Exploring Convolutional Neural Networks for Facial Expression Recognition: A Comprehensive Survey

Abstract

Facial emotion recognition is a very sought-after field in recent times as it has many important applications such as effective calculations, video game testing and motion capture in video games, human-computer interaction through machine vision, computer research etc. Facial expression is considered a nonverbal form of communication, as it reveals an individual's internal sentiments and emotional states through changes in multiple facial landmark points. Facial identification provides a more comprehensive insight into the person's thoughts and these expressions are analyzed using deep learning methods, such as CNN. The accuracy rates achieved are compared to other methods. In this paper, a concise exploration of diverse applications within Facial Expression Recognition (FER) fields and the publicly accessible data sets employed in FER studies is outlined. FER using multiple different CNN algorithms is also presented. Finally, through comparing multiple different studies of various CNN algorithms, a table and a chart are provided for a better understanding of the rate of accuracy achieved throughout the use of different datasets.