Human is able to distinguish a live face and a photograph without any effort, since human can very easily recognize many physiological clues of liveness, for example, facial expression variation, mouth movement, head rotation, eye change. However the tasks of computing these clues are often complicated for computer, even impossible for some clues under the unconstrained environment. This study describes, implements and evaluates a novel methodology for anti-spoof from measurements of the cardiac pulse rate extracted from video recorded by a basic webcam. By applying independent component analysis (ICA) on the color channels in video recordings, we extracted the blood volume pulse (BVP) from the facial regions. The component whose power spectrum contained the highest peak was then selected for further analysis. This study develops a methodology to perform the BVP peaks in the waveform signal from liveness (positive samples) and spoofs (negative samples). This proposed methodology may have a great value in monitoring person after adequate enhancements are introduced.