Facial Expression Recognition (FER)
125 papers with code • 24 benchmarks • 29 datasets
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 )
Benchmarks
These leaderboards are used to track progress in Facial Expression Recognition (FER)
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Libraries
Use these libraries to find Facial Expression Recognition (FER) models and implementations
AffectNet
CK+
FER2013
DISFA
RAF-DB
FER+
BP4D
JAFFE
Oulu-CASIA
RaFD
Subtasks
Latest papers with no code
LEAF: Unveiling Two Sides of the Same Coin in Semi-supervised Facial Expression Recognition
LEAF introduces a hierarchical expression-aware aggregation strategy that operates at three levels: semantic, instance, and category.
Cross-Task Multi-Branch Vision Transformer for Facial Expression and Mask Wearing Classification
With wearing masks becoming a new cultural norm, facial expression recognition (FER) while taking masks into account has become a significant challenge.
Dynamic Resolution Guidance for Facial Expression Recognition
Facial expression recognition (FER) is vital for human-computer interaction and emotion analysis, yet recognizing expressions in low-resolution images remains challenging.
Music Recommendation Based on Facial Emotion Recognition
Introduction: Music provides an incredible avenue for individuals to express their thoughts and emotions, while also serving as a delightful mode of entertainment for enthusiasts and music lovers.
Emotic Masked Autoencoder with Attention Fusion for Facial Expression Recognition
Facial Expression Recognition (FER) is a critical task within computer vision with diverse applications across various domains.
Exploring Facial Expression Recognition through Semi-Supervised Pretraining and Temporal Modeling
Facial Expression Recognition (FER) plays a crucial role in computer vision and finds extensive applications across various fields.
DrFER: Learning Disentangled Representations for 3D Facial Expression Recognition
Facial Expression Recognition (FER) has consistently been a focal point in the field of facial analysis.
Towards Realistic Landmark-Guided Facial Video Inpainting Based on GANs
Facial video inpainting plays a crucial role in a wide range of applications, including but not limited to the removal of obstructions in video conferencing and telemedicine, enhancement of facial expression analysis, privacy protection, integration of graphical overlays, and virtual makeup.
LRDif: Diffusion Models for Under-Display Camera Emotion Recognition
This study introduces LRDif, a novel diffusion-based framework designed specifically for facial expression recognition (FER) within the context of under-display cameras (UDC).
Realtime Facial Expression Recognition: Neuromorphic Hardware vs. Edge AI Accelerators
The paper focuses on real-time facial expression recognition (FER) systems as an important component in various real-world applications such as social robotics.
