I want matlab code to apply PCA on KDDCUP 99 for feature reduction ....etc

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Face recognition using laplaction face at java

The face recognition is a fairly controversial subject right now. A system such as this can recognize and track dangerous criminals and terrorists in a crowd, but some contend that it is an extreme invasion of privacy. The proponents of large-scale face recognition feel that it is a necessary evil to make our country safer. It could benefit the visually impaired and allow them to interact more easily with the environment. Also, a computer vision-based authentication system could be pu ....etc

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In machine learning methods, when the input data becomes extremely large, the current direct methods
require too large learning times and memory. This project investigates one sequential method to overcome this
problem. It is quite a simple method to implement and is tested using the well-known LDA classifier. The student
will try various investigations on different very large data sets and to measure their computational complexities. It
has been shown that this sequential method is very fast and only need a small subset of the large data s ....etc

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i need matlab code for digital video watermarking using dwt and pca ....etc

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Plz, help me with the PCA code for doing image fusion. i need the program with an example. i tried for cameraman image but getting the output as complete black. i blurred it at two different places so i hav i1 n i2 which are blurred images which i converted to zero mean images i_1 n i_2.This is my program. what to do next?

clc;
close all;
clear all;
f1=imread('cameraman.tif');
figure(1);
imshow(f1);
=size(f1);
f1=im2double(f1);
level=input('enter the level of blur');
degradation=fspecial('average',);
i1=f1;
....etc

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Feature extraction a type of dimensionality reduction that efficiently represents interesting parts of an image as a compact feature vector. This approach is useful when image sizes are large and a reduced feature representation is required to quickly complete tasks such as image matching and retrieval.

Feature detection, feature extraction, and matching are often combined to solve common computer vision problems such as object detection and recognition, content-based image retrieval, face detection and recognition, and texture classification. ....etc

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To get full information or details of face recognition using pca please have a look on the pages

http://seminarsprojects.net/Thread-Human-Face-Recognition-Using-PCA-on-Wavelet-Subband

http://seminarsprojects.net/Thread-Face-Recognition-using-Principal-Component-Analysis--44427

http://seminarsprojects.net/Thread-FACE-RECOGNITION-USING-PRINCIPAL-COMPONENT-ANALYSIS?pid=90032

if you again feel trouble on face recognition using pca please reply in that page and ask specific fields in face recognition using pca ....etc

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Abstract

Face recognition is taken into account to be some of the dependable biometric, when safety points are taken into concern. For this, function extraction turns into a crucial drawback. Different strategies are used for extraction of facial function that are broadly categorized into linear and nonlinear subspaces. Among the linear strategies are Linear Discriminant Analysis (LDA), Bayesian Methods (MAP and ML), Discriminative Common Vectors (DCV), Independent Component Analysis (ICA), Tensor faces Multi-Linear Singular Value Decompositio ....etc

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to get information about the topic face recognition technology full report ppt, and related topics refer the page link bellow

http://seminarsprojects.net/Thread-face-recognition-technology-a-seminars-report

http://seminarsprojects.net/Thread-face-recognition-technology-a-seminars-report?page=4

http://seminarsprojects.net/Thread-seminars-report-on-face-recognition-technology

http://seminarsprojects.net/Thread-FACE-RECOGNITION-TECHNOLOGY-SEMINAR-REPORT--28533

http://seminarsprojects.net/Thread-face-recognition-technology--27390

http://semin ....etc

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Principle Component Analysis PCA is a classical feature extraction and data representation technique
widely used in pattern recognition. It is one of the most successful techniques in face recognition. But it has
drawback of high computational especially for big size database. This paper conducts a study to optimize
the time complexity of PCA (eigenfaces) that does not affects the recognition performance. The authors
minimize the participated eigenvectors which consequently decreases the computational time. A
comparison is done to compare ....etc

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