1.improving face recognition performance using rbpca maxlike and information fusion

8/2/2019 1.Improving Face Recognition Performance Using RBPCA MaxLike and Information Fusion

1/14

IMPROVING FACE RECOGNITION

PERFORMANCE USING RBPCA MAXLIKE

AND INFORMATION FUSION


2/14

Abstract

We have implemented an efficient system to recognize faces from

images with some near real-time variations.

Our approach essentially was to implement and verify the

algorithm Eigen faces for Recognition Maximum Likelihood which

solves the recognition problem for 2-D image of faces, using the

principal component analysis.


3/14

Existing System

Face is a complex multidimensional visual model and developing a

computational model for face recognition is difficult.

If novel face representation using shapes derived from the masked trace

transform here after simply called shape transform (STT)

The Trace transform is a very rich representation of an image and in order to

use it directly for recognition, one has to produce a much simplified version of

it.


4/14

In our approach, a shape is represented by a discrete set of points obtained from

an edge detector. Let us denote by the set of edge pixels.

In other word, at edge pixel , we compute a coarse histogram of the relative

coordinates of all other edge points in the image, with respect to pixel.


5/14

Proposed System

Proposed methodology is connection of two stages Feature extraction using

Regularization Block Based Principle Component Analysis(RBPCA) and

recognition using the feed forward back propagation Neural Network.

The proposed technique is coding and decoding of face images, emphasizing

the significant local and global features.

Image size normalization, histogram equalization and conversion of the

images.


6/14

One Neural Network is used for each person in the database in which face

descriptors are used as inputs for positive and negative values variations.

New test image is taken for recognition (from test dataset) and its face

descriptor is calculated from the Eigen faces found before.


7/14

MODULES:

User LoginInput Face Image

Image Storage(RBPCA MaxLike)Face Featuring Extraction

(Information Fusion)

Feature Matching

Comparison/Decision


8/14

USER LOGIN:

User Login module provides the authentication of

the user.

It checks whether the user is the correct person to

access the resources by checking the username

and password (entered by the user) by comparing it

with the information stored in the database.


9/14

import java.awt.Font;import java.io.File;-----------------------------------------------------------------------------------------------------------

public UserAuthentication() {super();try{

UIManager.setLookAndFeel("com.sun.java.swing.plaf.windows.WindowsLookAndFeel");

}catch(Exception e){e.printStackTrace();

-----------------------------------------------------------------------------------------------------------This method initializes thisprivate void initialize() {

this.setSize(new java.awt.Dimension(800,600));

this.setContentPane(getJPanel());this.setTitle("Face Recognition");this.setVisible(true);


10/14

USER NAME:

private JTextField getUsername() {if (username == null) {

username = new JTextField();username.setBounds(new

java.awt.Rectangle(165,60,118,25));}return username;

PASSWORD:

private JPasswordField getPassword() {if (password == null) {

password = new JPasswordField();password.setBounds(new

java.awt.Rectangle(166,105,113,27));}return password;

}


11/14


12/14

IMAGE STORAGE(RBPCA MAXLIKE)


13/14

package EigenFaces;import Jama.*;public class EigenFaceComputation {private final static int MAGIC_NR =11;

public static FaceBundle submit(double[][] face_v, int width, int height, String[] id, boolean debug) {

int length = width*height;int nrfaces = face_v.length;int i, j, col,rows, pix, image;double temp = 0.0;double[][] faces = new double[nrfaces][length];

ImageFileViewer simple = new ImageFileViewer();

simple.setImage(face_v[0],width,height);

double[] avgF = new double[length];

/*Compute average face of all of the faces. 1xN^2*/

for ( pix = 0; pix < length; pix++) {temp = 0;

for ( image = 0; image < nrfaces; image++) {temp += face_v[image][pix];

}avgF[pix] = temp / nrfaces;

}

simple.setImage(avgF, width,height);


14/14

/*Compute difference.

*/

for ( image = 0; image < nrfaces; image++) {

for ( pix = 0; pix < length; pix++) {face_v[image][pix] = face_v[image][pix] - avgF[pix];

}}/* Copy our face vector (MxN^2). We will use it later */

for (image = 0; image < nrfaces; image++)System.arraycopy(face_v[image],0,faces[image],0,length);

System.arraycopy(face_v,0,faces,0,face_v.length);

//simple.setImage(face_v[0],width,height);

/*Build covariance matrix. MxM

*/

Matrix faceM = new Matrix(face_v, nrfaces,length);Matrix faceM_transpose = faceM.transpose();

/*Covariance matrix - its MxM (nrfaces x nrfaces)*/

Matrix covarM = faceM.times(faceM_transpose);

double[][] z = covarM.getArray();//System.out.println("Covariance matrix is "+z.length+" x "+z[0].length);

/*

Compute eigenvalues and eigenvector. Both are MxM

1.improving face recognition performance using rbpca maxlike and information fusion

Documents