Classification techniques’ performance evaluation for facial expression recognition

Mayyadah R. Mahmood (University of Zakho)
Maiwan B. Abdulrazaq (University of Zakho)
Subhi R. M. Zeebaree (Duhok Polytechnic University)
Abbas Kh. Ibrahim (University of Zakho)
Rizgar Ramadhan Zebari (Duhok Polytechinic University)
Hivi Ismat Dino (University of Zakho)

Indonesian Journal of Electrical Engineering and Computer Science
Vol 21, No 2: February 2021

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.