Happy-Sad Expression Recognition Using Emotion Geometry Feature and Support Vector Machine

Abstract

Currently human-computer interaction, especially emotional interaction, still lacks intuition. In health care, it is very important for the medical robot, who assumes the responsibility of taking care of patients, to understand the patient's feeling, such as happiness and sadness. We propose an approach to facial expression recognition for estimating patients' emotion. Two expressions (happiness and sadness) are classified in this paper. Our method uses a novel geometric feature parameter, which we call the Emotion Geometry Feature (EGF). The active shape model (ASM), which can be categorized mainly for non-rigid shapes, is used to locate Emotion Geometry Feature (EGF) points. Meanwhile, the Support Vector Machine (SVM) is used to do classification. Our method was tested on a Japanese Female Facial Expression (JAFFE) database. Experimental results, with the average recognition rate of 97.3%, show the efficiency of our method.