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
Most implemented papers
Challenges in Representation Learning: A report on three machine learning contests
phamquiluan/ResidualMaskingNetwork
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The ICML 2013 Workshop on Challenges in Representation Learning focused on three challenges: the black box learning challenge, the facial expression recognition challenge, and the multimodal learning challenge.
Training Deep Networks for Facial Expression Recognition with Crowd-Sourced Label Distribution
Crowd sourcing has become a widely adopted scheme to collect ground truth labels.
Deep Facial Expression Recognition: A Survey
AmrElsersy/Emotions-Recognition
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We then introduce the available datasets that are widely used in the literature and provide accepted data selection and evaluation principles for these datasets.
Facial Motion Prior Networks for Facial Expression Recognition
Deep learning based facial expression recognition (FER) has received a lot of attention in the past few years.
DeXpression: Deep Convolutional Neural Network for Expression Recognition
MaxLikesMath/DeepLearningImplementations
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The proposed architecture achieves 99. 6% for CKP and 98. 63% for MMI, therefore performing better than the state of the art using CNNs.
MicroExpNet: An Extremely Small and Fast Model For Expression Recognition From Face Images
On the other hand, KD is proved to be useful for model compression for the FER problem, and we discovered that its effects gets more and more significant with the decreasing model size.
Automatic Recognition of Student Engagement using Deep Learning and Facial Expression
omidmnezami/Engagement-Recognition
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This paper presents a deep learning model to improve engagement recognition from images that overcomes the data sparsity challenge by pre-training on readily available basic facial expression data, before training on specialised engagement data.
Deep-Emotion: Facial Expression Recognition Using Attentional Convolutional Network
In recent years, several works proposed an end-to-end framework for facial expression recognition, using deep learning models.
Consensual Collaborative Training And Knowledge Distillation Based Facial Expression Recognition Under Noisy Annotations
1980x/CCT
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A dynamic transition mechanism is used to move from supervision loss in early learning to consistency loss for consensus of predictions among networks in the later stage.
Greedy Search for Descriptive Spatial Face Features
Spatial features are derived from displacements of facial landmarks, and carry geometric information.
