Deep Learning based Framework for Emotion Recognition using Facial Expression

  • Nimra Bukhari Department of Computer Science, National College of Business Administration and Economics, Rahim Yar khan, 64200, Pakistan
  • Shabir Hussain School of Information Engineering, Zhengzhou University, Zhengzhou, China
  • Muhammad Ayoub School of Computer Science and Engineering, Central South University, Hunan, China
  • Yang Yu Distributed Systems Group, University of Duisburg-Essen, Duisburg 47050, Germany
  • Akmal Khan Department of Data Science, The Islamia University of Bahawalpur, Bahawalpur 63100, Pakistan
DOI: https://doi.org/10.51846/vol5iss3pp51-57
Keywords: Deep Learning, transfer Learning CNN, ResNet-50, VGG-16, Inception-V3

Abstract

Human convey their message in different forms. Expressing their emotions and moods through their facial expression is one of them. In this work, to avoid the traditional process of feature extraction (Geometry based method, Template based method, and Appearance based method), CNN model is used as a feature extractor for emotion detection using facial expression. In this study we also used three pre-trained models VGG-16, ResNet-50, Inception-V3. This Experiment is done on Fer-2013 facial expression dataset and Cohn Extended (CK+) dataset. By using FER-2013 dataset the accuracy rates for CNN, ResNet-50, VGG-16 and Inception-V3 are 76.74%, 85.71%, 85.78%s, 97.93% respectively. Similarly, the experimental results using CK+ dataset showed the accuracy rates for CNN, ResNet- 50, VGG-16 and Inception-V3 are 84.18%, 92.91%, 91.07%, and 73.16% respectively. The experimental results showed exceptional results for Inception-V3 with 97.93% using FER-2013 dataset and ResNet-50 with 91.92% using CK+ dataset.

Published
2022-11-17
How to Cite
[1]
N. Bukhari, S. Hussain, M. Ayoub, Y. Yu, and A. Khan, “Deep Learning based Framework for Emotion Recognition using Facial Expression”, PakJET, vol. 5, no. 3, pp. 51-57, Nov. 2022.
Issue
Vol 5 No 3 (2022): Pakistan Journal of Engineering and Technology
Section
Research Articles

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