Facial Emotion Recognition: State of the Art Performance on FER2013

Published in

arXiv.org(2021)

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TL;DR

This work adopts the VGGNet architecture, rigorously fine-tune its hyperparameters, and experiment with various optimization methods to achieve the highest single-network classification accuracy on the FER2013 dataset.

Abstract

Facial emotion recognition (FER) is significant for human-computer interaction such as clinical practice and behavioral description. Accurate and robust FER by computer models remains challenging due to the heterogeneity of human faces and variations in images such as different facial pose and lighting. Among all techniques for FER, deep learning models, especially Convolutional Neural Networks (CNNs) have shown great potential due to their powerful automatic feature extraction and computational efficiency. In this work, we achieve the highest single-network classification accuracy on the FER2013 dataset. We adopt the VGGNet architecture, rigorously fine-tune its hyperparameters, and experiment with various optimization methods. To our best knowledge, our model achieves state-of-the-art single-network accuracy of 73.28 % on FER2013 without using extra training data.

Authors

Yousif Khaireddin

1 papers

Z. Chen

1 papers

References37 items

Learning to Amend Facial Expression Representation via De-albino and Affinity

Facial Emotion Recognition Using Deep Convolutional Neural Network

Facial emotion recognition using convolutional neural networks (FERC)

Optimization for deep learning: theory and algorithms

An Exponential Learning Rate Schedule for Deep Learning

Facial Emotion Recognition: State of the Art Performance on FER2013

Published in

arXiv.org(2021)

External Links:

Generate Graph

TL;DR

Abstract

Authors

Yousif Khaireddin

1 papers

Z. Chen

1 papers

References37 items

Learning to Amend Facial Expression Representation via De-albino and Affinity

Facial Emotion Recognition Using Deep Convolutional Neural Network

Facial emotion recognition using convolutional neural networks (FERC)

Optimization for deep learning: theory and algorithms

An Exponential Learning Rate Schedule for Deep Learning

Extended deep neural network for facial emotion recognition

Automating facial emotion recognition

Deep-Emotion: Facial Expression Recognition Using Attentional Convolutional Network

Fast Facial emotion recognition Using Convolutional Neural Networks and Gabor Filters

A Sufficient Condition for Convergences of Adam and RMSProp

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Local Learning With Deep and Handcrafted Features for Facial Expression Recognition

Super-convergence: very fast training of neural networks using large learning rates

Facial emotion recognition on a dataset using convolutional neural network

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BranchOut: Regularization for Online Ensemble Tracking with Convolutional Neural Networks

Facial Expression Recognition using Convolutional Neural Networks: State of the Art

Facial Expression Recognition with CNN Ensemble

Going deeper in facial expression recognition using deep neural networks

Automatic Analysis of Facial Affect: A Survey of Registration, Representation, and Recognition

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Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift

Adam: A Method for Stochastic Optimization

Very Deep Convolutional Networks for Large-Scale Image Recognition

Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps

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Deep Learning using Linear Support Vector Machines

Improving deep neural networks for LVCSR using rectified linear units and dropout

Fast image scanning with deep max-pooling convolutional neural networks

ImageNet classification with deep convolutional neural networks

Human-Computer Interaction Using Emotion Recognition from Facial Expression

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Adagrad-An Optimizer for Stochastic Gradient Descent

Deep Learning Approaches for Facial Emotion Recognition: A Case Study on FER-2013

Local Learning to Improve Bag of Visual Words Model for Facial Expression Recognition

GradientBased Learning Applied to Document Recognition

Automati Fa ial Expression Analysis: A Survey

Field of Study

Computer Science

Journal Information

Name

ArXiv

Volume

abs/2005.00687

Venue Information

Name

arXiv.org

Type

URL

https://arxiv.org

Alternate Names

ArXiv