Interaction Experiment by Communication Robot Using Markovian Emotional Model Based on Facial Expression Recognition

Abstract

In this paper, a method that applies the difference of feature amount in human facial expression learned by SOM to the emotion transition in Markovian Emotional Model and generates an emotion reaction model of a communication robot is proposed. For example, it is thought that what an angry person bursts into tears than laughter occurs with high possibility when the emotion of person changes. The expression getting angry is more likely to resemble a crying expression than a laughing expression at that time. In other words, the emotion which the characteristic of facial expression resembles is easy to change. The emotion transition probability is obtained by a questionnaire, and an interaction experiment using a communication robot is performed in this study.