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人脸的检测与定位(在预处理部分，采用了特别的增强人脸特征与脸部皮肤之间对比度的方法及局域取阈值二值化方法，改进了预处理的效果。在图像分割部分，实现了经典的分合算法，并且使用成组算法改进了分合的效果。在人脸匹配部分，实现了基于眼睛和嘴的几何模型匹配，并对评价函数的构造进行了研究。)-Face Detection and Location (pretreatment, using a special facial features enhanced facial skin and the contr
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进行人脸检测的代码,主要可以实现人脸特征及肤色检测-for face detection code can be achieved major facial features and skin detection
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Matlab脸部识别程序包括
haar feature
adaboosting,Matlab including facial recognition program haar featureadaboosting
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两篇人脸检测的英文论文，是花钱买的哦，英文描述中是一篇文章的英文摘要！-Face and facial feature detection plays an important role in various applications such as human computer interaction, video surveillance,
face tracking, and face recognition. Efficient face and facial feature det
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图象处理,边缘检测提取,定位特征区域对人面部进行识别-Image processing, edge detection extraction, Positioning human facial features of the region to identify
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结合人脸肤色和发色进行的人脸检测，可以忽略人脸的姿态表情，不用进行人脸特征的定位-Combination of face color and hair color for face detection, facial gesture can ignore the expression, do not carry out the positioning of facial features
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针对人脸检测问题中快速性与准确性难以同时满足的情况，提出了一种将肤色和脸部特征相结
合的方法来检测人脸。由于通过基于肤色的算法能快速定位出人脸大致位置，因而检测速度能达
到实时的要求；同时利用脸部特征进一步细检，可保证检测的精确性。-Abstract：This paper proposes a human face detection algorithm based on skin color and facial features to meet
the demand for fa
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研究描述人脸特征的有效方法, 讨论身份证照片的特征提取和检索采用自适应肤色检测技术改进通用的肤色检测算法, 进行脸部区域的划分提出系数投影法对面部五官区域进行分割, 在各区域中提取面部几何特征引人描述脸颊和下额轮廓的曲线参数作为脸形特征, 得到对人脸特征更准确的描述将面部几何特征矢量匹配、脸形曲线参数匹配和脸部图像相关匹配相结合, 实现人像照片的准确检索实验表明该方法性能优良。-Describe the facial features of an effective way to discuss
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一种基于肤色分割、区域分析和模板分布的彩色图像人脸检测算法.首先对输入的彩色图像利用混合高斯模型和亮度模型进行分割,然后根据人脸五官的结构特征对得到的区域进一步分析处理,获得所有可能的候选人脸.接着构造了一种基于双眼和人脸模板的概率模型并利用其对候选人脸进行最终检测.-Based on skin color segmentation, regional analysis and the template in color images of face detection algorithm. F
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The aim of the project was to propose a method for reliable detection and extraction of
facial features in two and three dimensions.
Several 2D methods were attempted – edge detection, intensity scanning, and Gabor
transform. The Gabor transfor
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本程序和实现对一幅包含人脸的bmp图像进行光纤补偿，对人脸区域进行检测实现人眼和人嘴的检测与定位，最后勾绘出人脸的特征以实现人脸检测的目的。-This procedure and implementation of a compensate the fiber contains face of BMP image, the face region detection to realize human eyes and mouth detection and positioning, finall
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在VC6.0环境下，基于五官特征的人脸识别源代码，人脸检测，高斯平滑处理-VC6.0 environment, source code based on facial features face recognition, face detection, Gaussian smoothing
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程序中提供了一种简单方便的人脸面部特征提取方法，测方法提取人脸特征效率高，速度快，提取精度高-The program provides a simple and convenient method for human facial feature extraction, measurement methods to extract facial features high efficiency, high speed, high precision extraction
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人脸检测 基于特征脸的方法 eigenface 做人脸定位的可以参考一下 -Face detection method based on facial features eigenface Positioning human face can refer to
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This project describes the problem of facial expression recognition in the field of computer vision. Firstly, the psychological background of the problem is presented. Then, the idea of facial expression recognition system (FERS) is outlined and the
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This project describes the problem of facial expression recognition in the field of computer vision. Firstly, the psychological background of the problem is presented. Then, the idea of facial expression recognition system (FERS) is outlined and the
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人脸检测方法主要有基于知识的方法、特征不变方法、模板匹配方法、基于外观的方法；脸部特征定位方法分为基于先验知识、几何形状、色彩、外观和关联信息等五类；人脸表情识别方法分为基于几何特征的识别方法，基于整体的识别方法，基于模型的识别方法，基于模板的识别方法。-Face detection methods are mainly based on knowledge of the method, the characteristics of constant method, template match
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用python语言实现的基于Harr特征的人脸检测算法，可以用于人脸的检测，眼睛，鼻子等脸部五官的检测-Using python language based on the Harr feature-based face detection algorithm can be used for the detection of face, eyes, nose and other facial features detection
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该程序可以追踪人脸并检测人脸的面部特征，从而达到识别人脸的功能，利用了OPENCV的图形处理功能，自动追踪人脸，检测与已载入的数据库进行匹配，进行识别，最终会给出检测结果。(The program can track the face and detect the facial features of the face, so as to achieve the function of recognizing the face, the use of OPENCV graphics proces
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运用HOG提取人脸特征与支持向量机算法训练模型，进行人脸识别(Using HOG to extract facial features and support vector machine algorithm training model for face recognition)
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