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All Journal Indonesian Journal of Electrical Engineering and Computer Science
Mayyadah R. Mahmood
University of Zakho

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Classification techniques’ performance evaluation for facial expression recognition Mayyadah R. Mahmood; Maiwan B. Abdulrazaq; Subhi R. M. Zeebaree; Abbas Kh. Ibrahim; Rizgar Ramadhan Zebari; Hivi Ismat Dino
Indonesian Journal of Electrical Engineering and Computer Science Vol 21, No 2: February 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v21.i2.pp1176-1184

Abstract

Facial exprestion recognition as a recently developed method in computer vision is founded upon the idea of analazing the facial changes in which are witnessed due to emotional impacts on an individual. This paper provides a performance evaluation of a set of supervised classifiers used for facial expression recognition based on minimum features selected by chi-square. These features are the most iconic and influential ones that have tangible value for result dermination. The highest ranked six features are applied on six classifiers including multi-layer preceptron, support vector machine, decision tree, random forest, radial baised function, and k-nearest neioughbor to figure out the most accurate one when the minum number of features are utilized. This is done via analyzing and appraising the classifiers’ performance. CK+ is used as the research’s dataset. Random forest with the total accuracy ratio of 94.23 % is illustrated as the most accurate classifier amongst the rest. 
Co-Authors Abbas Kh. Ibrahim Hivi Ismat Dino Maiwan B. Abdulrazaq Rizgar Ramadhan Zebari Subhi R. M. Zeebaree