recognition—0
Emotic Masked Autoencoder with Attention Fusion for Facial Expression Recognition—0
Emotion Recognition in Speech using Cross-Modal Transfer in the Wild—0
Evaluating Facial Expression Recognition Datasets for Deep Learning: A Benchmark Study with Novel Similarity Metrics—0
Evaluation of the Spatio-Temporal features and GAN for Micro-expression Recognition System—0
EXPERTNet Exigent Features Preservative Network for Facial Expression Recognition—0
Exploring Emotion Features and Fusion Strategies for Audio-Video Emotion Recognition—0
Exploring Facial Expression Recognition through Semi-Supervised Pretraining and Temporal Modeling—0
Exploring Large-scale Unlabeled Faces to Enhance Facial Expression Recognition—0
Expression, Affect, Action Unit Recognition: Aff-Wild2, Multi-Task Learning and ArcFace—0
Expression Analysis Based on Face Regions in Read-world Conditions—0
Expression Empowered ResiDen Network for Facial Action Unit Detection—0
Expression Recognition Analysis in the Wild—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