In this paper we discuss the problem of human facial emotions and emotion intensity levels recognition using Active Appearance Models (AAM) and Support Vector Machines (SVM). AAM are used for appropriate feature extraction and SVM for convenient facial emotion and emotion level classification. Problems related to proper selection of data retrieved from AAM and SVM learning parameters settings are discussed too. Furthermore, we propose analysis of specially designed psychological experiment which led to alternative classifier evaluation methodology that uses the human visual system as a reference point. Finally, we analyze classification characteristics of proposed AAM-SVM classifier comparing to humans and show that our classifier give slightly more consistent labels to emotion categories than human subjects, while humans were more consistent at identifying emotion intensity level than SVM.
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