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 351–400 of 492 papers

TitleStatusHype
Learning Expressionlets on Spatio-Temporal Manifold for Dynamic Facial Expression Recognition—0
Learning Expressionlets via Universal Manifold Model for Dynamic Facial Expression Recognition—0
Learning from Synthetic Data: Facial Expression Classification based on Ensemble of Multi-task Networks—0
Learning Grimaces by Watching TV—0
Learning Pain from Action Unit Combinations: A Weakly Supervised Approach via Multiple Instance Learning—0
Learning to Augment Expressions for Few-shot Fine-grained Facial Expression Recognition—0
Learning Vision Transformer with Squeeze and Excitation for Facial Expression Recognition—0
Leave No Stone Unturned: Mine Extra Knowledge for Imbalanced Facial Expression Recognition—0
Less can be more: representational vs. stereotypical gender bias in facial expression recognition—0
Leveraging Recent Advances in Deep Learning for Audio-Visual Emotion Recognition—0
LLDif: Diffusion Models for Low-light Emotion Recognition—0
Local Learning with Deep and Handcrafted Features for Facial Expression Recognition—0
Local Shape Spectrum Analysis for 3D Facial Expression Recognition—0
Logistic Boosting Regression for Label Distribution Learning—0
Lossless Attention in Convolutional Networks for Facial Expression Recognition in the Wild—0
LRDif: Diffusion Models for Under-Display Camera Emotion Recognition—0
MAFW: A Large-scale, Multi-modal, Compound Affective Database for Dynamic Facial Expression Recognition in the Wild—0
Magnifying Subtle Facial Motions for Effective 4D Expression Recognition—0
Masked Linear Regression for Learning Local Receptive Fields for Facial Expression Synthesis—0
Memory Integrity of CNNs for Cross-Dataset Facial Expression Recognition—0
Meta Auxiliary Learning for Facial Action Unit Detection—0
Meta Transfer Learning for Emotion Recognition—0
Meta Transfer Learning for Facial Emotion Recognition—0
MFEViT: A Robust Lightweight Transformer-based Network for Multimodal 2D+3D Facial Expression Recognition—0
Micro-expression Action Unit Detection with Spatio-temporal Adaptive Pooling—0
Micro-Facial Expression Recognition Based on Deep-Rooted Learning Algorithm—0
Micro-Facial Expression Recognition in Video Based on Optimal Convolutional Neural Network (MFEOCNN) Algorithm—0
MIMIC: Mask Image Pre-training with Mix Contrastive Fine-tuning for Facial Expression Recognition—0
Mini-ResEmoteNet: Leveraging Knowledge Distillation for Human-Centered Design—0
Mirror Ritual: An Affective Interface for Emotional Self-Reflection—0
MixAugment & Mixup: Augmentation Methods for Facial Expression Recognition—0
Mode Variational LSTM Robust to Unseen Modes of Variation: Application to Facial Expression Recognition—0
Multi-Dimensional, Nuanced and Subjective - Measuring the Perception of Facial Expressions—0
Multi-Domain Norm-referenced Encoding Enables Data Efficient Transfer Learning of Facial Expression Recognition—0
Multi-Energy Guided Image Translation with Stochastic Differential Equations for Near-Infrared Facial Expression Recognition—0
Multi-Label Compound Expression Recognition: C-EXPR Database & Network—0
Multi Loss-based Feature Fusion and Top Two Voting Ensemble Decision Strategy for Facial Expression Recognition in the Wild—0
Multimodal Engagement Analysis from Facial Videos in the Classroom—0
Multi Modal Facial Expression Recognition with Transformer-Based Fusion Networks and Dynamic Sampling—0
Multimodal Prompt Alignment for Facial Expression Recognition—0
Multi-Region Ensemble Convolutional Neural Network for Facial Expression Recognition—0
Affective Behavior Analysis using Action Unit Relation Graph and Multi-task Cross Attention—0
Multi-threshold Deep Metric Learning for Facial Expression Recognition—0
Music Recommendation Based on Facial Emotion Recognition—0
Mutual Information Regularized Identity-aware Facial ExpressionRecognition in Compressed Video—0
MVT: Mask Vision Transformer for Facial Expression Recognition in the wild—0
Noisy Student Training using Body Language Dataset Improves Facial Expression Recognition—0
NR-DFERNet: Noise-Robust Network for Dynamic Facial Expression Recognition—0
Objective Classes for Micro-Facial Expression Recognition—0
Occlusion-Adaptive Deep Network for Robust Facial Expression 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
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