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This paper presents a facial expression recognition based on dimension model of internal states that uses automated feature extraction. We apply this approach mostly for the frontal pose. Features of facial expressions are extracted automatically in three steps. In the first steo, Gabor wavelet representation can provide edges extraction of major face components using the average value of the image's 2D Gabor wavelet coefficient fistogram. In the second step, sparse features of facial expressions are extracted using fuzzy C-means clustering (FCM) algorithm for neural faces, and in the third step, using the dynamic model (DM) for expression images. The result of facial expression recognition is compared with dimensional values of internal states derived from semantic ratings of words related to emotion by experimental subjects. The dimensional model can recognize not only 6 facial expressions related to Ekman's basic emotions, but also expressions of various internal states. A facial expression in the dimension model includes two dimensions which are pleasure-upleasure and arousal-sleep. We show the result of expression recognition in the dimension model. In this paper, with the dimension model we have improved the limitations of expression recognition based on basic emotions, and have extracted features automatically with a new approach using the FCM algorith and the Dynamic Model.
Czasopismo
Rocznik
Tom
Strony
573-585
Opis fizyczny
Bibliogr. 17 poz.
Bibliografia
Typ dokumentu
Bibliografia
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Identyfikator YADDA
bwmeta1.element.baztech-article-BWA1-0001-0886
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