Description: 基于非负矩阵分解（NMF）的人脸特征提取算法，NMF基本思想是找到一个线性子空间W，使的构成子空间的基本图像的像素点都是正值，而且人脸图像在子空间上的投影系数也是正数-Non-negative Matrix Factorization (NMF) of facial feature extraction algorithm, NMF basic idea is to find a linear sub-space W, so that the composition of sub-space of the basic image pixels are positive, and face image in the sub-space projection coefficient is positive Platform: |
Size: 1024 |
Author:李伟 |
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Description: 快速的人脸特征提取算法KPCA，比普通的pca特征提取算法在效率上好了不少-Fast facial feature extraction algorithm KPCA, than ordinary PCA feature extraction algorithm in the efficiency of a good many Platform: |
Size: 1024 |
Author:songy |
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Description: 采用SVD方法进行人脸特征提取,希望对大家有用-SVD method using facial feature extraction, in the hope that useful to everybody Platform: |
Size: 1024 |
Author:东方 |
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Description: 为了更有效地提取图像的局部特征，提出了一种基于2维偏最小二乘法(two—dimensional partial least
square，2DPLS)的图像局部特征提取方法，并将其应用于面部表情识别中。该方法首先利用局部二元模式(1ocal
binary pattern，LBP)算子提取一幅图像中所有子块的纹理特征，并将其组合成局部纹理特征矩阵。由于样本图像
被转化为局部纹理特征矩阵，因此可将传统PLS方法推广为2DPLS方法，用来提取其中的判别信息。2DPLS方法
通过对类成员关系矩阵的构造进行相应的修改，使其适应样本的矩阵形式，并能体现出人脸局部信息重要性的差
异。同时，对于类成员关系协方差矩阵的奇异性问题，也推导出了其广义逆的解析解。基于JAFFE人脸表情库的
实验结果表明，该方法不但可以有效地提取图像局部特征，并能取得良好的表情识别效果。-To better the image of the local feature extraction, a partial least squares method based on 2D (two-dimensional partial least
square, 2DPLS) image local feature extraction method, and applied to facial expression recognition. In this method, use of local binary pattern (1ocal
binary pattern, LBP) operator extracts an image texture features of all sub-blocks, and their combination into the local texture feature matrix. As the sample image
Be translated into the local texture feature matrix, so the traditional PLS method can be generalized to 2DPLS method used to extract the identification information. 2DPLS method
Through the class membership matrix in the corresponding modifications to adapt the sample matrix, and can reflect the importance of face poor local information
Different. Meanwhile, members of the class covariance matrix of the singular relations issues, also derived the generalized inverse of the analytical solution. Based on the JAFFE facial expression database
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Author:MJ |
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Description: 计算机人脸识别技术( Face Reocgnition)就利用计算机分析人脸图像,从中提取出有效的识别信息,用来辨认身份的一门技术。[ 1 ]即对已知人脸进行标准化处理后,通过某种方法和数据库中的人脸样本进行匹配,寻找库中对应人脸及该人脸相关信息。人脸自动识别系统有两个主要技术环节,一是人脸定位,即从输入图像中找到人脸存在的位置,将人脸从背景中分割出来,二是对标准化后的人脸图像进行特征提取和识别。本文中介绍的PCA (特征脸)方法就是一种常用的人脸
特征提取方法。-Computer Face Recognition Technology (Face Reocgnition) on the use of computer analysis of facial image, to extract the valid identification information used to identify the status of a technology. [1] that is known to standardize treatment of face, through a method and a database of face samples for matching, search library, the corresponding face and the face-related information. Automatic face recognition system has two main technical aspects, first, face location, that is, from the input image to find the location of the face there, the faces will be split out from the background, the second is, the standard features of face images extraction and recognition. Described in this paper PCA (Eigenfaces) method is a common facial feature extraction method. Platform: |
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Author:Highjoe |
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Description: 利用主元分析和奇异值分解进行人脸特征提取的方法(并详细阐述其在PQRSQT中的实现过程(包括读取图像文件U计算均值脸U求特征值和特征向量(计算人脸特征参数-实现过程均给出了MATLAB代码-Using principal component analysis and singular value decomposition facial feature extraction method (and detail its in the PQRSQT in the implementation process (including the read image files U U to find the eigenvalues and eigenvectors (calculated facial feature parameters to calculate the mean face- implementation process are given in the program code Platform: |
Size: 5358592 |
Author:michael |
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Description: 人脸识别技术作为生物体特征识别技术的重要组成部分，在近些年来已经发展成为计算机视觉和模式识别领域的研究热点。本实验是基于K-L变换的主成分分析法（PCA）在人脸识别中的应用，在ORL人脸库的基础上通过Matlab实现了快速PCA算法的验证仿真，并对样本图像进行了重构。本实验在ORL人脸库的基础上，选用每人前5张图片，共计40人200幅样本图像，通过快速PCA算法将10304维的样本特征向量降至20维，并实现了基于主分量的人脸重建，验证了PCA算法在高维数据降维处理与特征提取方面的有效性。-Facial recognition technology as a biological feature recognition technology is an important part of, in recent years has become a hot research topic in the field of computer vision and pattern recognition.This experiment is based on K- L transform principal component analysis (PCA) in the application of face recognition, based on ORL face validation of rapid PCA algorithm was realized by Matlab simulation, and reconstructed the sample image., on the basis of the experiment on ORL face , choose top 5 pictures each, a total of 40 people 200 sample image, through rapid PCA algorithm the sample feature vector of 10304 d down to 20 d, and implements the face reconstruction based on principal component, PCA algorithm is verified in the high-dimensional data processing and feature extraction is effective to dimension reduction. Platform: |
Size: 20067328 |
Author:季科 |
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