Facial Emotion Recognition: A multi-task approach using deep learning (2021-10-28T00:00:00.000000Z)

TL;DR

This paper proposes a multi-task learning algorithm, in which a single CNN detects gender, age and race of the subject along with their emotion, and shows that this approach is significantly better than the current State of the art algorithms for this task.

Abstract

Facial Emotion Recognition is an inherently difficult problem, due to vast differences in facial structures of individuals and ambiguity in the emotion displayed by a person. Recently, a lot of work is being done in the field of Facial Emotion Recognition, and the performance of the CNNs for this task has been inferior compared to the results achieved by CNNs in other fields like Object detection, Facial recognition etc. In this paper, we propose a multi-task learning algorithm, in which a single CNN detects gender, age and race of the subject along with their emotion. We validate this proposed methodology using two datasets containing real-world images. The results show that this approach is significantly better than the current State of the art algorithms for this task.

Authors

Aakash Saroop

1 papers

Pathik Ghugare

1 papers

Sashank Mathamsetty

1 papers

Facial Emotion Recognition: A multi-task approach using deep learning

TL;DR

Abstract

Authors

References25 items

Analysis of Artificial Intelligence based Image Classification Techniques

FedVision: An Online Visual Object Detection Platform Powered by Federated Learning

Facial Emotion Recognition using Convolutional Neural Networks

Fast Facial emotion recognition Using Convolutional Neural Networks and Gabor Filters

Reliable Crowdsourcing and Deep Locality-Preserving Learning for Unconstrained Facial Expression Recognition

Multi-task, multi-label and multi-domain learning with residual convolutional networks for emotion recognition

A Brief Review of Facial Emotion Recognition Based on Visual Information

A Comprehensive Analysis of Deep Learning Based Representation for Face Recognition

Facial expression recognition based on local region specific features and support vector machines

Recognition of Facial Emotion through Face Analysis based on Quadratic Bezier Curves

Deep Residual Learning for Image Recognition

Human emotion recognition from facial thermal image based on fused statistical feature and multi-class SVM

A real time facial expression classification system using Local Binary Patterns

FaceNet: A unified embedding for face recognition and clustering

Very Deep Convolutional Networks for Large-Scale Image Recognition

Visualizing and Understanding Convolutional Networks

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

Geometric Feature-Based Facial Expression Recognition in Image Sequences Using Multi-Class AdaBoost and Support Vector Machines

Development of a Facial Emotion Recognition Method Based on Combining AAM with DBN

The Japanese Female Facial Expression (JAFFE) Dataset

Deep Structured Learning for Facial Expression Intensity Estimation

The OpenCV Library

The social constructionist viewpoint

Appendix: The appendix contains the model architecture of the CNN used

TensorFlow: Large-scale machine learning on heterogeneous systems

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