Discrimination of stress coping responses on dimensionality-reduced facial thermal image space

This study investigates the use of facial skin temperature, measured through non-invasive facial thermal imaging, to classify stress-coping responses. While previous methods like Convolutional Neural Networks (CNN) and sparse coding have shown promise, capturing continuous changes in stress-coping states remains challenging. To address this limitation, we focus on t-SNE for dimensionality reduction, which compresses high-dimensional facial thermal data while preserving both local and global structure. Our findings show that facial thermal images from the same stress-coping response cluster together in the reduced space, allowing continuous monitoring of facial skin temperature changes. Additionally, the behavior of the data in the reduced space revealed a time lag between hemodynamic parameter variations and facial skin temperature distribution changes. These insights contribute to developing models that can continuously track stress-coping state changes.