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)
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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
ARBEx: Attentive Feature Extraction with Reliability Balancing for Robust Facial Expression Learning
takihasan/arbex
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We also employ learnable anchor points in the embedding space with label distributions and multi-head self-attention mechanism to optimize performance against weak predictions with reliability balancing, which is a strategy that leverages anchor points, attention scores, and confidence values to enhance the resilience of label predictions.
Neuromorphic Event-based Facial Expression Recognition
Recently, event cameras have shown large applicability in several computer vision fields especially concerning tasks that require high temporal resolution.
Masked Student Dataset of Expressions
Facial expression recognition (FER) algorithms work well in constrained environments with little or no occlusion of the face.
A novel facial emotion recognition model using segmentation VGG-19 architecture
VigneshS10/Segmentation-VGG19
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International Journal of Information Technology 2023
CNN has shown great potential in FER tasks due to its unique feature extraction strategy compared to regular FER models.
Quaternion Orthogonal Transformer for Facial Expression Recognition in the Wild
gabrella/qot
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Firstly, to reduce redundancy among features extracted from pre-trained ResNet-50, we use the orthogonal loss to decompose and compact these features into three sets of orthogonal sub-features.
SimFLE: Simple Facial Landmark Encoding for Self-Supervised Facial Expression Recognition in the Wild
One of the key issues in facial expression recognition in the wild (FER-W) is that curating large-scale labeled facial images is challenging due to the inherent complexity and ambiguity of facial images.
POSTER++: A simpler and stronger facial expression recognition network
POSTER achieves the state-of-the-art (SOTA) performance in FER by effectively combining facial landmark and image features through two-stream pyramid cross-fusion design.
Vision Transformer with Attentive Pooling for Robust Facial Expression Recognition
The proposed APP is employed to select the most informative patches on CNN features, and ATP discards unimportant tokens in ViT.
Pose-disentangled Contrastive Learning for Self-supervised Facial Representation
dreammr/pcl
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CVPR 2023
Self-supervised facial representation has recently attracted increasing attention due to its ability to perform face understanding without relying on large-scale annotated datasets heavily.
More comprehensive facial inversion for more effective expression recognition
We extensively evaluate ASIT on facial datasets such as FFHQ and CelebA-HQ, showing that our approach achieves state-of-the-art facial inversion performance.
