| Title |
Design of Hybrid Unsupervised-Supervised Classifier for Automatic Emotion Recognition |
| Authors |
이지은(Lee, JeeEun) ; 유선국(Yoo, Sun K.) |
| DOI |
https://doi.org/10.5370/KIEE.2014.63.9.1294 |
| Keywords |
Emotion ; Physiological Signal ; K-means ; Genetic Algorithm ; SVM |
| Abstract |
The emotion is deeply affected by human behavior and cognitive process, so it is important to do research about the emotion. However, the emotion is ambiguous to clarify because of different ways of life pattern depending on each individual characteristics. To solve this problem, we use not only physiological signal for objective analysis but also hybrid unsupervised-supervised learning classifier for automatic emotion detection. The hybrid emotion classifier is composed of K-means, genetic algorithm and support vector machine. We acquire four different kinds of physiological signal including electroencephalography(EEG), electrocardiography(ECG), galvanic skin response(GSR) and skin temperature(SKT) as well as we use 15 features extracted to be used for hybrid emotion classifier. As a result, hybrid emotion classifier(80.6%) shows better performance than SVM(31.3%). |