Face Recognition Using Lap Lac Ian Faces

Face Recognition Using Laplacianfaces

Project Work By J.Thiru kumaran. M.Umamaheshwaran.

Guide By Miss.N.Vasuki, M.E.,

Abstract

We propose an appearance-based face recognition method called the Laplacianface approach.

Using Locality Preserving Projections (LPP), the face images are mapped into a face subspace for analysis.

Existing System

Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA).

PCA is to reduce the large dimensionality of the data space to the smaller intrinsic dimensionality of feature space.

The jobs of PCA are prediction, redundancy removal, feature extraction, data compression, etc.

Disadvantages

Less accurate.

Does not deal with manifold structure.

It doest not deal with biometric characteristics.

Proposed System Locality Preserving Projection (LPP), a new algorithm

for learning a locality preserving subspace.

LPP is a general method for manifold learning. The difficulty that the matrix XDXT is Sometimes

singular.

To overcome the complication of a singular XDXT , we first project the image set to a PCA subspace so that the resulting matrix XDXT is nonsingular.

The Algorithm 1)PCA projection.

2)Constructing the nearest-neighbor graph.

3)Choosing the weights.

if node i and j are connected then

else Sij=0;

4)Eigenmap. To compute eigenvector Solve: Gives: w0;w1; . . . ;wk_1

5)Calculate Laplacianface: W=Wpca Wlpp; Where, Wlpp=[w0;w1; . . . ;wk_1] ; Wpca=Transformation matrix of PCA; W=Transformation matrix of Laplacianface.

Project Modules

Read/Write Module. The image files are read, processed and new

images are written into the output images.

Resizing Module. In this module large images or smaller images are converted into standard sizing.

Image Manipulation. The face recognition algorithm using

Locality Preserving Projections (LPP) is developed for various enrolled into the database.

Testing Module. The Intermediate image and find the

tested image then again compared with the laplacian faces

Design Flow Diagram

Experimental Results

Yale Database.

PIE Database.

MSRA Database.

Yale Database

Constructed at the Yale Center for Computational Vision and Control.

It contains 165 grayscale images of 15 individuals.

The images demonstrate variations in lighting condition,facial expression, and with/without glasses.

Performance Comparison on the Yale Database

PIE Database

In PIE Database, the error rate of our Laplacianfaces method decreases fast as the dimensionality of the face subspace.

Table shows the recognition results.

Performance Comparison on the PIE Database

MSRA Database

This database was collected at Microsoft Research Asia.

Sixty-four to eighty face images were collected for each individual in each session.

Table shows the Laplacian faces method has lower error rate than those of Eigen faces and fisher faces.

Performance Comparison on the MSRA Database

Form Design

Entering New Image

Identifying Images

Image Not Found

Application

It could benefit the visually impaired person.

A computer vision-based authentication system could be put in place to allow computer access.

Access to a specific room using face recognition.

Conclusion

Our system is proposed to use Locality Preserving Projection in Face Recognition which eliminates the flaws in the existing system.

This system makes the faces to reduce into lower dimensions and algorithm for LPP is performed for recognition.

THANK YOU………