Detecting living-skin tissue in a video on the basis of induced color changes due to blood pulsation is emerging for automatic region of interest localization in remote photoplethysmography (rPPG). However, the state-of-the-art method performing unsupervised living-skin detection in a video is rather time consuming, which is mainly due to the high complexity of its unsupervised online learning for pulse/noise separation. In this paper, we address this issue by proposing a fast living-skin classification method. Our basic idea is to transform the time-variant rPPG-signals into signal shape descriptors called "multiresolution iterative spectrum," where pulse and noise have different patterns enabling accurate binary classification. The proposed technique is a proof-of-concept that has only been validated in lab conditions but not in real clinical conditions. The benchmark, including synthetic and realistic (nonclinical) experiments, shows that it achieves a high detection accuracy better than the state-of-the-art method, and a high detection speed at hundreds of frames per second in MATLAB, enabling real-time living-skin detection.