A Model for Generating Facial Expressions Using Virtual Emotion Based on Simple Recurrent Network

Abstract

We propose an interactive facial expression model using the Simple Recurrent Network (SRN) for achieving interactions through facial expressions between robots and human beings. The proposed model counts humans in the root system as receivers of facial expressions to achieve a dynamic system bi-directionally affecting humans and robots. Robots typically generate only static changes in facial expression using motion files, so they seem bored, unnatural, and strange to their users. We use interactions between robots and people to diversity the inputs of robots and use emotional state transitions of robots to reduce uniformities in output facial expressions. This paper discusses a dynamic system that causes the proposed model to learn emotional facial expressions based on those of humans. Next, we regard internal states generated by the proposed model as virtual emotions and show that mixed emotions can be expressed by users’ inputs from the virtual emotional space. Moreover, based on the results of a questionnaire, we see that facial expressions adopted in the virtual emotional space of the proposed model received high rates of approval from the users.