Facial Expression Recognition Based on Deep Learning Convolution Neural Network: A Review


  • Sharmeen M. Saleem Abdullah Duhok polytechnic university
  • Adnan Mohsin Abdulazeez Duhok Polytechnic University


Facial expression recognition, deep learning, convolutional neural networks, feature extraction


Facial emotional processing is one of the most important activities in effective calculations, engagement with people and computers, machine vision, video game testing, and consumer research. Facial expressions are a form of nonverbal communication, as they reveal a person's inner feelings and emotions. Extensive attention to Facial Expression Recognition (FER) has recently been received as facial expressions are considered. As the fastest communication medium of any kind of information. Facial expression recognition gives a better understanding of a person's thoughts or views and analyzes them with the currently trending deep learning methods. Accuracy rate sharply compared to traditional state-of-the-art systems. This article provides a brief overview of the different FER fields of application and publicly accessible databases used in FER and studies the latest and current reviews in FER using Convolution Neural Network (CNN) algorithms. Finally, it is observed that everyone reached good results, especially in terms of accuracy, with different rates, and using different data sets, which impacts the results.




How to Cite

M. Saleem Abdullah, S., & Abdulazeez, A. M. . (2021). Facial Expression Recognition Based on Deep Learning Convolution Neural Network: A Review. Journal of Soft Computing and Data Mining, 2(1), 53–65. Retrieved from https://publisher.uthm.edu.my/ojs/index.php/jscdm/article/view/7906