自动化学报2012，Vol.38Issue(5)：788-796,9.DOI:10.3724/SP.J.1004.2012.00788
一种鲁棒高效的人脸特征点跟踪方法
A Robust and Efficient Facial Feature Tracking Algorithm
摘要
Abstract
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.关键词
人脸特征点跟踪/主动表象模型/姿态估计/自适应更新Key words
Facial feature tracking, active appearance model (AAM), pose estimation, adaptive updating引用本文复制引用
黄琛,丁晓青,方驰..一种鲁棒高效的人脸特征点跟踪方法[J].自动化学报,2012,38(5):788-796,9.基金项目
国家高技术研究发展计划(863计划)(2009AA11Z214),国家自然科学基金(60972094)资助 （863计划）
