Discrimination of Stress Coping Responses on Facial Thermal Images Using Dimensional Reduction

When humans experience stress, they exhibit a physiological coping responses, which is typically accompanied by an increase in blood pressure. This responses can be classified into three categories based on hemodynamic indices (mean blood pressure, cardiac output, and total peripheral vascular resistance) as either active coping responses, passive coping responses, or no stress coping. Conventional methods for labeling facial thermal images according to changes in hemodynamic indices often do not account for the delayed responses between skin temperature changes and hemodynamic activity. In this study, we employed t-SNE, a dimensionality reduction technique, to map high-dimensional facial thermal image data into a low-dimensional space. This approach enabled the simultaneous assessment of differences between facial thermal images and hemodynamic indices, classification of facial thermal images according to stress coping responses categories, and development of a stress coping responses discrimination model. The results indicate a temporal delay between facial thermal responses and hemodynamic indices, with the discriminant model achieving an accuracy of 81.3%, which surpasses the accuracy of conventional methods.