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
Affective Processes: stochastic modelling of temporal context for emotion and facial expression recognition—0
AffectNet+: A Database for Enhancing Facial Expression Recognition with Soft-Labels—0
A Fine-Grained Facial Expression Database for End-to-End Multi-Pose Facial Expression Recognition—0
AFNet-M: Adaptive Fusion Network with Masks for 2D+3D Facial Expression Recognition—0
A Generative Restricted Boltzmann Machine Based Method for High-Dimensional Motion Data Modeling—0
All-In-One: Facial Expression Transfer, Editing and Recognition Using A Single Network—0
Alzheimer's Disease Diagnosis Based on Cognitive Methods in Virtual Environments and Emotions Analysis—0
An Alternative to Low-level-Sychrony-Based Methods for Speech Detection—0
An optimized Capsule-LSTM model for facial expression recognition with video sequences—0
A novel database of Children's Spontaneous Facial Expressions (LIRIS-CSE)—0
A Novel Geometric Framework on Gram Matrix Trajectories for Human Behavior Understanding—0
A Novel Space-Time Representation on the Positive Semidefinite Con for Facial Expression Recognition—0
A Novel Space-Time Representation on the Positive Semidefinite Cone for Facial Expression Recognition—0
A Peek at Peak Emotion Recognition—0
A Recursive Framework for Expression Recognition: From Web Images to Deep Models to Game Dataset—0
Assessing Gender Bias in Predictive Algorithms using eXplainable AI—0
A Study of Local Binary Pattern Method for Facial Expression Detection—0
A Sub-Layered Hierarchical Pyramidal Neural Architecture for Facial Expression Recognition—0
A Survey of the Trends in Facial and Expression Recognition Databases and Methods—0
A survey on Graph Deep Representation Learning for Facial Expression Recognition—0
Critically examining the Domain Generalizability of Facial Expression Recognition models—0
AU-Aware Vision Transformers for Biased Facial Expression Recognition—0
AU-Guided Unsupervised Domain Adaptive Facial Expression Recognition—0
Automated Pain Detection from Facial Expressions using FACS: A Review—0
Automatic 4D Facial Expression Recognition via Collaborative Cross-domain Dynamic Image Network—0
Automatic Analysis of Facial Expressions Based on Deep Covariance Trajectories—0
Automatic Facial Expression Recognition Using Features of Salient Facial Patches—0
Balancing the Scales: Enhancing Fairness in Facial Expression Recognition with Latent Alignment—0
Baseline CNN structure analysis for facial expression recognition—0
Batch Transformer: Look for Attention in Batch—0
Benchmarking Deep Facial Expression Recognition: An Extensive Protocol with Balanced Dataset in the Wild—0
Bidirectional Warping of Active Appearance Model—0
Boosting Facial Expression Recognition by A Semi-Supervised Progressive Teacher—0
Bounded Residual Gradient Networks (BReG-Net) for Facial Affect Computing—0
CAKE: Compact and Accurate K-dimensional representation of Emotion—0
Capturing Complex Spatio-temporal Relations among Facial Muscles for Facial Expression Recognition—0
CASIA-Face-Africa: A Large-scale African Face Image Database—0
CIAO! A Contrastive Adaptation Mechanism for Non-Universal Facial Expression Recognition—0
Class adaptive threshold and negative class guided noisy annotation robust Facial Expression Recognition—0
Classifying emotions and engagement in online learning based on a single facial expression recognition neural network—0
CLIPER: A Unified Vision-Language Framework for In-the-Wild Facial Expression Recognition—0
CMS-RCNN: Contextual Multi-Scale Region-based CNN for Unconstrained Face Detection—0
Coarse-to-Fine Cascaded Networks with Smooth Predicting for Video Facial Expression Recognition—0
Coherence Constraints in Facial Expression Recognition—0
Combating Uncertainty and Class Imbalance in Facial Expression Recognition—0
Comparing Facial Expression Recognition in Humans and Machines: Using CAM, GradCAM, and Extremal Perturbation—0
Open Compound Domain Adaptation—0
Confidence-Weighted Local Expression Predictions for Occlusion Handling in Expression Recognition and Action Unit detection—0
Constrained Deep Transfer Feature Learning and its Applications—0
Continual Facial Expression Recognition: A Benchmark—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