Performance analysis of classifiers for facial expression recognition under constrained settings

This paper deals with the identification of the fast and accurate classifier for facial expression recognition under constrained setting image acquisition situations. Four different machine learning techniques namely Partial Least Squares (PLS), K-Nearest Neighbor (KNN), Support Vector Machine (SVM) and Extreme Learning Machine (ELM) are experimented in this paper and the results are evaluated with respect to time and accuracy. From the results, it's concluded that ELM is both fast and accurate during the classification of the six categories of emotions namely Happy, Sad, Angry, Surprise, Disgust, Fear and Neutral.