Facial Expression Recognition (FER)
127 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)
| Trend | Dataset | Best Model | Paper | Code | Compare |
|---|---|---|---|---|---|
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
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
Most implemented papers
Facial Emotion Recognition Using Transfer Learning in the Deep CNN
Human facial emotion recognition (FER) has attracted the attention of the research community for its promising applications.
Facial Emotion Recognition: State of the Art Performance on FER2013
usef-kh/fer
•
•
Facial emotion recognition (FER) is significant for human-computer interaction such as clinical practice and behavioral description.
Distract Your Attention: Multi-head Cross Attention Network for Facial Expression Recognition
yaoing/dan
•
•
To address these issues, we propose our DAN with three key components: Feature Clustering Network (FCN), Multi-head cross Attention Network (MAN), and Attention Fusion Network (AFN).
Detect Faces Efficiently: A Survey and Evaluations
However, with the tremendous increase in images and videos with variations in face scale, appearance, expression, occlusion and pose, traditional face detectors are challenged to detect various "in the wild" faces.
Complete Face Recovery GAN: Unsupervised Joint Face Rotation and De-Occlusion From a Single-View Image
yeongjoonJu/CFR-GAN
•
•
Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) 2022
In addition, the lack of high-quality paired data remains an obstacle for both methods.
Explore the Expression: Facial Expression Generation using Auxiliary Classifier Generative Adversarial Network
azadlab/FExGAN
•
•
Facial expressions are a form of non-verbal communication that humans perform seamlessly for meaningful transfer of information.
Video-Based Frame-Level Facial Analysis of Affective Behavior on Mobile Devices Using EfficientNets
HSE-asavchenko/face-emotion-recognition
•
•
CVPR Workshop 2022
In this paper, we consider the problem of real-time video-based facial emotion analytics, namely, facial expression recognition, prediction of valence and arousal and detection of action unit points.
Classifying emotions and engagement in online learning based on a single facial expression recognition neural network
HSE-asavchenko/face-emotion-recognition
•
•
IEEE Transactions on Affective Computing 2022
It is shown that the resulting facial features can be used for fast simultaneous prediction of students’ engagement levels (from disengaged to highly engaged), individual emotions (happy, sad, etc.,) and group-level affect (positive, neutral or negative).
Analysis of Semi-Supervised Methods for Facial Expression Recognition
To reduce the reliance of deep neural solutions on labeled data, state-of-the-art semi-supervised methods have been proposed in the literature.
Optimal Transport-based Identity Matching for Identity-invariant Facial Expression Recognition
Specifically, to find pairs of similar expressions from different identities, we define the inter-feature similarity as a transportation cost.
