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: 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: 采用特征脸的人脸识别MATLAB程序,识别特定的人脸-Eigenfaces for Face Recognition using MATLAB procedures, identify a specific face Platform: |
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Author:陈炜 |
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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: Amir Hossein Omidvarnia用matlab编写的基于PCA的人脸识别系统和基于FLD的人脸识别系统,其中
的图像示例为Essex face database中 face94 的部分图像,文献可参考"Eigenfaces vs. Fisherfaces:
Recognition using class specific linear projection."已经测试过程序可正常运行没有问题。-Amir Hossein Omidvarnia prepared using matlab Face Recognition System Based on PCA and FLD-based face recognition systems, which sample the image of Essex face database for ' face94' part of images, documents may refer to " Eigenfaces vs. Fisherfaces: Recognition using class specific linear projection. " procedures have been tested there is no problem to normal operation. Platform: |
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Author:刘子木 |
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Description: 线性判别分析(LDA)是一种较为普遍的用于特征提取的线性分类方法。但是将LDA直接用于人脸识别
会遇到维数问题和“小样本”问题。人们经过研究，通过多种途径解决了这两个问题并实现了基于I,DA的人脸识
别 文章对几种基于LDA的人脸识别方法做了理论上的比较和实验数据的支持，这些方法包括Eigenfaces、Fish—
erfaceS、DLDA、VDLDA及VDFLDA。实验结果表明VDFLDA是其中最好的一种方法。-Low—dimensional feature representation with enhanced discriminatory power is of paramount importance to face
recognition(FR)system．Linear Discriminant Analysis(LDA)is one of the most popular linear classification techniques of
feature extraction。but it will meet two problems as computational challenging and “small sample size’’when applying to
face recognition directly．After studying people solve the two problems through several ways and realize the face recogni—
tion based on LDA． The short paper here makes compare on theory and experimental data analysis on several Face
Recognition system using LDA—Based Algorithm，such as Eigenfaces(using PCA)，Fisherfaces，DLDA，VDLDA and VD—
FLDA．The experimental results show that the VDFLDA method is the best of al1． Platform: |
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Author:费富里 |
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Description: 用matlab编写的基于人脸识别系统,文献可参考"Eigenfaces vs. Fisherfaces: Recognition using class specific linear projection."已经测试过程序可正常运行没有问题-Prepared using matlab face recognition system based on the literature may refer to " Eigenfaces vs. Fisherfaces: Recognition using class specific linear projection." Procedures have been tested there is no problem running Platform: |
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Author:jefferychang |
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Description: The purpose of this work is to identify a given face image using main features of face. The dimensionality of face image is reduced by the Principal component analysis (PCA, using eigenfaces method) and the recognition is done by the Back propagation Neural Network (BPNN). Platform: |
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Author:amine |
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Description: eigenfaces face recognition is an algorithm using PCA (principal component anlysis) to identify persons via their visages Platform: |
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Author:Sanaa |
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Description: Eigenfaces are a set of eigenvectors used in the computer vision problem of human face recognition. The approach of using eigenfaces for recognition was developed by Sirovich and Kirby (1987) and used by Matthew Turk and Alex Pentland in face classification. It is considered the first successful example of facial recognition technology.[citation needed] These eigenvectors are derived from the covariance matrix of the probability distribution of the high-dimensional vector space of possible faces of human beings. Platform: |
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Author:pongpan |
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Description: opencv最新书籍《Master OpenCV with Practical Computer Vision Projects》。基于opencv2.4.3编写。采用了实例工程方式讲解。-opencv book:
Chapters:
Ch1) Cartoonifier and Skin Changer for Android, by Shervin Emami.
Ch2) Marker-based Augmented Reality on iPhone or iPad, by Khvedchenia Ievgen.
Ch3) Marker-less Augmented Reality, by Khvedchenia Ievgen.
Ch4) Exploring Structure from Motion using OpenCV, by Roy Shilkrot.
Ch5) Number Plate Recognition using SVM and Neural Networks, by David Escrivá.
Ch6) Non-rigid Face Tracking, by Jason Saragih.
Ch7) 3D Head Pose Estimation using AAM and POSIT, by Daniel Lélis Baggio.
Ch8) Face Recognition using Eigenfaces or Fisherfaces, by Shervin Emami.
Ch9) Developing Fluid Wall using the Microsoft Kinect, by Naureen Mahmood.
Platform: |
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Author:王邦平 |
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Description: This a code in matlab for face recognition using PCA-This is a code in matlab for face recognition using PCA Platform: |
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Author:Dungttn |
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Search - face recognition using eigenfaces