A Robust and Efficient Facial Feature Tracking Algorithm
一种鲁棒高效的人脸特征点跟踪方法

Facial feature tracking obtains precise information of facial components in addition to the coarse face position and moving track, and is important to computer vision. The active appearance model (AAM) is an efficient method to describe the facial features. However, it suffers from the sensitivity to initial parameters and may easily be stuck in local minima due to the gradient-descent optimization, which makes the AAM based tracker unstable in the presence of large pose, illumination and expression changes. In the framework of multi-view AAM, a real time pose estimation algorithm is proposed by combining random forest and linear discriminate analysis (LDA) to estimate and update the head pose during tracking. To improve the robustness to variations in illumination and expression, a modified online appearance model (OAM) is proposed to evaluate the goodness of AAM fitting, then the appearance model of AAM is updated adaptively using the incremental principle component analysis (PCA). The experimental results show that the proposed algorithm has both efficiency and robustness.