Abstract—Although there have been many breakthroughs in the use of convolutional neural networks (CNN) for image classification, facial expression recognition (FER) in real-life is still a challenge in this research area. This paper proposes a method to leverage state-of-the-art multi-deep CNN encoders with support vector machines (SVM) to classify facial expression. We conducted experiments to show that combining features from multi-deep CNN is better than using a single deep CNN model. As well as combining multiple CNN models, we show that using rules to remove noise images from the training dataset improves the performance of the FER system. The FER2013 dataset was used to evaluate the proposed approach, which achieved 73.78% accuracy.
Index Terms—Convolutional neural networks, deep convolutional neural network features, facial expression recognition in the wild, FER2013.
Tran Ngoc Dong is with Information Science Faculty, Univeristy of Information Technology, Vietnam National University, Hochiminh City, Vietnam (e-mail: firstname.lastname@example.org).
Le Van and Pham The Bao is with Information Science Faculty, Sai Gon University, Hochiminh City, Vietnam (Corresponding author: Pham The Bao; e-mail: email@example.com, firstname.lastname@example.org).
Cite: Tran Ngoc Dong, Le Van, and Pham The Bao, "Facial Expression Recognition Using Multi-deep Convolutional Neural Network Encoders with Support Vector Machines," International Journal of Machine Learning and Computing vol. 11, no. 5, pp. 345-349, 2021.Copyright © 2021 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).