Fast facial landmark detection using cascade classifiers and a simple 3D model

Facial landmark detection is an essential module in many face related applications and it often appears as the most time consuming part in face processing pipeline. This paper proposes a fast and effective method for facial landmark detection using Haar cascade classifiers and a simple 3D head model, which not only detects the position of landmark points but also gives an estimation of head pose such as yaw and pitch angles. To reduce the amount of computation, only 7 landmark points are detected (4 eye corners, 2 mouth corners, 1 nose tip) that generally meets the requirement of face alignment and face recognition. Experiment on multiple datasets shows our algorithm can provide sufficient accuracy of facial landmark localization while being able to run in super real-time at Intel Atom 1.3 GHz embedded processors.