We estimate the velocity field of the blood flow in a human face from videos. Our approach first performs spatial preprocessing to improve the signal-to-noise ratio (SNR) and the computational efficiency. The discrete Fourier transform (DFT) and a temporal band-pass filter are then applied to extract the frequency corresponding to the subject's heart rate. We propose two techniques for reducing the noise from the resulting phase and amplitude maps. The 2D blood flow field is then estimated from the relative phase shift between the pixels. We evaluate our approach on real and synthetic face videos using two different metrics. Our method produces a velocity field with an angular error of 20° and an error in magnitude of 29% on the average.