7-Emotion Classification <a id="seven-emotion"></a>

• • Open-Set Facial Expression Recognition - uaiaaaa) |
  • Facial Expression Recognition with Adaptive Frame Rate based on Multiple Testing Correction - savchenko/face-emotion-recognition) |
  • Leave No Stone Unturned: Mine Extra Knowledge for Imbalanced Facial Expression Recognition - uaiaaaa/Mine-Extra-Knowledge) |
  • ASM: Adaptive Sample Mining for In-The-Wild Facial Expression Recognition
  • POSTER: A Pyramid Cross-Fusion Transformer Network for Facial Expression Recognition
  • LA-Net: Landmark-Aware Learning for Reliable Facial Expression Recognition under Label Noise
  • Latent-OFER: Detect, Mask, and Reconstruct with Latent Vectors for Occluded Facial Expression Recognition - OFER) |
  • Prompting Visual-Language Models for Dynamic Facial Expression Recognition - CLIP) |
  • MAE-DFER: Efficient Masked Autoencoder for Self-supervised Dynamic Facial Expression Recognition - DFER) |
  • Rethinking the Learning Paradigm for Facial Expression Recognition
  • Multi-Domain Norm-Referenced Encoding enables Data Efficient Transformer Learning of Facial Expression Recognition
  • Revisiting Self-Supervised Contrastive Learning for Facial Expression Recognition
  • Cluster-level pseudo-labelling for source-free cross-domain facial expression recognition
  • Biased Heritage: How Datasets Shape Models in Facial Expression Recognition - upna/biased-heritage) |
  • Rethinking Affect Analysis: A Protocol for Ensuring Fairness and Consistency
  • LLDif: Diffusion Models for Low-light Emotion Recognition
  • SynFER: Towards Boosting Facial Expression Recognition with Synthetic Data
  • From Macro to Micro: Boosting Micro-Expression Recognition via Pre-Training on Macro-Expression Videos
  • FacePhi: Light-Weight Multi-Modal Large Language Model for Facial Landmark Emotion Recognition
  • Enhancing Zero-Shot Facial Expression Recognition by LLM Knowledge Transfer - CLIP) |
  • MSSTNET: A Multi-Scale Spatio-Temporal CNN-Transformer Network for Dynamic Facial Expression Recognition
  • Open-Set Facial Expression Recognition - uaiaaaa) |
  • MIDAS: Mixing Ambiguous Data with Soft Labels for Dynamic Facial Expression Recognition
  • Hard Sample-aware Consistency for Low-resolution Facial Expression Recognition
  • Leave No Stone Unturned: Mine Extra Knowledge for Imbalanced Facial Expression Recognition - uaiaaaa/Mine-Extra-Knowledge) |
  • ASM: Adaptive Sample Mining for In-The-Wild Facial Expression Recognition
  • POSTER: A Pyramid Cross-Fusion Transformer Network for Facial Expression Recognition
  • GaFET: Learning Geometry-aware Facial Expression Translation from In-The-Wild Images
  • LA-Net: Landmark-Aware Learning for Reliable Facial Expression Recognition under Label Noise
  • Latent-OFER: Detect, Mask, and Reconstruct with Latent Vectors for Occluded Facial Expression Recognition - OFER) |
  • Addressing Racial Bias in Facial Emotion Recognition
  • Learning Deep Hierarchical Features with Spatial Regularization for One-Class Facial Expression Recognition
  • Facial Expression Recognition with Adaptive Frame Rate based on Multiple Testing Correction
  • Feature Representation Learning with Adaptive Displacement Generation and Transformer Fusion for Micro-Expression Recognition
  • Rethinking the Learning Paradigm for Facial Expression Recognition
  • Multi-Domain Norm-Referenced Encoding enables Data Efficient Transformer Learning of Facial Expression Recognition
  • Uncertainty-aware Label Distribution Learning for Facial Expression Recognition - distribution-learning-fer-tf/) |
  • POSTER V2: A simpler and stronger facial expression recognition network - Q/POSTER_V2) |
  • Prompting Visual-Language Models for Dynamic Facial Expression Recognition - CLIP) |
  • MAE-DFER: Efficient Masked Autoencoder for Self-supervised Dynamic Facial Expression Recognition - DFER) |
  • Learn From All: Erasing Attention Consistency for Noisy Label Facial Expression Recognition - uaiaaaa/Erasing-Attention-Consistency) |
  • Learn-to-Decompose: Cascaded Decomposition Network for Cross-Domain Few-Shot Facial Expression Recognition
  • Teaching with Soft Label Smoothing for Mitigating Noisy Labels in Facial Expressions
  • Towards Semi-Supervised Deep Facial Expression Recognition with An Adaptive Confidence Margin - CM/) |
  • Face2Exp: Combating Data Biases for Facial Expression Recognition
  • Facial Expression Recognition By Using a Disentangled Identity Invariant Expression Representation - web.ing.unimore.it/icpr/media/posters/12024.pdf) | ICPR | ⭐️⭐️ | N/A |
  • Vision Transformer Equipped with Neural Resizer on Facial Expression Recognition Task
  • A Prototype-Oriented Contrastive Adaption Network For Cross domain Facial Expression Recognition
  • Soft Label Mining and Average Expression Anchoring for Facial Expression Recognition - AEA) |
  • Revisiting Self-Supervised Contrastive Learning for Facial Expression Recognition
  • Cluster-level pseudo-labelling for source-free cross-domain facial expression recognition
  • Analysis of Semi-Supervised Methods for Facial Expression Recognition
  • TransFER: Learning Relation-aware Facial Expression Representations with Transformers
  • Understanding and Mitigating Annotation Bias in Facial Expression Recognition
  • Dive into Ambiguity: Latent Distribution Mining and Pairwise Uncertainty Estimation for Facial Expression Recognition - CV/FaceX-Zoo/tree/main/addition_module/DMUE) |
  • Affective Processes: stochastic modelling of temporal context for emotion and facial expression recognition
  • Identity-Aware Facial Expression Recognition Via Deep Metric Learning Based on Synthesized Images
  • Relative Uncertainty Learning for Facial Expression Recognition - uaiaaaa/Relative-Uncertainty-Learning) |
  • Identity-Free Facial Expression Recognition using conditional Generative Adversarial Network
  • Feature Decomposition and Reconstruction Learning for Effective Facial Expression Recognition
  • Learning a Facial Expression Embedding Disentangled from Identity
  • Affect2MM: Affective Analysis of Multimedia Content Using Emotion Causality
  • A Circular-Structured Representation for Visual Emotion Distribution Learning
  • Temporal Stochastic Softmax for 3D CNNs: An Application in Facial Expression Recognition - stochastic-softmax) |
  • Facial Expression Recognition in the Wild via Deep Attentive Center Loss
  • Domain Generalisation for Apparent Emotional Facial Expression Recognition across Age-Groups
  • Suppressing Uncertainties for Large-Scale Facial Expression Recognition - Cure-Network) |
  • Label Distribution Learning on Auxiliary Label Space Graphs for Facial Expression Recognition
  • Graph Neural Networks for Image Understanding Based on Multiple Cues: Group Emotion Recognition and Event Recognition as Use Cases - Neural-Networks-for-Image-Understanding-Based-on-Multiple-Cues) |
  • Detecting Face2Face Facial Reenactment in Videos
  • ExpLLM: Towards Chain of Thought for Facial Expression Recognition
  • Feature Representation Learning with Adaptive Displacement Generation and Transformer Fusion for Micro-Expression Recognition
  • Addressing Racial Bias in Facial Emotion Recognition
  • Generalizable Facial Expression Recognition - uaiaaaa/Generalizable-FER) |
  • Norface: Improving Facial Expression Analysis by Identity Normalization - fea.github.io/) |
  • Bridging the Gaps: Utilizing Unlabeled Face Recognition Datasets to Boost Semi-Supervised Facial Expression Recognition
  • GaFET: Learning Geometry-aware Facial Expression Translation from In-The-Wild Images

• Facial Expression Manipulation and Synthesis [back-to-top](#seven-emotion) <a id="fem"></a>

  • Efficient Emotional Adaptation for Audio-Driven Talking-Head Generation
  • DisCoHead: Audio-and-Video-Driven Talking Head Generation by Disentangled Control of Head Pose and Facial Expressions - research.github.io/discohead/) |
  • Seeing What You Said: Talking Face Generation Guided by a Lip Reading Expert
  • TransEditor: Transformer-Based Dual-Space GAN for Highly Controllable Facial Editing
  • EAMM: One-Shot Emotional Talking Face via Audio-Based Emotion-Aware Motion Model
  • Learning an Animatable Detailed 3D Face Model from In-The-Wild Images
  • Information Bottlenecked Variational Autoencoder for Disentangled 3D Facial Expression Modelling
  • Detection and Localization of Facial Expression Manipulations
  • Talk-to-Edit: Fine-Grained Facial Editing via Dialog - to-Edit) |
  • Audio-Driven Emotional Video Portraits
  • GANmut: Learning Interpretable Conditional Space for Gamut of Emotions
  • 3D Dense Geometry-Guided Facial Expression Synthesis by Adversarial Learning
  • FACIAL: Synthesizing Dynamic Talking Face with Implicit Attribute Learning
  • Cascade EF-GAN: Progressive Facial Expression Editing with Local Focuses
  • Interpreting the latent space of gans for semantic face editing
  • Towards Localized Fine-Grained Control for Facial Expression Generation
  • 3D Facial Expressions through Analysis-by-Neural-Synthesis
  • FlowVQTalker: High-Quality Emotional Talking Face Generation through Normalizing Flow and Quantization
  • EMOPortraits: Emotion-enhanced Multimodal One-shot Head Avatars
  • FG-EmoTalk: Talking Head Video Generation with Fine-Grained Controllable Facial Expressions
  • EmoStyle: One-Shot Facial Expression Editing Using Continuous Emotion Parameters
  • EmoTalk: Speech-Driven Emotional Disentanglement for 3D Face Animation
  • EMMN: Emotional Motion Memory Network for Audio-driven Emotional Talking Face Generation
  • Efficient Emotional Adaptation for Audio-Driven Talking-Head Generation
  • DisCoHead: Audio-and-Video-Driven Talking Head Generation by Disentangled Control of Head Pose and Facial Expressions - research.github.io/discohead/) |
  • Learning Adaptive Spatial Coherent Correlations for Speech-Preserving Facial Expression Manipulation
  • FSRT: Facial Scene Representation Transformer for Face Reenactment from Factorized Appearance, Head-pose, and Facial Expression Features
  • LipFormer: High-fidelity and Generalizable Talking Face Generation with A Pre-learned Facial Codebook
  • SadTalker: Learning Realistic 3D Motion Coefficients for Stylized Audio-Driven Single Image Talking Face Animation
  • Identity-Preserving Talking Face Generation with Landmark and Appearance Priors
  • OTAvatar : One-shot Talking Face Avatar with Controllable Tri-plane Rendering
  • High-fidelity Generalized Emotional Talking Face Generation with Multi-modal Emotion Space Learning
  • EMOCA: Emotion Driven Monocular Face Capture and Animation
  • 4D Facial Expression Diffusion Model
  • Sparse to Dense Dynamic 3D Facial Expression Generation - 3DSAM/Sparse2Dense) |
  • Neural Emotion Director: Speech-preserving semantic control of facial expressions in “in-the-wild” videos
  • All You Need is Your Voice: Emotional Face Representation with Audio Perspective for Emotional Talking Face Generation
  • AnimateMe: 4D Facial Expressions via Diffusion Models

• Valence-arousal Affect Estimation and Analysis [back-to-top](#seven-emotion) <a id="affect"></a>

  • Learning from Label Relationships in Human Affect
  • Are 3D Face Shapes Expressive Enough for Recognising Continuous Emotions and Action Unit Intensities?
  • Optimal Transport-based Identity Matching for Identity-invariant Facial Expression Recognition
  • Detail-Enhanced Intra- and Inter-modal Interaction for Audio-Visual Emotion Recognition
  • Bridging the Gap: Protocol Towards Fair and Consistent Affect Analysis - Consistent-Affect-Analysis) |
  • Optimal Transport-based Identity Matching for Identity-invariant Facial Expression Recognition
  • Emotion-aware Multi-view Contrastive Learning for Faciel Emotion Recognition
  • Inconsistency-Aware Cross-Attention for Audio-Visual Fusion in Dimensional Emotion Recognition
  • CAGE: Circumplex Affect Guided Expression Inference - niklas/CAGE_expression_inference) |
  • Cross-Attention is Not Always Needed: Dynamic Cross-Attention for Audio-Visual Dimensional Emotion Recognition
  • Learning from Label Relationships in Human Affect
  • Contrastive Adversarial Learning for Person Independent Facial Emotion Recognition - Adversarial-Learning-FER) |
  • Estimating continuous affect with label uncertainty
  • Factorized Higher-Order CNNs with an Application to Spatio-Temporal Emotion Estimation
  • BReG-NeXt: Facial affect computing using adaptive residual networks with bounded gradient - NeXt) |
  • Ig3D: Integrating 3D Face Representations in Facial Expression Inference
  • 3DEmo: for Portrait Emotion Recognition with New Dataset

• About Facial Privacy [back-to-top](#seven-emotion) <a id="privacy"></a>

  • here
  • GANonymization: A GAN-based Face Anonymization Framework for Preserving Emotional Expressions
  • Privacy-Preserving Face Recognition with Learnable Privacy Budgets in Frequency Domain
  • Anonymization Prompt Learning for Facial Privacy-Preserving Text-to-Image Generation
  • ϵ-Mesh Attack: A Surface-based Adversarial Point Cloud Attack for Facial Expression Recognition - mesh-attack) |
  • Walk as you feel: Privacy preserving emotion recognition from gait patterns
  • GANonymization: A GAN-based Face Anonymization Framework for Preserving Emotional Expressions
  • Simulated adversarial testing of face recognition models
  • Exploring frequency adversarial attacks for face forgery detection
  • Protecting Facial Privacy: Generating Adversarial Identity Masks via Style-robust Makeup Transfer - codes/AMT-GAN) |
  • AdverFacial: Privacy-Preserving Universal Adversarial Perturbation Against Facial Micro-Expression Leakages
  • Lie to me: shield your emotions from prying software
  • Point adversarial self-mining: A simple method for facial expression recognition
  • Improving transferability of adversarial patches on face recognition with generative models
  • Towards face encryption by generating adversarial identity masks - IM) |
  • Disentangled Representation with Dual-stage Feature Learning for Face Anti-spoofing
  • ϵ-Mesh Attack: A Surface-based Adversarial Point Cloud Attack for Facial Expression Recognition - mesh-attack) |
  • DuetFace: Collaborative Privacy-Preserving Face Recognition via Channel Splitting in the Frequency Domain

• Emotion Recognition, Facial Representations, and Others [back-to-top](#seven-emotion) <a id="er-fr-o"></a>

  • The Strong Pull of Prior Knowledge in Large Language Models and Its Impact on Emotion Recognition
  • Distilling Privileged Multimodal Information for Expression Recognition using Optimal Transport
  • EmoCLIP: A Vision-Language Method for Zero-Shot Video Facial Expression Recognition
  • Deep Imbalanced Learning for Multimodal Emotion Recognition in Conversations
  • An Empirical Study of Super-resolution on Low-resolution Micro-expression Recognition
  • Decoupled Multimodal Distilling for Emotion Recognition
  • Context De-confounded Emotion Recognition
  • Norms of valence, arousal, and dominance for 13,915 English lemmas
  • Norms of valence, arousal, and dominance for 13,915 English lemmas
  • DrFER: Learning Disentangled Representations for 3D Facial Expression Recognition
  • Norms of valence, arousal, and dominance for 13,915 English lemmas
  • Norms of valence, arousal, and dominance for 13,915 English lemmas
  • Norms of valence, arousal, and dominance for 13,915 English lemmas
  • Norms of valence, arousal, and dominance for 13,915 English lemmas
  • Norms of valence, arousal, and dominance for 13,915 English lemmas
  • Norms of valence, arousal, and dominance for 13,915 English lemmas
  • Norms of valence, arousal, and dominance for 13,915 English lemmas
  • Evaluating and Inducing Personality in Pre-trained Language Models
  • Norms of valence, arousal, and dominance for 13,915 English lemmas
  • Norms of valence, arousal, and dominance for 13,915 English lemmas
  • Norms of valence, arousal, and dominance for 13,915 English lemmas
  • Norms of valence, arousal, and dominance for 13,915 English lemmas
  • Norms of valence, arousal, and dominance for 13,915 English lemmas
  • Norms of valence, arousal, and dominance for 13,915 English lemmas
  • Norms of valence, arousal, and dominance for 13,915 English lemmas
  • Norms of valence, arousal, and dominance for 13,915 English lemmas
  • Norms of valence, arousal, and dominance for 13,915 English lemmas
  • Norms of valence, arousal, and dominance for 13,915 English lemmas
  • Generative Technology for Human Emotion Recognition: A Scope Review
  • Multimodal Prompt Learning with Missing Modalities for Sentiment Analysis and Emotion Recognition
  • AffectGPT: Dataset and Framework for Explainable Multimodal Emotion Recognition
  • Towards Context-Aware Emotion Recognition Debiasing from a Causal Demystification Perspective via De-confounded Training
  • Norms of valence, arousal, and dominance for 13,915 English lemmas
  • Learning Emotion Representations from Verbal and Nonverbal Communication
  • Multivariate, Multi-frequency and Multimodal: Rethinking Graph Neural Networks for Emotion Recognition in Conversation
  • How you feelin’? Learning Emotions and Mental States in Movie Scenes - ai.github.io/projects/emotx/) |
  • Decoupled Multimodal Distilling for Emotion Recognition
  • Context De-confounded Emotion Recognition
  • More is Better: A Database for Spontaneous Micro-Expression with High Frame Rates
  • EmoCLIP: A Vision-Language Method for Zero-Shot Video Facial Expression Recognition
  • Towards affective computing that works for everyone
  • Emotional Listener Portrait: Realistic Listener Motion Simulation in Conversation
  • Affective Image Filter: Reflecting Emotions from Text to Images
  • EmoGen: Emotional Image Content Generation with Text-to-Image Diffusion Models
  • A Unified and Interpretable Emotion Representation and Expression Generation - diffusion.github.io/) |
  • Weakly-Supervised Emotion Transition Learning for Diverse 3D Co-speech Gesture Generation - lab.github.io/Emo-Transition-Gesture/) |
  • EmoVIT: Revolutionizing Emotion Insights with Visual Instruction Tuning
  • Robust Emotion Recognition in Context Debiasing
  • Region-Based Emotion Recognition via Superpixel Feature Pooling
  • Emotion Recognition from the perspective of Activity Recognition
  • GPT as Psychologist? Preliminary Evaluations for GPT-4V on Visual Affective Computing - Research/GPT4Affectivity) |
  • The Strong Pull of Prior Knowledge in Large Language Models and Its Impact on Emotion Recognition
  • DrFER: Learning Disentangled Representations for 3D Facial Expression Recognition
  • Distilling Privileged Multimodal Information for Expression Recognition using Optimal Transport
  • Beyond Accuracy: Fairness, Scalability, and Uncertainty Considerations in Facial Emotion Recognition
  • Deep Imbalanced Learning for Multimodal Emotion Recognition in Conversations
  • Norms of valence, arousal, and dominance for 13,915 English lemmas
  • Pre-training strategies and datasets for facial representation learning - face-representation) |
  • Multi-Dimensional, Nuanced and Subjective – Measuring the Perception of Facial Expressions
  • General Facial Representation Learning in a Visual-Linguistic Manner
  • Robust Egocentric Photo-realistic Facial Expression Transfer for Virtual Reality
  • Fair Contrastive Learning for Facial Attribute Classification - CoolG/FSCL) |
  • Quantified Facial Expressiveness for Affective Behavior Analytics
  • Deep facial expression recognition: A survey
  • iMiGUE: An Identity-free Video Dataset for Micro-Gesture Understanding and Emotion Analysis
  • Emotions as overlapping causal networks of emotion components: Implications and methodological approaches
  • An Empirical Study of Super-resolution on Low-resolution Micro-expression Recognition
  • Norms of valence, arousal, and dominance for 13,915 English lemmas
  • Norms of valence, arousal, and dominance for 13,915 English lemmas
  • Weakly Supervised Video Emotion Detection and Prediction via Cross-Modal Temporal Erasing Network - zhichengzhang/WECL) |
  • Latent to Latent: A Learned Mapper for Identity Preserving Editing of Multiple Face Attributes in StyleGAN-generated Images - To-Latent) |
  • FaceForensics++: Learning to Detect Manipulated Facial Images
  • Graph-Structured Referring Expression Reasoning in The Wild
  • EmotiCon: Context-Aware Multimodal Emotion Recognition using Frege’s Principle
  • Learning Visual Emotion Representations from Web Data - stonybrook/EmotionNet_CVPR2020) |
  • Computational Models of Emotion Inference in Theory of Mind: A Review and Roadmap
  • Putting feelings into words: Affect labeling as implicit emotion regulation
  • Affective cognition: Exploring lay theories of emotion
  • Facial Expression Recognition: A Survey
  • Norms of valence, arousal, and dominance for 13,915 English lemmas
  • Facial expression and emotion
  • Understanding face recognition
  • A circumplex model of affect
  • Norms of valence, arousal, and dominance for 13,915 English lemmas
  • Norms of valence, arousal, and dominance for 13,915 English lemmas
  • Norms of valence, arousal, and dominance for 13,915 English lemmas
  • How you feelin’? Learning Emotions and Mental States in Movie Scenes - ai.github.io/projects/emotx/) |
  • Norms of valence, arousal, and dominance for 13,915 English lemmas
  • Norms of valence, arousal, and dominance for 13,915 English lemmas
  • Norms of valence, arousal, and dominance for 13,915 English lemmas
  • FSFM: A Generalizable Face Security Foundation Model via Self-Supervised Facial Representation Learning - 3c.github.io/) |
  • Norms of valence, arousal, and dominance for 13,915 English lemmas
  • Norms of valence, arousal, and dominance for 13,915 English lemmas
  • Emotion-LLaMA: Multimodal Emotion Recognition and Reasoning with Instruction Tuning - LLaMA) [Demo](https://huggingface.co/spaces/ZebangCheng/Emotion-LLaMA) |
  • EAI: Emotional Decision-Making of LLMs in Strategic Games and Ethical Dilemmas - Institute/EAI-Framework) |
  • Norms of valence, arousal, and dominance for 13,915 English lemmas
  • Norms of valence, arousal, and dominance for 13,915 English lemmas
  • Affective Visual Dialog: A Large-Scale Benchmark for Emotional Reasoning Based on Visually Grounded Conversations - visual-dialog.github.io/) |
  • Training A Small Emotional Vision Language Model for Visual Art Comprehension - code) |
  • Norms of valence, arousal, and dominance for 13,915 English lemmas
  • Norms of valence, arousal, and dominance for 13,915 English lemmas
  • Norms of valence, arousal, and dominance for 13,915 English lemmas
  • Norms of valence, arousal, and dominance for 13,915 English lemmas
  • A survey on Graph Deep Representation Learning for Facial Expression Recognition
  • Norms of valence, arousal, and dominance for 13,915 English lemmas
  • Exploring Vision Language Models for Facial Attribute Recognition: Emotion, Race, Gender, and Age
  • A Survey on Facial Expression Recognition of Static and Dynamic Emotions
  • Emotion-LLaMA: Multimodal Emotion Recognition and Reasoning with Instruction Tuning - LLaMA) [Demo](https://huggingface.co/spaces/ZebangCheng/Emotion-LLaMA) |
  • Norms of valence, arousal, and dominance for 13,915 English lemmas
  • Norms of valence, arousal, and dominance for 13,915 English lemmas
  • Norms of valence, arousal, and dominance for 13,915 English lemmas
  • Norms of valence, arousal, and dominance for 13,915 English lemmas
  • Hidden Emotion Detection using Multi-modal Signals
  • Norms of valence, arousal, and dominance for 13,915 English lemmas
  • Norms of valence, arousal, and dominance for 13,915 English lemmas
  • More is Better: A Database for Spontaneous Micro-Expression with High Frame Rates
  • Norms of valence, arousal, and dominance for 13,915 English lemmas
  • Norms of valence, arousal, and dominance for 13,915 English lemmas
  • To Err Like Human: Affective Bias-Inspired Measures for Visual Emotion Recognition Evaluation - nku/ECC) |
  • Apathetic or Empathetic? Evaluating LLMs' Emotional Alignments with Humans - ARISE/EmotionBench) |
  • Norms of valence, arousal, and dominance for 13,915 English lemmas
  • Norms of valence, arousal, and dominance for 13,915 English lemmas
  • Norms of valence, arousal, and dominance for 13,915 English lemmas

• Challenges [back-to-top](#seven-emotion) <a id="challenges"></a>

  • Site
  • Site
  • Paper
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  • Paper - behavior-analysis-in-the-wild.github.io/8th/) [Github](https://github.com/av-savchenko/EmotiEffLib/tree/main/training_and_examples/ABAW/ABAW8) |

• Tools [back-to-top](#seven-emotion) <a id="tools"></a>

  • PyTorch
  • Learning a model of facial shape and expression from 4D scans
  • Web
  • Joint Face Detection and Alignment using Multi-task Cascaded Convolutional Networks - pytorch) [TensorFlow](https://github.com/davidsandberg/facenet) |

• Datasets [back-to-top](#seven-emotion) <a id="datasets"></a>

  • FindingEmo: An Image Dataset for Emotion Recognition in the Wild
  • EmoSet: A Large-scale Visual Emotion Dataset with Rich Attributes
  • FindingEmo: An Image Dataset for Emotion Recognition in the Wild
  • VEATIC: Video-based Emotion and Affect Tracking in Context Dataset
  • EmoSet: A Large-scale Visual Emotion Dataset with Rich Attributes
  • MimicME: A Large Scale Diverse 4D Database for Facial Expression Analysis
  • CelebV-HQ: A Large-Scale Video Facial Attributes Dataset - hq.github.io/) [GitHub](https://github.com/CelebV-HQ/CelebV-HQ) [Demo](https://www.youtube.com/watch?v=Y0uxlUW4sW0) |
  • FERV39k: A Large-Scale Multi-Scene Dataset for Facial Expression Recognition in Videos
  • MAFW: A Large-scale, Multi-modal, Compound Affective Database for Dynamic Facial Expression Recognition in the Wild - database.github.io/MAFW/) |
  • __Aff-wild2__: Extending the aff-wild database for affect recognition - wild2/) |
  • __Aff-wild__: valence and arousal 'In-the-Wild' challenge - affect-wild-challenge/) |
  • Facial Dynamics in Video: Instruction Tuning for Improved Facial Expression Perception and Contextual Awareness - star/FacialDynamic) |
  • $E^3$ : Exploring Embodied Emotion Through A Large-Scale Egocentric Video Dataset - embodied-emotion-official.github.io/) |
  • EEVR: A Dataset of Paired Physiological Signals and Textual Descriptions for Joint Emotion Representation Learning
  • FindingEmo: An Image Dataset for Emotion Recognition in the Wild

• Remarkable Papers (2019~) [back-to-top](#seven-emotion) <a id="previous"></a>

  • Facial expression recognition from near-infrared videos
  • Facial expression recognition from near-infrared videos
  • Facial expression recognition from near-infrared videos
  • Facial expression recognition from near-infrared videos
  • Facial expression recognition from near-infrared videos
  • Facial expression recognition from near-infrared videos
  • Facial expression recognition from near-infrared videos
  • Facial expression recognition from near-infrared videos
  • Facial expression recognition from near-infrared videos
  • Facial expression recognition from near-infrared videos
  • Facial expression recognition from near-infrared videos
  • Facial expression recognition from near-infrared videos
  • Facial expression recognition from near-infrared videos
  • Facial expression recognition from near-infrared videos
  • A Compact Embedding for Facial Expression Similarity
  • A Personalized Affective Memory Model for Improving Emotion Recognition - AffMem) |
  • Facial Expression Recognition by De-expression Residue Learning
  • Joint pose and expression modeling for facial expression recognition - expression-recognition) |
  • Identity-Aware Convolutional Neural Network for Facial Expression Recognition
  • Facenet2expnet: Regularizing a deep face recognition net for expression recognition
  • Facial expression recognition from near-infrared videos
  • Facial expression decomposition
  • Facial expression recognition from near-infrared videos

• Facial Action Unit (AU) Detection (or Recognition) [back-to-top](#seven-emotion) <a id="au"></a>

  • Causal intervention for subject-deconfounded facial action unit recognition
  • Trend-Aware Supervision: On Learning Invariance for Semi-supervised Facial Action Unit Intensity Estimation
  • FAN-Trans: Online Knowledge Distillation for Facial Action Unit Detection
  • Knowledge-Driven Self-Supervised Representation Learning for Facial Action Unit Recognition
  • Towards Accurate Facial Landmark Detection via Cascaded Transformers
  • Causal intervention for subject-deconfounded facial action unit recognition
  • PIAP-DF: Pixel-Interested and Anti Person-Specific Facial Action Unit Detection Net with Discrete Feedback Learning

• Multi-modal, EEG-based Emotion Recognition [back-to-top](#seven-emotion) <a id="mm-er"></a>

  • A Comprehensive Survey on EEG-Based Emotion Recognition: A Graph-Based Perspective
  • Beyond Mimicking Under-Represented Emotions: Deep Data Augmentation with Emotional Subspace Constraints for EEG-Based Emotion Recognition
  • A Brain-Inspired Way of Reducing the Network Complexity via Concept-Regularized Coding for Emotion Recognition - conceptual-knowledge) |