We introduce an integrated framework for detecting peripheral sympathetic responses through purely imaging means. The measurements are performed on three facial areas of sympathetic importance, that is, periorbital, supraorbital, and maxillary. To the best of our knowledge, this is the first time that the sympathetic importance of the maxillary area is documented. Because the imaging measurements are thermal in nature and are composed of multiple components of variable frequency (i.e., blood flow, sweat gland activation, and breathing), we chose wavelets as the signal analysis framework. We also propose a novel method that localizes the periorbital tissue on thermal infrared imagery. Specifically, the method consistently segments the tissue that is over the facial and ophthalmic arterial-venous complexes, which supply blood to the ocular muscle. This tissue area is used to extract a mean thermal signal over time (periorbital signal), which is a correlate of blood flow. However, due to imperfections in tissue tracking and segmentation, the measurements from all three areas carry substantial noise, which we suppress to a large degree by our novel noise cleaning approach. The image analysis of these three facial areas is grounded on Galvanized Skin Response (GSR) signals, which are still considered the gold standard in peripheral neurophysiological and psychophysiological studies. Our experimental results show that monitoring of the facial channels yields detecting power similar to those of GSR. This work opens a new research area that leads to unobtrusive screening technologies in neurophysiology and psychophysiology. The goal of this effort is to provide a complete set of stress measurements that are highly automated and can be performed at a distance. Rigorous and ubiquitous quantification of stress is expected to have an impact beyond lie detection and may revolutionize psychological practice.