Deep convolutional generative adversarial networks for data imbalance in convolutional neural networks for facial expression classification

Main Article Content

Sugiarto Surono
Choo Wou Onn
Almuzhidul Mujhid
Goh Khang Wen

Abstract

Facial expression recognition technology is a critical direction of emotion computing research, and it is an essential part of human-computer interaction. The facial expression recognition method is a classification method. An excellent classification method and widely used today are the Convolutional Neural Network (CNN). However, there are still shortcomings in accuracy in the CNN method if the available dataset is minimal and imbalanced. There are two ways to overcome this, adding the training data or changing the architecture on CNN. In this research, the researcher uses the method to add to the training dataset using the Deep Convolutional Generative Adversarial Networks (DCGAN) method.

Article Details

How to Cite
Surono, S., Onn, C. W., Mujhid, A., & Wen, G. K. (2023). Deep convolutional generative adversarial networks for data imbalance in convolutional neural networks for facial expression classification. Journal of Numerical Optimization and Technology Management, 1(1), 1-8. Retrieved from https://shmpublisher.com/index.php/jnotm/article/view/214
Section
Articles
Abstract viewed = 43 times