  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 network--"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 network for
reconstructing 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 reconstructed ISTI signal is fed into a
stress-detection model to detect and classify the individual's stress state (
i.e. stress or no stress ). A detailed evaluation demonstrates that StressNet
achieves estimated the ISTI signal with 95% accuracy and detect stress with
average precision of 0.842. The source code is available on Github.
