Description: 用主成分分析与神经网络进行人脸的识别
文件是整个的ＭＡＴＬＡＢ数据文件-using principal component analysis and neural networks face identification document is the entire data file MATLAB Platform: |
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Description: 从前用matlab编的人脸识别的特征提取以及识别程序，希望有用-The previous series of face recognition using matlab feature extraction and identification procedures, seek to help Platform: |
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Author:Bryan |
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Description: Malic是一个完整的Linux下的人脸识别系统源代码，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)-Malic is a complete face recognition system under the Linux source code, it is a SourceForge open source project, using real-time Malib treatment, 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) Platform: |
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Author:严锐 |
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Description: matlab编写的Modular PCA的源代码，以Yale人脸库为例。Moduplar PCA首先对原始图像分块，然后对分块后的所有子图像进行PCA分析提取投影特征，对待识别图像也是先进行分块，然后分别计算子图像在投影特征下的投影系数，最后根据最近邻分类器进行分类。-Modular PCA prepared matlab source code to Yale face database as an example. Moduplar PCA first block of the original image, and then on the block after all the sub-image analysis to extract PCA projection characteristics, treatment identification image is first block, and then calculated the characteristics of sub-image in the projector under the projection coefficient, and finally the light of recent Neighbor Classifier classification. Platform: |
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Author:章格 |
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Description: 做模式分类时（例如指纹识别，人脸识别），一个需要处理的难题是维数非常大，人脸往往是百万维的，目前计算机的能力还不足以快速地计算这么高维的数据。pca是一中降维的方法，用它可以把高维数据映射到一个维数较低的空间上考虑。-To do pattern classification (for example, fingerprint identification, face recognition), a need to deal with the problem is very large dimension, often millions of people face-dimensional, the current capacity of the computer fast enough to calculate such a high-dimensional data. PCA is a dimensionality reduction of the method, it can be high dimensional data is mapped to a lower dimension space to consider. Platform: |
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Author:李志焕 |
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Description: Malic是一个完整的Linux下的人脸识别系统源代码，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)-Malic is a complete face recognition system under the Linux source code, it is a SourceForge open source project, using real-time Malib treatment, 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) Platform: |
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Author:张晓 |
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Description: 线性鉴别技术是一种较好的分类技术,本程序采用了基于PCA与LCA的人脸识别方法使人脸识别效率得到提高-Linear identification technology is a better classification techniques, the procedures for the use of PCA and LCA-based face recognition method of face recognition efficiency Platform: |
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Author:黄璞 |
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Description: 人脸识别算法PCA的可执行程序，识别率为88.5％，为matlab平台程序，大家共享吧-PCA Face Recognition Algorithm for the executable program, identification rate of 88.5 for matlab platform procedure, let everyone share Platform: |
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Author:周秋红 |
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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: |
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Author:乔超 |
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Description: 本文的目的是提供一个我开发的SSE优化的，C++库，用于人脸检测，你可以马上把它用于你的视频监控系统中。涉及的技术有：小波分析，尺度缩减模型（PCA,LDA,ICA），人工神经网络（ANN），支持向量机（SVM），SSE编程，图像处理，直方图均衡，图像滤波，C++编程，还有一下其它的人脸检测的背景知识-The purpose of this paper is to provide an I developed SSE optimized, C++ library, used for face detection, you can immediately use it for your video surveillance system. The technology involved are: wavelet analysis, scaling down model (PCA, LDA, ICA), artificial neural network (ANN), support vector machine (SVM), SSE programming, image processing, histogram equalization, image filtering, C++ programming, but also what other people have the background knowledge of face detection Platform: |
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Author:憨豆 |
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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: 这篇文章是我实验室写的基于2DPCA的红外人脸识别方法，这里面系统的阐述了使用2DPCA算法比传统PCA算法的好处，本文还阐述了红外图像在人类识别方面独特的优点及存在的不足。-This article is written in my laboratory face recognition method based on infrared 2DPCA, there is a systematic exposition of the use of algorithms than the traditional PCA algorithm 2DPCA benefits, the paper also describes the infrared image in human identification for the existence of the unique advantages and inadequate. Platform: |
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Author:邢春 |
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Description: 本程序是本人根据人脸识别PCA改进的，用于掌纹识别，是学习PCA特征提取的很好的学习资料。-I based this program is to improve the face recognition PCA for palmprint identification, PCA feature extraction is to learn a good learning materials. Platform: |
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Author:彭峰 |
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Description: 采用PCA对人脸特征进行抽取，用SVM多累分类器对人脸进行识别，有操作界面-Using PCA for facial feature extraction, and more tired with the SVM classifier for face identification, a user interface Platform: |
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Author:钟佩 |
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Description: 研究内容包括四个方面：分别是人脸检测，图像的预处理，特征提取和人脸识别。能在不同光照，不同表情，不同姿态的情况下获得准确的识别。-The study includes four aspects: face detection, image preprocessing, feature extraction and face recognition. The accurate identification can be obtained in the case of different light, different expressions, different posture. Platform: |
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Author:xuyanjie |
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Description: 基于matlab的PCA人脸识别完成程序，包含特征值与特征向量的提取、训练样本以及最后的识别检验-Matlab-based PCA Face Recognition to complete the process, including the eigenvalues and eigenvectors, identification of training samples and the final test Platform: |
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Author:薛进 |
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Description: 稀疏主成分分析用于脸部检测和识别的基础知识介绍。初学者很专业的入门材料。-Sparse principal component analysis for face detection and identification of the basics of introduction. Introductory material for beginners very professional. Platform: |
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Author:hzx |
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