Corresponding Author

Hivi I. Dino

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Document Type

Research Article


Facial expression recognition (FER) has achieved an extreme role in research area since the 1990s. This paper provides a comparison approach for FER based on three feature selection methods which are correlation, gain ration, and information gain for determining the most distinguished features of face images using multi-classification algorithms which are multilayer perceptron, Naïve Bayes, decision tree, and K-nearest neighbor (KNN). These classifiers are used for the mission of expression recognition and for comparing their proportional performance. The main aim of the provided approach is to determine the most effective classifier based on minimum acceptable number of features by analyzing and comparing their performance. The provided approach has been applied on CK+ dataset. The experimental results show that KNN is proven to be better classifier with 91% accuracy using only 30 features


Correlation, Facial expression recognition, Feature selection, Gain ratio, Information gain

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