Classification techniques’ performance evaluation for facial expression recognition
Mayyadah Ramiz Mahmood, Maiwan Bahjat Abdulrazaq, Subhi R. M. Zeebaree, Abbas Ibrahim, Rizgar R. Zebari, Hivi Ismat Dino
Abstract
<p><span>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. </span></p>