Blind source separation technique based on independent component analysis (ICA) can separate blood volume pulse (BVP) from the facial video and then realize the telemetry of heart rate, blood oxygen saturation, respiratory rate and other vital signs parameters. However, the superiority of ICA in BVP extraction has not been demonstrated in the existing researches. Some researchers suggested using traditional G-channel method for BVP extraction (G-BVP) instead of ICA method (ICA-BVP). This study investigated the applicability of ICA-BVP comparatively. To solve the inherent permutation problem of ICA, a spectral kurtosis-based method was proposed for BVP identification. The experimental results based on the facial video datasets from 9 subjects shows that ICA-BVP method has apparent advantages in motion artifacts attenuation and ambient light changes elimination. The kurtosis-based method achieved a good performance in BVP identification and dynamic heart rate (HR) estimation. In practical application, the proposed ICA-BVP method could present a better stability and accuracy in vital signs parameters extraction.