Computational Intelligence Methods for Facial Emotion Recognition: A Comparative Study

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  • Fatemeh Shahrabi Farahani1,
  • Mansour Sheikhan*2,
  1. Department of Mechatronic Engineering, Islamic Azad University, South Tehran Branch
  2. Electrical Engineering Department, South Tehran Branch, Islamic Azad University, Tehran, Iran.

Revised: 2018-04-08

Accepted: 2018-06-04

Published in Issue 2018-06-01

How to Cite

Shahrabi Farahani, F., & Sheikhan, M. (2018). Computational Intelligence Methods for Facial Emotion Recognition: A Comparative Study. Signal Processing and Renewable Energy (SPRE), 2(2), 1-13. https://oiccpress.com/spre/article/view/7768

PDF views: 148

Abstract

Emotion recognition plays a critical role in the human communications. It is one of the major ways to be in touch with others. Four parameters including eye opening size, mouth opening size, ratio of eye opening size to eye width and mouth width are used as a reduced-size feature set in this study. This paper compares the performance of facial emotion recognition classification models based on the following computational intelligence methods: fuzzy logic, chaotic gravitational search algorithm (CGSA), and artificial neural network (ANN) from eyes and mouth features tested on the FACES database. Experimental results show the superior performance of ANN-based method compared to fuzzy- and CGSA-based methods. In addition, this comparative study triggers the idea of a hybrid system based on these computational methods that outperforms the human detection system.
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