Transcript USER VERIFICATION SYSTEM

USER VERIFICATION SYSTEM
Scope
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Web Interface
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RGB separation
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Pervasive
Eigenface
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The pixel values of an image are represented in a vector
form.
Mean face is arrived at from these vectors which is
nothing but a mean vector.
Then this mean face is deducted from each of the training
images.
The adjusted training images are arranged in a matrix
form. Say matrix A.
Covariance matrix of the above matrix is arrived at as
follows: C= AAT
Then a reverse of the above matrix is calculated which is
nothing but L=ATA.
Eigenfaces contd…
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Then the eigenvectors of the above matrix are linearly
combined with the vectors in the training images matrix to
obtain ‘eigenfaces’ face space.
To make it simple, since the matrix L is the reverse of the
matrix C, the original source image from the Eigenfaces
“face space” can be obtained to determine where the
image came from.
Now, by taking a test image and mapping it onto this ‘face
space’ we can explore the common features of this one
face with that of the whole training set.
Then, the eigenface of the test image is reconstructed and
compared with the training set images using nearest
neighbor algorithm to find out whether it matches with
any of them
RGB Images
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They are also called true color images.
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Each pixel has three values.
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The pixel values can be represented as a
matrix of size M x N x 3.
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Values are between 0 and 255.
Gray Scale Images
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The R G B values are forced to be equal.
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Each pixel has only one value which
represents the intensity of the brightness.
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Values are between 0 and 1.
Examples of RGB and
Gray Scale images in
Photoshop.
RGB Masking
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Assign an equal value to 2 channels to get
the third channel.
image(:,:,2)=0;
image(:,:,3)=0;
imshow(image);
The Red version of the image will be
displayed.
RGB Separation
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Separate the R value, G value and B value
and store them in different matrices.
R=image(:,:,1);
B=image(:,:,2); and so on.
R is a matrix containing the Red
components of the pixel values and so on.
Once separated they will be in Gray only.
Pervasive
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Training was done everytime atleast once.
For generating few variables that were used by
the testing algorithm.
These variables were stored in the workspace
temporarily for further reference during the same
session.
The value of these variables were lost whenever
matlab was closed.
Modified the training algorithm to save the
variables and the testing algorithm to load these
variables at run time.
This makes the system pervasive.
DEMO TIME!!!