Multi-view based face chin contour extraction

Chin contour is an important facial feature to build a 3D morphable model, the core step of which is to establish feature points correspondence between each face in the training set and the reference face. In this paper, robust face detection is implemented firstly using probabilistic method. A probability of detection is obtained for each image of different position and at several scales and poses. Then, the chin contours are extracted accurately using the active shape model (ASM), which depends on the parameters obtained from the face detection. From frontal (0°) to profile (90°) faces that are equally divided into 10 parts, we train 10 flexible models. Then, different flexible models are used to extract the face chin contour according to the corresponding face pose. Experimental results show that the proposed approach can extract the chin contours of different people across different poses with good accuracy.