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Facial Expression Recognition (FER)
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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.
<span style="color:grey; opacity: 0.6">( Image credit: DeXpression )</span>
논문
이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.
Enhancing Zero-Shot Facial Expression Recognition by LLM Knowledge Transfer
From Macro to Micro: Boosting micro-expression recognition via pre-training on macro-expression videos
Hierarchical Space-Time Attention for Micro-Expression Recognition
FER-YOLO-Mamba: Facial Expression Detection and Classification Based on Selective State Space
Ada-DF: An Adaptive Label Distribution Fusion Network For Facial Expression Recognition
CAGE: Circumplex Affect Guided Expression Inference
LEAF: Unveiling Two Sides of the Same Coin in Semi-supervised Facial Expression Recognition
Cross-Task Multi-Branch Vision Transformer for Facial Expression and Mask Wearing Classification
Meta-Auxiliary Learning for Micro-Expression Recognition
Joint Contrastive Learning with Feature Alignment for Cross-Corpus EEG-based Emotion Recognition
eMotion-GAN: A Motion-based GAN for Photorealistic and Facial Expression Preserving Frontal View Synthesis
Dynamic Resolution Guidance for Facial Expression Recognition
Music Recommendation Based on Facial Emotion Recognition
Joint Training on Multiple Datasets With Inconsistent Labeling Criteria for Facial Expression Recognition
Multi-Scale Spatio-Temporal Graph Convolutional Network for Facial Expression Spotting
A Lightweight Attention-based Deep Network via Multi-Scale Feature Fusion for Multi-View Facial Expression Recognition
Audio-Visual Compound Expression Recognition Method based on Late Modality Fusion and Rule-based Decision
Emotic Masked Autoencoder with Attention Fusion for Facial Expression Recognition
Exploring Facial Expression Recognition through Semi-Supervised Pretraining and Temporal Modeling
DrFER: Learning Disentangled Representations for 3D Facial Expression Recognition
GPT as Psychologist? Preliminary Evaluations for GPT-4V on Visual Affective Computing
Parameter Efficient Finetuning for Speech Emotion Recognition and Domain Adaptation
HunFlair2 in a cross-corpus evaluation of biomedical named entity recognition and normalization tools
Towards Realistic Landmark-Guided Facial Video Inpainting Based on GANs
LRDif: Diffusion Models for Under-Display Camera Emotion Recognition
Guided Interpretable Facial Expression Recognition via Spatial Action Unit Cues
Realtime Facial Expression Recognition: Neuromorphic Hardware vs. Edge AI Accelerators
Expression-aware video inpainting for HMD removal in XR applications
Open-Set Facial Expression Recognition
Investigating the Generalizability of Physiological Characteristics of Anxiety
MIMIC: Mask Image Pre-training with Mix Contrastive Fine-tuning for Facial Expression Recognition
Distribution Matching for Multi-Task Learning of Classification Tasks: a Large-Scale Study on Faces & Beyond
Information Type Classification with Contrastive Task-Specialized Sentence Encoders
FER-C: Benchmarking Out-of-Distribution Soft Calibration for Facial Expression Recognition
Hypergraph-Guided Disentangled Spectrum Transformer Networks for Near-Infrared Facial Expression Recognition
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