Heart rate variability (HRV) can be an important indicator of several conditions that affect the autonomic nervous system, including traumatic brain injury, post-traumatic stress disorder and peripheral neuropathy. Recent work has shown that some of the HRV features can potentially be used for distinguishing a subject’s normal mental state from a stressed one. In all of these past works, HRV analysis is performed on the cardiac activity data acquired by conventional electrocardiography electrodes, which may introduce additional stress and complexity to the acquired data. In this paper we use remotely acquired time-series data extracted from the human facial skin reflectivity signal during rest and mental stress conditions to compute HRV driven features. We further apply a set of classification algorithms to distinguishing between these two states. To determine heart beat signal from the facial skin reflectivity, we apply Principal Component Analysis (PCA) for denoising and Independent Component Analysis (ICA) for source selection. To determine the signal peaks to extract the RR-interval time-series, we apply a threshold-based detection technique and additional peak conditioning algorithms. To classify RR-intervals, we explored classification algorithms that are commonly used for medical applications such as logistic regression and linear discriminant analysis (LDA). Goodness of each classifier is measured in terms of sensitivity/specificity. Results from each classifier are then compared to find the optimal classifier for stress detection. This work, performed under an IRB approved protocol, provides initial proof that remotely-acquired heart rate signal can be used for stress detection. This result shows promise for further development of a remote-sensing stress detection technique both for medical and deception-detection applications.