Heart rate is one of the most important biological features for health information. Most of the state-of-the-art heart rate monitoring systems rely on contact technologies that require physical contact with the user. In this paper, we discuss a proof-of-concept of a non-contact technology based on a single camera to measure the user’s heart rate in real time. The algorithm estimates the heart rate based on facial color changes. The input is a series of video frames with the automatically detected face of the user. A Gaussian pyramid spatial filter is applied to the inputs to obtain a down-sampled high signal-to-noise ratio images. A temporal Fourier transform is applied to the video to get the signal spectrum. Next, a temporal band-pass filter is applied to the transformed signal in the frequency domain to extract the frequency band of heart beats. We then used the dominant frequency in the Fourier domain to find the heart rate. Further, an inverse Fourier transform is used on the spectrum to convert the signal back to the time domain. After scaling, the amplified signal is added to the input image to magnify the subtle facial color change that is caused by the heartbeat. We implement on an iPhone 5s using the front facing camera and obtaining a 1.4 beats per minute error when compared to a standard portable heart rate monitor.