Facial Expression Recognition (FER)
126 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
RAF-DB
DISFA
FER+
BP4D
JAFFE
Oulu-CASIA
RaFD
Subtasks
Latest papers with no code
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.
Open-Set Facial Expression Recognition
Facial expression recognition (FER) models are typically trained on datasets with a fixed number of seven basic classes.
MIMIC: Mask Image Pre-training with Mix Contrastive Fine-tuning for Facial Expression Recognition
In addition, when compared with the domain disparity existing between face datasets and FER datasets, the divergence between general datasets and FER datasets is more pronounced.
Distribution Matching for Multi-Task Learning of Classification Tasks: a Large-Scale Study on Faces & Beyond
Multi-Task Learning (MTL) is a framework, where multiple related tasks are learned jointly and benefit from a shared representation space, or parameter transfer.
FER-C: Benchmarking Out-of-Distribution Soft Calibration for Facial Expression Recognition
We present a soft benchmark for calibrating facial expression recognition (FER).
Multi-Energy Guided Image Translation with Stochastic Differential Equations for Near-Infrared Facial Expression Recognition
Illumination variation has been a long-term challenge in real-world facial expression recognition(FER).
Hypergraph-Guided Disentangled Spectrum Transformer Networks for Near-Infrared Facial Expression Recognition
With the strong robusticity on illumination variations, near-infrared (NIR) can be an effective and essential complement to visible (VIS) facial expression recognition in low lighting or complete darkness conditions.
Contrastive Learning of View-Invariant Representations for Facial Expressions Recognition
ViewFX learns view-invariant features of expression using a proposed self-supervised contrastive loss which brings together different views of the same subject with a particular expression in the embedding space.
Benchmarking Deep Facial Expression Recognition: An Extensive Protocol with Balanced Dataset in the Wild
Facial expression recognition (FER) is a crucial part of human-computer interaction.
Multi Loss-based Feature Fusion and Top Two Voting Ensemble Decision Strategy for Facial Expression Recognition in the Wild
Different from previous studies, this paper applies both internal feature fusion for a single model and feature fusion among multiple networks, as well as the ensemble strategy.
