提出一种基于人脸肤色统计模型和主元分析(pca)的人脸检测和定位方法。
This paper presents a human face detection and localization approach which is based on skin color detection and principle component analysis (PCA).
提出一种基于肤色的人脸检测定位算法，设计了基于肤色的人脸检测和定位系统。
In this paper, the authors have presented an algorithm and designed a system for face detection and location based on complexion.
系统分为三部分，按数据流程的先后顺序，分别是视频采集模块、人脸检测和定位模块及人脸识别模块。
System consists of three parts, according to data flow in the order, respectively video capture module, face detection and localization module and face recognition module.
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.
人眼检测和定位是人脸识别技术中一个重要组成部分。
The detection and location of eyes is a very important part of face recognition.
最后设计人脸和眼睛的实时检测及定位的算法。
Finally the face and eyes of the real-time positioning and detection algorithm are designed.
人眼定位和人脸检测是人脸识别的重要环节，复杂背景下，人眼定位及人脸检测容易受到光照以及其它物体的影响。
Eyes location and face detection are important parts of face recognition, and in complex background, illumination and other objects have influence on eyes location and face detection.
为了提高眼睛检测和定位的成功率，该方法包含了一个人脸边界优化算法和一个多级人眼检测方案。
In order to improve the correct rate of eye locating, an algorithm for the refinement of face boundaries and a multi-level eye detection scheme are included in this method.
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.
本文提出了一种视频人脸的定位与跟踪算法，包括人脸检测和人脸跟踪两个方面。
This paper proposes a new method of detecting and tracking video-based human faces, which includes the aspects of human face detection and object tracking.
在粗略定位人脸的基础上，提出了一种新颖简单的区域分割方法，有效地定位出人脸的候选区域，通过检测眼睛和嘴唇完成对人脸的确认。
After detecting face regions approximately, a simply and new region segmentation method is proposed which can effectively locate face candidate regions followed by eye and lip detection.
最后，利用人眼的检测来确定是否为真正的人脸区域，同时还对眼睛和嘴的定位作了深入的研究和实验。
Finally, we further verify these regions by detecting eyes and output the result. At the same time the feature localization is researched and studied.
本文研究了复杂多变人脸的检测和定位问题，提出了一种新的基于对称特征的人脸定位方法。
To improve the efficiency and robustness of the face detection algorithm, a novel approach based on traditional SNoW(Sparse Network of Winnows)is proposed.
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.
人脸特征识别的过程主要分为人脸检测定位和人脸识别。
The paper describes algorithms about face detection and face recognition as many as possible.
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.
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.
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