Face Recognition Using Face Recognition Using Laplacian facesLaplacian faces

Presented by,Pulkit, Shashank, Tanuj,

Shreyash

FACE DETECTION

FEATURE EXTRACTION

FACE RECOGNITION

Table of contentsTable of contents1. Introduction.

2. Objective of the project.

3. Working of the project.

4. Algorithm used.

5. Modules.

6. References.

AbstractAbstractWe propose an appearance based face

recognition method called the laplacianface approach.

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

Existing SystemExisting SystemPrincipal 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.

DisadvantageDisadvantageLess accuracy.

Does not deal with manifold structure.

It doesn’t deal with biometric characteristics.

Proposed System (Objective)Proposed System (Objective)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.

Working (Flow Diagram)Working (Flow Diagram)

InputDBMS

Resizing Resizing

IntermediateFace

LaplacianFace

ComposedImage

Output

SourceDBMS

Compare

Compare

Compare

Average

The AlgorithmThe Algorithm1) PCA projection.

2) Constructing the nearest-neighbor graph.

3) Choosing the weights.If node I and j are connected the

else Sij=0;

4) Eigenmap.

to compute eigenvectorSolve: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 ModulesProject ModulesRead/ 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.

Project ModulesProject ModulesImage Manipulation.

The face recognition algorithm using locality Preserving Projection (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.

Form DesignForm Design

Entering New ImageEntering New Image

Identifying ImageIdentifying Image

Match Not FoundMatch Not Found

Image Not FoundImage Not Found

ApplicationApplicationIt 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.

ConclusionConclusionOur 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.

ReferencesReferences Avinash Kaushal1, J P S Raina, A., “Face Detection using Neural Network

& Gabor Wavelet Transform”, IJCST Vol. 1, Iss ue 1, September 2013 I S S N : 0 9 7 6 - 8 4 9 1

Steve Lawrence , Lee Giles “Face Recognition: A Convolutional Neural Network Approach “ IEEE Transactions on Neural Networks, Special Issue on Neural Networks and Pattern Recognition. vol.3, no110, 2009

Parvinder S. Sandhu, Iqbaldeep Kaur, “Face Recognition Using Eigen face Coefficients and Principal Component Analysis”, International Journal of Electrical and Electronics Engineering 3:8 2009 ISSN 0978-9481

Stan Z. Li and Juwei Lu., “Face Recognition Using the Nearest Feature Line Method” , IEEE TRANSACTIONS ON NEURAL NETWORKS, VOL. 10, NO. 2, MARCH 1999 pp-439-443

S. T. Gandhe, K. T. Talele, and A.G.Keskar “Face Recognition Using Contour Matching” IAENG International Journal of Computer Science, 35:2, IJCS_35_2_06

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