Control of human excitement as reactions to a dynamic virtual 3D face

Abstract

This paper introduces how generalized minimum variance control principles are applied to the control of human emotion – excitement signal. We use changing distance-between-eyes in a virtual 3D face as a stimulus - control signal. Human responses to the stimuli were observed using EEG based signal - excitement. We have investigated predictive input-output structure models for exploring dependencies between virtual 3D face features and human reaction to them. A generalized minimum variance control law is synthesized by minimizing quality control criterion in an admissible domain. Admissible domain is composed of control signal boundaries. Sufficiently good control quality of excitement signal (maintained signal level is at average about to 80% higher) is demonstrated by modelling results. We can decrease variations of the control signal using a limited signal variation speed or changing weights coefficient in a generalized minimum variance control