Facial expression spatial charts for representing of dynamic diversity of facial expressions

This paper presents a method to generate individual Facial Expression Spatial Charts (FESC) using Self-Organizing Maps (SOM) and Fuzzy Adaptive Resonance Theory (ART) networks. We specifically examine the dynamic diversity of facial expressions in time-series facial images after conversion using Gabor wavelet filters. The proposed method consists of three steps: the first step is to extract topological features from time-series facial image datasets using SOMs; the second step is to integrate weights of SOM into categories using Fuzzy ART networks; the third step is to create FESCs integrated by all arousal levels produced from categories of facial expressions in each basic facial expression. For considering the influence that stress gives an expression, we measured the psychological emphasis that a subject feels at that time. The result shows a negative correlation for psychological stress and the expanse of FESC, which means that the expression became poor during feelings of stress.