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。
We segment the face image into several "Windows" according to the prOportion relationship of organs and detect the feature points in each window.
根据人的面部器官所遵循的比例关系，将人脸划分为若干个窗口，在窗口内对面部特征点进行检测。
Then, the classification abilities of different local positions in the face were evaluated, and feature points were chosen at the positions with the strongest discriminating power.
然后对人脸不同局部位置处采样点的分类能力进行评价，选择分类能力最强的位置提取特征点。
A database involving 50 images of human face is built up by this system. Head outline and feature points of human face can be effectively achieved by this method, as shown by experimental results.
本系统建了一个包含50个人脸图象的数据库，实验结果表明这种方法可以有效地获取头部轮廓和人脸特征点。
This approach would help to extract the vital feature points on human face automatically and improve the accuracy of face recognition.
眼角的自动定位能够给后续的人脸特征自动提取和识别算法研究奠定良好的基础，帮助提高人脸识别算法的识别率。
Facial feature points localization takes an important role in the face recognition, facial expression analysis, cartoon face synthesis, etc.
人脸特征点的定位在人脸识别、人脸表情分析以及卡通人脸生成等方面具有非常重要的作用。
It discusses the method of detecting these points, the expression-independent vectorization of human face and also the arrangement and optimization of the facial feature database.
文章给出了这些特征点的提取方法，对与表情变化无关的人脸的矢量化方法进行了研究，并对人脸特征数据库设计和优化进行了探讨。
Face tracking using KLT algorithm can reduce the impact caused by the tilt of the head, only the first detection of the human eye, after all detected feature points, computing speed.
人脸跟踪使用KLT算法。能够减少人头倾斜造成的影响，只有第一次检测人眼，以后都是检测特征点，运算速度快。- Use vision。
Second, global face features with salient features were employed to constrain the movement of feature points;
然后采取全局特征与局部特征相结合的方法来共同实现对特征点的定位；
We realize an algorithm based on minimum features for rapid face modeling from video, by tracking feature points, calibrating exterior parameter, estimating 3D location of feature points.
通过跟踪视频中的特征点，标定相机外参，进而估计特征点的3D位置，实现了基于一段视频中小特征点集的人脸建模算法。
A two-step matching method for 3d face recognition is proposed. Feature points are detected based on curvature and geometric constraint.
针对3维人脸识别问题，提出一种由粗到细的两步识别方法。
Then the symmetrical plane of 3D face is determined based on the feature points, and the profile is determined by the obtained symmetrical plane.
首先结合几何约束与曲率信息定位特征点，根据特征点确定人脸对称面，提取人脸侧面轮廓线。
In the part of "3d face registration", feature points based pie-registration method is implemented with the help of feature points location method.
三维人脸配准方面，通过和标志点定位结合，实现了基于标志点的粗配准方法。
In the part of "3d face registration", feature points based pie-registration method is implemented with the help of feature points location method.
三维人脸配准方面，通过和标志点定位结合，实现了基于标志点的粗配准方法。
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