Abstract
Emotion recognition plays a vital role in the field of Human-Computer Interaction (HCI). Among the visual human emotional cues, facial expressions are the most commonly used and understandable cues. Different machine learning techniques have been utilized to solve the expression recognition problem; however, their performance is still disputed. In this paper, we investigate the capability of several classification techniques to discriminate between the six universal facial expressions using Speed Up Robust Features (SURF). The evaluation were conducted using the BU-3DFE database with four classifiers, namely, Support Vector machine (SVM), Neural Network (NN), k-Nearest Neighbors (k-NN), and Naive Bayes (NB). Experimental results show that the SVM was successful in discriminating between the six universal facial expressions with an overall recognition accuracy of 79.36%, which is significantly better than the nearest accuracy achieved by Naive Bayes at significance level p < 0.05.
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