Active Shape Model with random forest for facial features detection

Efficiency of facial feature detection is very crucial in face related applications such as face recognition and reconstruction. Traditional algorithms of high precision are often with expensive computation. In this paper, we propose a fast facial feature detection algorithm with good precision. The basic idea is to combine fast search strategy in the global image and high precision classifier in the local regions. In a global image, we borrow the idea of Active Shape Model (ASM) and utilize the average search template to decrease the search area in images. At each local region, we use trained random forest classifier (RF) to identify the existence of facial feature. An iteration procedure of template adjustment is specially designed to ensure the detection precision. Experiments show the effectiveness of the proposed facial feature detection algorithm.