A Classifier Model based on the Features Quantitative Analysis for Facial Expression Recognition

Amir Jamshidnezhad, Md Jan Nordin


In recent decades computer technology has considerable developed in use of intelligent systems for classification. The development of HCI systems is highly depended on accurate understanding of emotions. However, facial expressions are difficult to classify by a mathematical models because of natural quality. In this paper, quantitative analysis is used in order to find the most effective features movements between the selected facial feature points. Therefore, the features are extracted not only based on the psychological studies, but also based on the quantitative methods to arise the accuracy of recognitions. Also in this model, fuzzy logic and genetic algorithm are used to classify facial expressions. Genetic algorithm is an exclusive attribute of proposed model which is used for tuning membership functions and increasing the accuracy.


Facial expression recognition; fuzzy rule based system; genetic algorithm; classifier

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DOI: http://dx.doi.org/10.18517/ijaseit.1.4.81


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Published by INSIGHT - Indonesian Society for Knowledge and Human Development