提出了一种基于模板匹配的快速人脸定位算法。
This paper presents a fast and reliable face location algorithm based on template matching.
提出了一种基于多直方图的自适应人脸定位算法。
In this paper, an adaptive face orientation algorithm which is based on the multiple histograms has been provided.
本文提出了一种基于积分投影的快速人脸定位算法。
In this paper, we present a fast and reliable face location algorithm based on integration projection.
提出了一种用于视频会议及可视电话的头肩序列图像人脸定位方法。
A method of face location in head-shoulder sequence images in video conference and video telephone application are proposed.
实验证明该方法能够自动执行头肩分割，人脸定位，简单快速而且有效。
The experimental results show that this approach can automatically perform head-shoulder segmentation and locate the facial region with efficient, simple and fast computation.
HBEL算法是针对大多数人都有头发这一特点，以头发定位为基础的人脸定位算法。
As most people have hairs, HBEL algorithm is based on the hair location technique to meet this characteristic.
系统采用了图像处理和模式识别等技术，提出了一种基于模板匹配的快速人脸定位算法。
The system combines image processing and pattern recognition techniques. We present a fast and reliable face location algorithm based on template matching.
实验表明，本方法有近90 %的检测成功率，速度较快，对部分遮挡的人脸定位也适用。
Experimental result demonstrates its 90% of successful localization rate and the feasible processing speed, it may be work even in a facial part missing occasion.
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.
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.
该算法利用积分投影法预测人脸可能的范围与位置，然后在限定的范围内采用模板匹配的方法定位人脸，从而实现了快速且可靠的人脸定位。
The algorithm firstly detects the sphere and possible location of a face through integration projection and then realizes the precise location of the face within the sphere.
提出了一种快速的、鲁棒的人脸定位及跟踪研究方法，定义了一种新的运动能量表示方法，利用该方法可以很快地检测出图像中的运动区域。
A fast and robust approach method for making the detection, localization and tracking of a persona face in image sequences was presented. A new representation of motion energy was defined.
提出一种在检测到人脸区域的前提下，对人眼进行准确定位的算法。
An eye location algorithm was developed on the premise of that the face region has been detected.
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.
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.
鉴于此，同样是在结合人脸相关信息的基础上，作者提出了一种新的基于人工神经网络的目标定位图像中心的控制策略。
Herein, the idea, which is based on artificial neural network for the control strategy of face auto-location, is proposed.
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.
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).
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.
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.
在侧面人脸图像检测过程中，利用积分投影法对预处理后的二值化侧面人脸图像进行人脸区域的定位。
During side face image detection, the person's face area is located by use of integration projection in the pre-disposed binary image.
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.
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.
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.
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