Abstract
This paper presents Facial Expression Spatial Charts (FESCs) as a new framework to describe individual facial expression spaces, particularly addressing the dynamic diversity of facial expressions that appear as an exclamation or emotion. The FESCs are created using Self-Organizing Maps (SOMs) and Fuzzy Adaptive resonance Theory (ART) of unsupervised neural networks. In the experiment, we created an original facial expression dataset consisting of three facial expressions-happiness, anger, and sadness-obtained from 10 subjects during 7-20 weeks at one-week intervals. Results of creating FESCs in each subject show that the method can adequately display the dynamic diversity of facial expressions between subjects. Moreover, we used stress measurement sheets to obtain temporal changes of stress for analyzing psychological effects of the stress that subjects feel. We estimated stress levels of four grades using Support Vector Machines (SVMs). The mean estimation rates for all 10 subjects and for 5 subjects over more than 10 weeks were, respectively, 68.6 and 77.4%.
