Facial Expression Recognition (FER)

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 )

Facial Expression Recognition (FER)

Benchmarks

Libraries

Datasets

Subtasks

Most implemented papers

Challenges in representation learning: A report on three machine learning contests

Content

Training deep networks for facial expression recognition with crowd-sourced label distribution

Facial Motion Prior Networks for Facial Expression Recognition

DeXpression: Deep Convolutional Neural Network for Expression Recognition

Deep Facial Expression Recognition: A Survey

Deep-Emotion: Facial Expression Recognition Using Attentional Convolutional Network

Consensual Collaborative Training And Knowledge Distillation Based Facial Expression Recognition Under Noisy Annotations

Detect Faces Efficiently: A Survey and Evaluations

Automatic Recognition of Student Engagement Using Deep Learning and Facial Expression

MicroExpNet: An Extremely Small and Fast Model For Expression Recognition From Face Images