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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.
AffectNet+: A Database for Enhancing Facial Expression Recognition with Soft-Labels
Balancing the Scales: Enhancing Fairness in Facial Expression Recognition with Latent Alignment
PESFormer: Boosting Macro- and Micro-expression Spotting with Direct Timestamp Encoding
Bridging the Gaps: Utilizing Unlabeled Face Recognition Datasets to Boost Semi-Supervised Facial Expression Recognition
GReFEL: Geometry-Aware Reliable Facial Expression Learning under Bias and Imbalanced Data Distribution
EmojiHeroVR: A Study on Facial Expression Recognition under Partial Occlusion from Head-Mounted Displays
Spatial Action Unit Cues for Interpretable Deep Facial Expression Recognition
AfriHuBERT: A self-supervised speech representation model for African languages
Knowledge-Enhanced Facial Expression Recognition with Emotional-to-Neutral Transformation
ExpLLM: Towards Chain of Thought for Facial Expression Recognition
ResEmoteNet: Bridging Accuracy and Loss Reduction in Facial Emotion Recognition
A Survey on Facial Expression Recognition of Static and Dynamic Emotions
From Bias to Balance: Detecting Facial Expression Recognition Biases in Large Multimodal Foundation Models
EMO-LLaMA: Enhancing Facial Emotion Understanding with Instruction Tuning
The Whole Is Bigger Than the Sum of Its Parts: Modeling Individual Annotators to Capture Emotional Variability
Generalizable Facial Expression Recognition
EEG-SCMM: Soft Contrastive Masked Modeling for Cross-Corpus EEG-Based Emotion Recognition
LLDif: Diffusion Models for Low-light Emotion Recognition
A Lightweight Model Enhancing Facial Expression Recognition with Spatial Bias and Cosine-Harmony Loss
SpotFormer: Multi-Scale Spatio-Temporal Transformer for Facial Expression Spotting
Norface: Improving Facial Expression Analysis by Identity Normalization
Representation Learning and Identity Adversarial Training for Facial Behavior Understanding
Micro-Expression Recognition by Motion Feature Extraction based on Pre-training
Learning with Alignments: Tackling the Inter- and Intra-domain Shifts for Cross-multidomain Facial Expression Recognition
Batch Transformer: Look for Attention in Batch
Real Time Emotion Analysis Using Deep Learning for Education, Entertainment, and Beyond
GCF: Graph Convolutional Networks for Facial Expression Recognition
Less can be more: representational vs. stereotypical gender bias in facial expression recognition
Three-Stream Temporal-Shift Attention Network Based on Self-Knowledge Distillation for Micro-Expression Recognition
Multi-threshold Deep Metric Learning for Facial Expression Recognition
Prompting Whisper for QA-driven Zero-shot End-to-end Spoken Language Understanding
Adaptive Temporal Motion Guided Graph Convolution Network for Micro-expression Recognition
Micro-expression recognition based on depth map to point cloud
EmoBox: Multilingual Multi-corpus Speech Emotion Recognition Toolkit and Benchmark
DB3V: A Dialect Dominated Dataset of Bird Vocalisation for Cross-corpus Bird Species Recognition
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