A supervised machine learning approach to remote video-based heart rate (HR) estimation is proposed. We demonstrate the possibility of training a discriminative statistical model to estimate the Blood Volume Pulse signal (BVP) from the human face using ambient light and any off-the-shelf webcam. The proposed algorithm is 120 times faster than state of the art approach and returns a confidence metric to evaluate the HR estimates plausibility. The algorithm was evaluated against the state-of-the-art on 120 minutes of face videos, the largest video-based heart rate evaluation to date. The evaluation results showed a 53% decrease in the Root Mean Squared Error (RMSE) compared to state-of-the-art.