Facial Expression Recognition (FER)
123 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
Latest papers with no code
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.
Leave No Stone Unturned: Mine Extra Knowledge for Imbalanced Facial Expression Recognition
Inspired by that, we propose a novel method that leverages re-balanced attention maps to regularize the model, enabling it to extract transformation invariant information about the minor classes from all training samples.
ASM: Adaptive Sample Mining for In-The-Wild Facial Expression Recognition
First, the Adaptive Threshold Learning module generates two thresholds, namely the clean and noisy thresholds, for each category.
InFER: A Multi-Ethnic Indian Facial Expression Recognition Dataset
In this work, we present InFER, a real-world multi-ethnic Indian Facial Expression Recognition dataset consisting of 10, 200 images and 4, 200 short videos of seven basic facial expressions.
