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Description: 该文件架下一共有4个matlab程序。结合到一起可以实现在matlab下实现选中ORL库的一幅图像然后找出与之最相近3张照片并显示出来。-the document-a total of the next four Matlab procedures. Can be combined to achieve the Matlab ORL selected under the image of a then identify with the most similar 3 photos and displayed.
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Size: 1061888 |
Author: 周桐 |
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Description: 結合webcam 進行人臉辨識並有GUI方便可正確執行-Combined with webcam for face recognition and can facilitate the correct implementation of GUI
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Size: 1244160 |
Author: BB |
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Description: 基于PSO训练SVM的人脸识别
利用支持向量机在学习能力方面表现的良好性能,结合核主元分析特征提取方法,将其应用于人脸识别中,该方法在实验中表现了良好的识别性能,为人脸识别领域提供了一条新的识别途径-PSO-based SVM for face recognition training using support vector machine learning ability in the performance of good performance, combined with KPCA feature extraction method, applied to face recognition, the method in experiments to identify the performance of a good performance for the field of face recognition has provided a new way to identify
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Author: 彭伟 |
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Description: 摘 要： 提出了一种基于改进 Ha u s d o r f f距离的人脸相似度匹配的方法， 该方法首先将人脸划分为脸型、 双眼、 鼻、 嘴等几个特征点
集， 分别计算各部分的改进 Ha u s d o r f f 距离， 然后进行加权计算相似度。利用该方法， 在 A S M( 主动形状模型) 定位人脸的基础上进
行了人脸检索。 实验表明， 利用人脸相似度计算方法对人脸特征库进行搜索， 达到 了较好的效果。同时结合 A S M 自动人脸检测， 本
方法可以全自动完成人脸匹配， 应用于人脸识别及数字娱乐等领域。-Abstract: This paper presents a distance based on an improved Ha usdorff face similarity matching method, which will first face is divided into face, eyes, nose, mouth, and several other feature point sets, were calculated to improve the various parts of Ha usdorff distance, and then weighted similarity. Using this method, in the ASM (active shape model) positioned on the basis of human face human face retrieval. Experiments show that the use of face similarity calculation method for facial feature database to search, to achieve good results. ASM automatically combined with face detection, this method can be completed fully automatic human face matching, used in face recognition and digital entertainment and other fields.
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Author: alsocc |
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Description: 编程是由pca和lda结合的脸部特征提取，用于三维图像。-multilinear principal component analysis combined with Linear discriminant analysis 3D face feature extraction.
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Size: 4096 |
Author: tai shu worn |
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Description: 特征提取是模式识别研究领域的一个热点.本文提出了一种基于独立成分分析和线性鉴别分析的特征提取方法.谊方法中引入了零空间的概念,指出了前人算法中的不足之,并且给出了一个完整的独立成分分析和线性鉴别分析的组合算法.在ORL和Yale人脸数据库上的实验表明了该方法的有效性.-Feature extraction is a hot field of pattern recognition research. In this paper, which is based on independent component analysis and linear discriminant analysis feature extraction method. Yi method introduced the concept of zero-space, pointing out that the algorithm predecessors shortcomings, and gives a complete independent component analysis and linear discriminant analysis of the combined algorithm. In the ORL and Yale face database, the experiment shows the effectiveness of this method.
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Size: 256000 |
Author: 费富里 |
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Description: 二维线性鉴别分析(2DLDA)算法能有效解决线性鉴别分析(LDA)算法的“小样本”效应，支持向量机
(SVM)具有结构风险最小化的特点，将两者结合起来用于人脸识别。首先，利用小波变换获取人脸图像的低频分量，忽
略高频分量：然后，用2DLDA算法提取人脸图像低频分量的线性鉴别特征，用“一对多”的SVM 多类分类算法完成人脸
识别。基于ORL人脸数据库和Yale人脸数据库的实验结果验证了2DLDA+SVM算法应用于人脸识别的有效性。-”Small sample size”problem of LDA algorithm can be overcome by two—dimensional LDA f 2DLDA)，and
Support Vector Machine(SVM)has the characteristic of structural risk minimization．In this paper，two methods were
combined and used for face recognition．Firstly，the original images were decomposed into high—frequency and low—frequency
components by Wavelet Transform(WT)．The high—frequency components were ignored，while the low—frequency components
can be obtained．Then．the liner discriminant features were extracted by 2DLDA，and”one VS rest”。strategy of SVMs for
muhiclass classification was chosen to perform face recognition． Experimental results based on ORL f Olivetti Research
Laboratory1 face database and Yale face database show the validity of 2DLDA+SVM algorithm for face recogn ition．
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Author: 费富里 |
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Description: 实现一种结合颜色空间、变换及变形模板的自动唇部定位及唇轮廓提取、跟踪方法首先在空间建立肤色模型进行人脸检测、定位, 并由人脸几何特征进行唇部粗定位然后结合唇色模型进行变换使肤、唇色差别明显化, 提出根据亮度信息对变换结果预处理后用法进行图像分割, 经唇色模型进一步验证后实现唇部精定位再使用变形模板来进行嘴唇轮廓特征提取, 为增强内轮廓定位的鲁棒性, 对经亮度预处理和唇色模型验证得到的口腔区域边缘图进行曲线拟合来实现内轮廓定位最后, 将唇读图像序列中上一帧的唇部定位结果拓展后作为当前帧的预测区域再进行处理来实现唇动跟踪。-To achieve a combination of color space, transform and deformable template automatic lip localization and lip contour extraction, tracking the establishment of the first color model in the space of face detection, location, geometric features of the face by the rough location and then combined with lip lip transformation so that the skin color model, lip color difference visible, presented the results according to the luminance information to transform the image after pretreatment use of segmentation, and further validated by the lip model to achieve precise positioning and then use the deformation of the lip template for lip contour extraction , to enhance the robustness of location within the outline, by the brightness of the pretreatment and the lip model validation by oral Quyu edge map to fit curve to achieve the final positioning within the contour, the lip-reading image sequence in the last frame of the lips After positioning results extend the forecast area as the current frame
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Size: 970752 |
Author: 郭事业 |
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Description: 主成成份分析( PCA) 方法是人脸识别技术中常用的一种一维特征抽取方法。传统PCA 方法用于人脸识别常常面临图像维数高,直接计算量的问题。为了解决这2 个问题,人们对PCA 进行了改进,提出并实现了多种基于PCA 的人脸识别。对3 种基于PCA 的人脸识别方法做了理论上的研究和实验上的性能比较。实验结果表明PCA + 2DPCA 是其中综合效果最好的一种方法。-Principal component analysis into (PCA) is a commonly used face recognition feature extraction method of one-dimensional. The traditional PCA method for face recognition are often faced with images of high dimensionality, direct calculation of the problem. To solve this two problems, one of the PCA has been improved, proposed and implemented a variety of PCA-based Face Recognition. On 3 Face Recognition Based on PCA do theoretical research and experiments on the performance comparison. Experimental results show that the PCA+ 2DPCA combined effect is one of the best methods.
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Author: Open00 |
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Description: 此文的目的有三个：第一，当地连续均值量化变换特征是提出照明和传感器敏感操作在目标识别上。其次，注册稀疏Winnows网络分割，提出了加快原分类。最后，特点和分类相结合对于正面人脸检测任务。检测结果列
为MIT + CMU系统和BioID数据库。关于这人脸检测器，接收器操作特征曲线BioID数据库产生最好的结果公布。对于结果麻省理工学院的中央结算系统+数据库相当于国家的最先进的脸探测器。一个人脸检测算法的MATLAB版本可以从http://www.mathworks.com/matlabcentral/fileexchange/
loadFile.do？的ObjectID = 13701＆的objectType =FILE下载。
-The purpose of this paper is threefold: firstly, the local Successive
Mean Quantization Transform features are proposed for illumination
and sensor insensitive operation in object recognition. Secondly, a
split up Sparse Network of Winnows is presented to speed up the
original classifier. Finally, the features and classifier are combined
for the task of frontal face detection. Detection results are presented
for the MIT+CMU and the BioID databases. With regard to this
face detector, the Receiver Operation Characteristics curve for the
BioID database yields the best published result. The result for the
CMU+MIT database is comparable to state-of-the-art face detectors.
A Matlab version of the face detection algorithm can be downloaded
from http://www.mathworks.com/matlabcentral/fileexchange/
loadFile.do?objectId=13701&objectType=FILE.
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Size: 1397760 |
Author: 霞 |
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Description: 对两张图片合并，看起来合并效果不错，可以-Of the two images merge, the combined effect looks good, you can try
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Size: 181248 |
Author: yinjun |
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Description: Face detection algorithms are widely used in computer vision as they provide fast and reliable results depending on the application
domain. A multi view approach is here presented to detect frontal and profile pose of people face using Histogram of Oriented Gradients, i.e. HOG, features. A K-mean clustering technique is used in a cascade of HOG feature classifiers to detect faces. The evaluation of the algorithm shows similar performance in terms of detection rate as state of the art algorithms. Moreover, unlike state of the art algorithms,our system can be quickly trained before detection is possible. Performance is considerably increased in terms of lower computational cost and lower false detection rate when combined with motion constraint given by moving objects in video sequences. The detected HOG features are integrated within a tracking framework and allow reliable face tracking results in several tested surveillance video sequences.
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Size: 293888 |
Author: linuszhao |
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Description: Combined Face Recognition Using Wavelet Transform and Bayesian
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Size: 16930816 |
Author: venkatvasu |
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Description: 在高性能的人脸识别系统中，结合人脸与眼睛的检测子-In the high-performance face recognition system, combined with the face and eye detection sub
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Size: 347136 |
Author: 草广 |
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Description: 结合OPENCV与VC写的关于人脸识别的代码，实现了对人脸的检测，然后再进行识别！-Combined with VC wrote on OPENCV face recognition code, the realization of human face detection, and then to identify!
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Size: 15360 |
Author: xiao |
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Description: 利用labview与matlab结合实现人脸定位算法 置入mathscript-Labview matlab combined with the use of a face localization algorithm implementation into mathscript
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Size: 22528 |
Author: 张哲为 |
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Description: 一种人脸识别的方法，结合了传统的pca和lda，也加入了一些新的思考和处理思路-A method of face recognition, and combined with the traditional PCA and LDA, also added some new thinking and process thinking
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Author: 杨光 |
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Description: 计一个新的人脸片网络，基于我们定义的一个新的相似性度量，称为随机路径度量（RP）。RP度量来自于路径的整体相似性，通过在网络中执行随机walk。它可以全局性的表征人脸空间的构造（contextual, 上下文）和曲线特征。为了应用RP度量，我们构建2类网络：in-face 网络和out-face 网络。这2个网络在结构上是互补的且组合可以改进识别性能。-The traditional measurements such as various
mathematical norms, Hausdorff distance, and approximate
geodesic distance cannot accurately capture the structural
information between faces in such complex circumstances.
To address this issue, we develop a novel face patch
network, based on which we define a new similarity measure
called the random path (RP) measure. The RP measure is
derived the collective similarity of paths by performing
random walks in the network. It can globally characterize
the contextual and curved structures of the face space. To
apply the RP measure, we construct two kinds of networks:
the in-face network and the out-face network. The in-face
network is drawn any two face images and captures
the local structural information. The out-face network
is constructed all the training face patches, thereby
modeling the global structures of face space. The two
face networks are structurally complementary and can be
combined together to improve the reco
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Author: 郭继东 |
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Description: 用贝叶斯中的高斯类库实现人脸识别，结合PCA算法实现(Face recognition using the Gauss class library in Bayes, combined with PCA algorithm)
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Size: 1024 |
Author: L.Xun
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Description: 用贝叶斯中的努伯利类实现人脸识别，结合PACK算法实现(Bayesian face recognition in Nuboli, combined with PACK algorithm)
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Size: 1024 |
Author: L.Xun
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