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In this paper, we present a novel framework for quantifying physiological stress at a distance via thermal imaging. The method captures stress-induced neurophysiological responses on the perinasal area that manifest as transient perspiration. We have developed two algorithms to extract the perspiratory signals from the thermophysiological imagery. One is based on morphology and is computationally efficient, while the other is based on spatial isotropic wavelets and is flexible; both require the support of a reliable facial tracker. We validated the two algorithms against the clinical standard in a controlled lab experiment where orienting responses were invoked on n=18 subjects via auditory stimuli. Then, we used the validated algorithms to quantify stress of surgeons (n=24) as they were performing suturing drills during inanimate laparoscopic training. This is a field application where the new methodology shines. It allows nonobtrusive monitoring of individuals who are naturally challenged with a task that is localized in space and requires directional attention. Both algorithms associate high stress levels with novice surgeons, while low stress levels are associated with experienced surgeons, raising the possibility for an affective measure (stress) to assist in efficacy determination. It is a clear indication of the methodology's promise and potential.