Download - Face Recognition Using Laplacian 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………