I show how remote photoplethysmography (rPPG), which are blood flow induced intensity variations in the skin observed with a camera, can improve driver monitoring and face liveness verifcation. A leading cause of car accidents is driver distraction. These accidents could be prevented by monitoring drivers rPPG signals while driving. However, it is challenging to measure rPPG signals in a moving vehicle due to drastic illumination variations and large motion. I built a narrow-band near-infrared set up to reduce outside illumination variations and I developed an algorithm called SparsePPG to exploit spatial low rankness and sparsity in frequency of rPPG signals. Face recognition algorithms can provide highly secure user authentication due to their high accuracy; however, they cannot distinguish between authentic faces and face attacks, such as photographs. I developed an algorithm called PPGSecure which uses rPPG signals from a face video recording and machine learning to detect these face attacks.