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 |
Hits:
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
Platform: |
Size: 315392 |
Author:MJ |
Hits:
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 |
Hits:
Search - facial feature extraction of an image matlab