Citation

Madokoro H, Sato K. J. multimed. 2012; 7(4).

Copyright

(Copyright © 2012, Academy Publisher)

DOI

10.4304/jmm.7.4.314-324

PMID

unavailable

Abstract

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. For facial images with emphasized sparse representations using Gabor wavelet filters, SOMs extract topological information in facial expression images and classify them as categories in the fixed space that are decided by the number of units on the mapping layer. Subsequently, Fuzzy ART integrates categories classified by SOMs using adaptive learning functions under fixed granularity that is controlled by the vigilance parameter. The categories integrated by Fuzzy ART are matched to Expression Levels (ELs) for quantifying facial expression intensity based on the arrangement of facial expressions on Russell's circumplex model. We designate the category that contains neutral facial expression as the basis category. Actually, FESCs can visualize and represent dynamic diversity of facial expressions consisting of ELs extracted from facial expressions. 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 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 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%.