Description: 现有的代数特征的抽取方法绝大多数采用一维的方法，即首先将图像转换为一维向量，再用主分量分析（PCA）,Fisher线性鉴别分析（LDA）,Fisherfaces式核主分量分析（KPCA）等方法抽取特征，然后用适合的分类器分类。针对一维方法维数过高，计算量大，协方差矩阵常常是奇异矩阵等不足，提出了二维的图像特征抽取方法，计算量小，协方差矩阵一般是可逆的，且识别率较高。-existing algebra feature extraction method using a majority of the peacekeepers, First images will be converted into one-dimensional vector, and then principal component analysis (PCA), Fisher Linear Discriminant Analysis (LDA), Fisherfaces audits principal component analysis (KPCA), and other selected characteristics, then use the appropriate classification for classification. Victoria against an excessive dimension method, calculation, covariance matrix is often inadequate singular matrix, a two-dimensional image feature extraction method, a small amount of covariance matrix is usually reversible, and the recognition rate higher. Platform: |
Size: 2048 |
Author:小弟 |
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Description: 基于主元分析和神经网络的人脸识别方法.pdf-based on principal component analysis and neural networks face recognition. Pdf Platform: |
Size: 151552 |
Author:shj |
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Description: PCA主元分析法进行特征提取，再进行人脸识别-PCA principal component analysis method for feature extraction, and then carried out face recognition Platform: |
Size: 2048 |
Author:chenxi |
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Description: 基于ORL人脸库的主元分析法用于人脸识别-ORL face database based on the principal component analysis method for face recognition Platform: |
Size: 1024 |
Author:chenxi |
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Description: PCA人脸识别
This package implements basic Principal Component Analysis in Matlab and
tests is with grayscale portion of the FERET database. Images are not
preprocessed and it is up to the user to preprocess the images as wanted,
not changing the filenames-PCA Face Recognition This package implements basic Principal Component Analysis in Matlab andtests is with grayscale portion of the FERET database. Images are notpreprocessed and it is up to the user to preprocess the images as wanted, not changing the filenames Platform: |
Size: 3072 |
Author:蔡加欣 |
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Description: PCA人脸识别 This package implements basic Principal Component Analysis in Matlab and
tests is with grayscale portion of the FERET database. Images are not
preprocessed and it is up to the user to preprocess the images as wanted,
not changing the filenames-PCA Face Recognition This package implements basic Principal Component Analysis in Matlab andtests is with grayscale portion of the FERET database. Images are notpreprocessed and it is up to the user to preprocess the images as wanted, not changing the filenames Platform: |
Size: 1024 |
Author:蔡加欣 |
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Description: PCA主成分分析，用于人脸识别，特征提取等-PCA principal component analysis for face recognition, feature extraction, etc. Platform: |
Size: 1024 |
Author:夏汐 |
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Description: 人脸识别matlab程序
with a principal components analysis for a set of face images as the theme-Face recognition matlab procedures with a principal components analysis for a set of face images as the theme Platform: |
Size: 4096 |
Author:vfory |
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Description: 用于人脸识别的模糊独立成分分析+主成分分析，用模糊支持向量机进行的分类。-Fuzzy Face Recognition for independent component analysis+ principal component analysis, using fuzzy support vector machine classification. Platform: |
Size: 2030592 |
Author:戴欢 |
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Description: 它是SourceForge上的一个开源项目，使用Malib实现实时处理，CSU Face Identification Evaluation System进行人脸识别。算法包括：主成份分析(principle components analysis (PCA)),a.k.a eigenfaces算法,混合主成份分析,线性判别分析(PCA+LDA),图像差分分类器(IIDC),弹性图像匹配算法(EBGM)等等
Malic is realtime face recognition system that based on Malib and CSU Face Identification Evaluation System (csuFaceIdEval). Uses Malib library for realtime image processing and some of csuFaceIdEval for face recognition.-It is a SourceForge open source project, using real-time Malib processing, CSU Face Identification Evaluation System for Face Recognition. Algorithms include: Principal component analysis (principle components analysis (PCA)), aka eigenfaces algorithm, mixed-principal component analysis, linear discriminant analysis (PCA+ LDA), the image difference classifier (IIDC), a flexible image-matching algorithm (EBGM), etc. such as Malic is realtime face recognition system that based on Malib and CSU Face Identification Evaluation System (csuFaceIdEval). Uses Malib library for realtime image processing and some of csuFaceIdEval for face recognition. Platform: |
Size: 1326080 |
Author:乔超 |
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Description: PCA 主成分分析在人脸识别中的应用 基于主成分分析理论对不同人脸库进行学习 总结“经验”并将“经验”用于对人脸的识别中-PCA Principal Component Analysis for Face Recognition Based on principal component analysis theory of different learning face database summary of " experience" and " experience" for the Face Recognition Platform: |
Size: 179200 |
Author:付乔 |
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Description: 主成分分析，人脸识别，模式识别，对图像处理有点帮助-Principal component analysis, face recognition, pattern recognition, image processing for a little help Platform: |
Size: 2048 |
Author:ydq |
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Description: 主成分分析，可以用来做人脸识别的程序，方便，快捷-Principal component analysis, face recognition can be used to do the procedure, convenient and fast Platform: |
Size: 3100672 |
Author:李圣杰 |
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Description: 基于核函数的主分量分析法源代码，可用于人脸识别-Kernel-based principal component analysis source code, can be used for face recognition Platform: |
Size: 27648 |
Author:xiechaocheng |
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Description: Deaf people use facial expressions a non-manual channel for conveying grammatical information in sign language. Tracking facial features using the Kanade - Lucas - Tomasi (KLT) algorithm is a simple and effective method toward recognizing these facial expressions, which are performed simultaneously with head motions and hand signs. To make the tracker robust under these conditions, a Bayesian framework was developed as a feedback mechanism to the KLT tracker. This mechanism relies on a set of face shape subspaces learned by Probabilistic Principal Component Analysis. An update scheme was utilized to modify these subspaces and adapt to persons with different face shapes. The result shows that the proposed system can track facial features with large head motions, substantial facial deformations, and temporary face occlusions by hand.-Deaf people use facial expressions as a non-manual channel for conveying grammatical information in sign language. Tracking facial features using the Kanade- Lucas- Tomasi (KLT) algorithm is a simple and effective method toward recognizing these facial expressions, which are performed simultaneously with head motions and hand signs. To make the tracker robust under these conditions, a Bayesian framework was developed as a feedback mechanism to the KLT tracker. This mechanism relies on a set of face shape subspaces learned by Probabilistic Principal Component Analysis. An update scheme was utilized to modify these subspaces and adapt to persons with different face shapes. The result shows that the proposed system can track facial features with large head motions, substantial facial deformations, and temporary face occlusions by hand. Platform: |
Size: 189440 |
Author:Ng Jack |
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Description: 基于主成份分析(PCA)的人脸识别算法MATLAB程序的实现。机器视觉的作业，内附人脸识别的matlab程序，和人脸库，还有作业的详细要求，以及格式示例和部分参考文献。-Based on principal component analysis (PCA) of the face recognition algorithm MATLAB program implementation. Machine vision operations, included face recognition matlab program, and the face database, as well as the detailed job requirements and format examples and some references. Platform: |
Size: 14964736 |
Author:姚树军 |
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Search - principal component analysis face