AAM人脸特征点定位模块是关键模块，其定位结果直接影响人脸表情动画合成效果。
AAM facial feature points positioning module is the master key for its orientation result directly influence the synthetic effect of the facial expression animation.
一般来说，人脸识别系统包括人脸检测、特征点定位、图像预处理、人脸特征提取以及人脸识别。
Generally speaking, the face recognition system consists of face detection. feature piont location, image pre-processing, feature extraction and face recognition.
然而，用现有的人脸特征点定位算法进行人脸形状估计时，嘴巴区域特征点的定位误差相对较大。
However, when estimating facial shapes using current facial landmarks detecting methods, the locating error of feature points around the mouth region is relatively large.
本文首先介绍了AAM人脸特征点定位的基本原理，然后针对传统AAM算法存在的不足进行了一些改进。
This paper first introduced the AAM theory, and then made some improvement based on the shortcomings of the traditional AAM algorithm.
系统主要可分为三大模块：AAM人脸特征点定位模块、FAP转换模块、MPEG-4模型动画实现模块。
System mainly divided into three modules:AAM facial feature points positioning module, FAP conversion module, and MPEG-4 model animation module.
人脸面部的关键特征点定位既是人脸识别研究领域中的一个关键问题，也是计算机视觉和图形学领域的一个基本问题。
Facial feature point location is one of the fundamental and crucial problems in the field of facial recognition, computer vision and graphics.
人脸特征点定位就是对人脸的形状和人脸局部特征(如眉毛、眼睛、鼻子和嘴巴等)的位置、关键点或轮廓线进行描述。
It can provide the description for face shape and the information of the local features, such as eyebrows, eyes, nose, mouth and so on.
在人脸检测的基础上，面部关键的特征点定位试图定位人脸面部主要特征点的位置以及眼睛和嘴巴等主要器官的形状信息。
It aims to locate the facial feature points and the shape information of eyes, mouth and so on based on the facial detect.
本文采用该算法训练人脸面部特征检测器，提取区域的几何中心进行面部特征点定位，获取以双眼和嘴巴为特征顶点的人脸特征三角形。
The thesis uses the method to train face feature classifier, to get the face feature points and build face feature triangle that uses eyes and mouth as vertexes.
本文采用该算法训练人脸面部特征检测器，提取区域的几何中心进行面部特征点定位，获取以双眼和嘴巴为特征顶点的人脸特征三角形。
The thesis uses the method to train face feature classifier, to get the face feature points and build face feature triangle that uses eyes and mouth as vertexes.
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