基于照片的人脸特征点提取。
文中提出一种跟踪人脸特征点的方法。
The extraction of face features is an important part in the process of face automatic recognition.
在研究了人脸的颅骨、肌肉组织特点的基础上，给出了人脸特征点的定义及选取规则。
On the research of human cranium and head muscle, the definition of the facial feature points is given.
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。
Facial feature points localization takes an important role in the face recognition, facial expression analysis, cartoon face synthesis, etc.
针对人脸特征点局部特征描述问题，提出了局部灰度特征与纹理特征相结合的局部特征描述方法。
An improved method is introduced to describe the local property which combining the local gray property with the local texture property.
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。
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.
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.
该方法对人脸特征点集做三角剖分，在纹理图像和三维网格之间建立了一个准确的拓扑同构映射关系，从而得到高真实度的纹理映射。
Applying a D-triangulation on the facial feature points, a precise and topological isomorphic mapping is established between 3d mesh and texture. The texture mapping with high quality can be obtained.
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。
We segment the face image into several "Windows" according to the prOportion relationship of organs and detect the feature points in each window.
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。
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.
人脸部特征点的定位是人脸识别中的关键步骤，定位准确与否直接关系到后续应用的可靠性。
Human facial features positioning is a key stage in face recognition and the accuracy of the positioning directly relates to the reliability of subsequent applications.
基于积分投影的人脸图像特征点的提取方法对人脸进行定位特别精确。
Bonus point projection based on the characteristics of face images from the point of methodology is precised for people face special position.
提出了利用边缘点集的协方差矩阵的特征值与特征矢量作为人脸图像尺度与方向的粗估计方法，从理论和实验上证明了该方法的可行性。
A new method for human face image scale and direction estimation is proposed using covariance matrix eigenvalue and eigenvector of edge set, which is proved by theory and experiments.
针对人脸运动特点，将面部特征点分为具有复杂运动模式和简单运动模式的特征点集。
According to the characteristics of facial motion, the facial feature points are divided into two subsets.
一般的人脸姿态估计方法要通过提取脸部特征点之间的关系或根据已知的三维人脸模型来估测人脸的方向。
A new pose estimation strategy based on seven hair templates has been presented to estimate the orientation of face.
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。
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.
因此，特征点精确定位是人脸应用的关键，是研究的重点。
So, how to detect the features accurately is the key to the face application and the emphasis of our research.
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。
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.
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.
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。
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.
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。
Then the symmetrical plane of 3D face is determined based on the feature points, and the profile is determined by the obtained symmetrical plane.
本文结合人脸结构生理学基础，定义人脸的特征点集；
The paper integrates with physiological basis of facial structure, and defines facial feature points set;
然后交互地修正人脸特点的准确正面位置，并从侧面图象提取特征点的深度；
Secondly the exact front positions of features are rectified by hand with a friendly interface, the depth positions of the feature points are defined from side image manually.
引入基于非线性色彩变换的肤色分割方法，对侧面人脸照片中特征突出的点进行定位，利用已定位点的信息选择初始形状，并将其定位到侧面人脸照片中。
Then, the profile face is segmented based on the nonlinear color transform the initial shape in profile is selected and located using some located distinct points.
引入基于非线性色彩变换的肤色分割方法，对侧面人脸照片中特征突出的点进行定位，利用已定位点的信息选择初始形状，并将其定位到侧面人脸照片中。
Then, the profile face is segmented based on the nonlinear color transform the initial shape in profile is selected and located using some located distinct points.
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