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
Convolutional Neural Networks for Facial Expression Recognition
We have developed convolutional neural networks (CNN) for a facial expression recognition task.
A Compact Embedding for Facial Expression Similarity
Most of the existing work on automatic facial expression analysis focuses on discrete emotion recognition, or facial action unit detection.
Increasingly Packing Multiple Facial-Informatics Modules in A Unified Deep-Learning Model via Lifelong Learning
ivclab/PAE
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Proceedings of the 2019 on International Conference on Multimedia Retrieval 2019
Simultaneously running multiple modules is a key requirement for a smart multimedia system for facial applications including face recognition, facial expression understanding, and gender identification.
Frame attention networks for facial expression recognition in videos
The feature embedding module is a deep Convolutional Neural Network (CNN) which embeds face images into feature vectors.
Suppressing Uncertainties for Large-Scale Facial Expression Recognition
kaiwang960112/Self-Cure-Network
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CVPR 2020
Annotating a qualitative large-scale facial expression dataset is extremely difficult due to the uncertainties caused by ambiguous facial expressions, low-quality facial images, and the subjectiveness of annotators.
Facial Expression Recognition with Deep Learning
One of the most universal ways that people communicate is through facial expressions.
Graph Convolution with Low-rank Learnable Local Filters
ZichenMiao/GNN-with-Low-rank-Learnable-Local-Filters
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ICLR 2021
Recent deep models using graph convolutions provide an appropriate framework to handle such non-Euclidean data, but many of them, particularly those based on global graph Laplacians, lack expressiveness to capture local features required for representation of signals lying on the non-Euclidean grid.
Affect Expression Behaviour Analysis in the Wild using Spatio-Channel Attention and Complementary Context Information
Facial expression recognition(FER) in the wild is crucial for building reliable human-computer interactive systems.
Pre-training strategies and datasets for facial representation learning
1adrianb/unsupervised-face-representation
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Recent work on Deep Learning in the area of face analysis has focused on supervised learning for specific tasks of interest (e. g. face recognition, facial landmark localization etc.)
Facial expression and attributes recognition based on multi-task learning of lightweight neural networks
HSE-asavchenko/face-emotion-recognition
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Moreover, it is shown that the usage of our neural network as a feature extractor of facial regions in video frames and concatenation of several statistical functions (mean, max, etc.)
