通过建议一个人脸形状先验模型关注该问题，该模型基于受限Boltzmann Machines (RBM)及其变种构建。特别的，我们首先基于深度信任网络构建一个模型以获取接近正视角的表情变化的人脸形状变量。为了解决姿态变化问题，我们将正面人脸形状先验模型整合到一个3路（3-way）RBM模型，其可以获取正面人脸形状和非正面人脸形状间的关系。最后，我们建议一个方法，将人脸先验模型和人脸特征点的图像度量系统性地组合在一起。-we address this problem

by proposing a face shape prior model that is constructed

based on the Restricted Boltzmann Machines (RBM) and

their variants. Specifically, we first construct a model based

on Deep Belief Networks to capture the face shape variations due to varying facial expressions for near-frontal

view. To handle pose variations, the frontal face shape

prior model is incorporated into a 3-way RBM model that

could capture the relationship between frontal face shapes

and non-frontal face shapes. Finally, we introduce methods to systematically combine the face shape prior models

with image measurements of facial feature points. Experiments on benchmark s show that with the proposed

method, facial feature points can be tracked robustly and

accurately even if faces have significant facial expressions

and poses.