对于复杂的人脸模式，脸部特征定位是人脸自动识别技术的关键。
Due the complex human face patterns, facial features localization is an important technique for automatic human face recognition.
分析了图像信号相位信息的空间特性，以人脸图像为例，研究了相位信息在图像特征定位中的应用。
The space characteristic of phase information of image signal is analysed, and using face image as an example, the application of phase information in image feature location is studied.
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.
本文工作包括：侧面人脸的检测与定位，侧面人脸面部特征的定位与提取和侧面人脸的识别等内容。
The main work in this paper include: side face image detection, side face feature extraction and side face recognition and so on.
主动外观模型是进行人脸面部特征定位和人脸识别的有效方法，近年来已成为图像处理等领域的研究热点。
Active Appearance Models (AAMs) is an effective method of facial recognition and facial feature location, and has become a research hotspot in image processing areas.
许多重要的识别技术假设人脸的脸部特征已被精确定位，因此人脸及其特征的精确定位是人脸处理系统的关键步骤。
Since most recognition techniques suppose the positions of facial features are located accurately, the fine location of facial features is crucial steps in most face processing system.
本文还研究了基于AAM的面部特征定位方法，分析了灰度模型参数及表观模型参数在人脸重构中的作用。
Active appearance model (AAM) is also studied for the localization of facial features. The parameters of texture model and appearance model are studied in detail in reconstruction of face images.
提出了一种改进的主动形状模型，并将其用于人脸面部特征定位的研究。
An improvement ASM is proposed and this model is applied to facial feature localization.
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.
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.
人脸识别主要包括三方面的内容：人脸检测与定位，特征提取，分类与识别。
Face recognition includes three parts: face detection and localization, feature extraction and classification.
因此对于人脸识别中的特征提取来说，不仅要检测出这些特征，而且要准确地加以定位。
Therefore, feature extraction for face recognition, not only to detect these features, but also to accurately locate.
论文的创新性成果包括：1。提出了一种改进的主动形状模型，并将其用于人脸面部特征定位的研究。
The innovative achievements of the thesis include:1. An improvement ASM is proposed and this model is applied to facial feature localization.
该文根据视频应用的特点，结合人脸的肤色和特征部位几何分布特征，提出了一种应用于头肩像序列视频编码的快速人脸定位算法。
A fast face location algorithm for head-and-shoulder sequence is proposed using both skin-color feature and feature face component geometry template.
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.
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。
This approach would help to extract the vital feature points on human face automatically and improve the accuracy of face recognition.
人脸识别系统主要包括两个技术环节：首先是人脸检测和定位，然后是对归一化的人脸图像进行特征提取与识别。
Two key techniques accounts the most for a human-face recognition system: one is face detection and orientation; the other is feature abstraction and recognition from unified human-face image.
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.
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.
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.
在定位分割出上半人脸运动单元子区域图像之后，提出了采用KPCA算法提取它们的特征。
After upper facial action unit location and segmentation, we present the facial action unit feature extraction algorithm based on KPCA.
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.
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.
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.
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.
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.
人脸几何参数测量和特征定位是光学三维测量在医学整形美容、三维人脸识别等方面应用的关键技术。
The geometric parameter measurement and feature location of the face are the key technology in the application of the optical 3D measurement.
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.
本文首先在对人脸检测、人脸面部特征定位技术的相关文献进行综述的基础上，提出了人脸检测技术融合算法。
In this thesis, I present a novel face detection framework based on a face detection algorithm merged with some advanced algorithms.
本文首先在对人脸检测、人脸面部特征定位技术的相关文献进行综述的基础上，提出了人脸检测技术融合算法。
In this thesis, I present a novel face detection framework based on a face detection algorithm merged with some advanced algorithms.
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