StressNet: detecting stress in thermal videos
Precise measurement of physiological signals is critical for the effective monitoring of human vital signs. Recent developments in computer vision have demonstrated that signals such as pulse rate and respiration rate can be extracted from digital video of humans, increasing the possibility of contact-less monitoring. This paper presents a novel approach to obtaining physiological signals and classifying stress states from thermal video. The proposed net-work" StressNet", features a hybrid emission representation model that models the direct emission and absorption of heat by the skin and underlying blood vessels. This results in an information-rich feature representation of the face, which is used by spatio-temporal networks for recon-structing the ISTI (Initial Systolic Time Interval: a measure of change in cardiac sympathetic activity that is considered to be a quantitative index of stress in humans). The recon-structed ISTI signal is fed to a stress-detection model to detect and classify the individual's stress state (ie stress or no stress). A detailed evaluation demonstrates that Stress-Net achieves a mean square error of 5.845 ms for predicting the ISTI signal and an average precision of 0.842 for stress detection.