The number of deaths due to the motor vehicle accidents has increased drastically during the last few years. It is estimated that nearly 40–50 % of the accidents are due to the distractions of the driver. One of the methods to assess driver distraction is using head pose estimation. The unavailability of a standard driving database hampers the study and testing of driver distraction algorithms. In this paper we present the development and bench-marking of the Distracted Car Driver (DCD) database. The database contains real time driving videos of 12 different individuals, in varying environmental conditions. To bench-mark the database we use standard linear and nonlinear manifold techniques for data embedding.