The Effect of Classification Methods on Facial Emotion Recognition Accuracy
Suhaila N. Mohammed *
Department of Computer Science, College of Science, Baghdad University, Baghdad, Iraq
Loay E. George
Department of Computer Science, College of Science, Baghdad University, Baghdad, Iraq
Hayder A. Dawood
Department of Computer Science, University of Technology, Iraq
*Author to whom correspondence should be addressed.
Abstract
The interests toward developing accurate automatic face emotion recognition methodologies are growing vastly, and it is still one of an ever growing research field in the region of computer vision, artificial intelligent and automation. However, there is a challenge to build an automated system which equals human ability to recognize facial emotion because of the lack of an effective facial feature descriptor and the difficulty of choosing proper classification method. In this paper, a geometric based feature vector has been proposed. For the classification purpose, three different types of classification methods are tested: statistical, artificial neural network (NN) and Support Vector Machine (SVM). A modified K-Means clustering algorithm has been developed for clustering purpose. Mainly, the purpose of using modified K-means clustering technique is to group the similar features into (K) templates in order to simulate the differences in the ways that human express each emotion. To evaluate the proposed system, a subset from Cohen-Kanade (CK) dataset have been used, it consists of 870 facial images samples for the seven basic emotions (angry, disgust, fear, happy, normal, sad, and surprise). The conducted test results indicated that SVM classifier can lead to higher performance in comparison with the results of other proposed methods due to its desirable characteristics (such as large-margin separation, good generalization performance, etc.).
Keywords: Facial emotions, feature selection, data clustering, modified K-Means clustering algorithm, LDA algorithm, Statistical classifier, Neural Network, Support Vector Machine (SVM)