A robust and efficient face tracking kernel for Driver Inattention Monitoring System

For the application of Driver Inattention Monitoring System this paper propose a zero-order binocular face tracking kernel and an efficient face shape registration approach. This kernel tracks such parameters as the face position & orientation, the eyeball yaw & pitch, the eyelids animation and the mouth & jaw animation. From the view of computational cost, accuracy and robustness against registration error, camera calibration error and measurement noise, the proposed zero-order binocular face tracking kernel has shown robustness and efficiency.