1. USER VERIFICATION SYSTEM

2. Scope Web Interface RGB separation Pervasive

3. Eigenface 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= AA T Then a reverse of the above matrix is calculated which is nothing but L=A T A.

4. Eigenfaces contd… 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

5. RGB Images They are also called true color images. Each pixel has three values. The pixel values can be represented as a matrix of size M x N x 3. Values are between 0 and 255.

6. Gray Scale Images The R G B values are forced to be equal. Each pixel has only one value which represents the intensity of the brightness. Values are between 0 and 1.

7. Examples of RGB and Gray Scale images in Photoshop.

8. RGB Masking 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.

9. RGB Separation 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.

10. Pervasive 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.

11. DEMO TIME!!!