Over the past 5 years several video-based heart rate (HR) estimation methods have been developed. These non-contact methods of HR estimation use video processing techniques to estimate the HR of humans in the scene. This is known as videoplethys-mography (VHR) and has applications to the medical and surveillance fields. In this paper, we review two previous VHR techniques and describe techniques to improve VHR accuracy. These include: (1) targeted skin detection within the facial region, (2) recursive temporal difference filtering and small variation amplification, (3) periodic signal detection within the expected human HR range while considering background periodic signals, and (4) reduction of signal range using a cutoff frequency search. These improvements increased our HR estimate accuracy in two conditions (no-motion, non-random motion) when compared to earlier VHR methods but were not significantly better than those that employ an adaptive frequency analysis.