A Review of Deep Learning models for Facial Emotion Detection

EasyChair Preprint 7738

Abstract

In the recent years facial emotion recognition has been majorly a very powerful topic among researchers, as it has an impactful contribution in effective Human Computer Interaction. Facial emotion recognition is part of affective computing that enables computers to understand human emotions and respond accordingly. This paper provides a brief review of Deep learning models that can be used to enhance the limitations of facial emotion recognition issues. The focus is on up-to-date deep learning approaches such as Convolutional Neural Network, Recurrent Neural Network, Transfer Learning and Generative Adversarial Network. The purpose of this paper is to assist and guide researchers by providing insights and future directions in terms of enhancing this discipline.

Keyphrases: Deep Learning Models, Facial emotion detection, deep learning, survey

@booklet{EasyChair:7738,
  author    = {Deepshikha Mehta and Shweta Barhate and Mahendra Dhore},
  title     = {A Review of Deep Learning models for Facial Emotion Detection},
  howpublished = {EasyChair Preprint 7738},
  year      = {EasyChair, 2022}}