Estimation of psychological stress levels using Facial Expression Spatial Charts

摘要

This paper presents a new framework to describe individual facial expression spaces, particularly addressing the dynamic diversity of facial expressions that appear as an exclamation or emotion, to create a unique space for each person. We name this framework Facial Expression Spatial Charts (FESCs). 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, in addition to temporal changes in each subject. Moreover, we used stress measurement sheets to obtain temporal changes of stress in each subject for analyzing psychological effects of the stress that subjects feel. We analyzed relations between numerous individual facial expression patterns and psychological stress values. Results show that facial expressions when influenced by stress differ among subjects. Moreover, we estimated stress levels of four grades using Support Vector Machines (SVMs). The estimation accuracy for all 10 subjects and for 5 subjects over more than 10 weeks were, respectively, 68.6 and 77.4%.