Scanning the Internet for External Cloud Exposures via SSL Certs
Face recognition using laplacian faces
1. FFaaccee RReeccooggnniittiioonn UUssiinngg
LLaappllaacciiaann ffaacceess
Presented by,
Pulkit, Shashank, Tanuj,
Shreyash
FACE DETECTION
FEATURE
EXTRACTION
FACE
RECOGNITION
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1. Introduction.
2. Objective of the project.
3. Working of the project.
4. Algorithm used.
5. Modules.
6. References.
3. AAbbssttrraacctt
We 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.
4. EExxiissttiinngg SSyysstteemm
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.
6. PPrrooppoosseedd SSyysstteemm ((OObbjjeeccttiivvee))
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.
7. WWoorrkkiinngg ((FFllooww DDiiaaggrraamm))
Input
DBMS
Resizing Resizing
Intermediate
Face
Laplacian
Face
Composed
Image
Output
Source
DBMS
Compare
Compare
Compare
Average
8. TThhee AAllggoorriitthhmm
1) PCA projection.
2) Constructing the nearest-neighbor graph.
3) Choosing the weights.
If node I and j are connected the
else
Sij=0;
9. 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.
10. PPrroojjeecctt MMoodduulleess
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.
11. PPrroojjeecctt MMoodduulleess
Image 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.
17. AApppplliiccaattiioonn
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.
18. CCoonncclluussiioonn
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.
19. RReeffeerreenncceess
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
