Dynamic Emotion Understanding in Human–Robot Interaction Based on Two-Layer Fuzzy SVR-TS Model

Abstract

Two-layer fuzzy support vector regression-Takagi–Sugeno (TLFSVR-TS) model is proposed for emotion understanding in human–robot interaction (HRI), where the real-time dynamic emotion is recognized according to facial expression, and emotional intention understanding is obtained mainly based on human emotions and identification information. It aims to make robots capable of recognizing and understanding human emotions, in such a way that make HRI run smoothly. TLFSVR-TS considers about the priori knowledge inferred from human personal preference to reduce the uncertainty of various people, and multiple support vector regression (SVR) corresponding to different genders/provinces/ages of human to guarantee the local learning ability. Preliminary application experiments are performed in the developing emotional social robot system, where 30 volunteers experience the scenario of “drinking in the bar.” Results show that the proposal receives higher understanding accuracy than that of TLFSVR, kernel fuzzy ${c}$ -means clustering is fused with SVR, and SVR.