BIOMETRIC FACE AND IRIS AUTHENTICATION
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10072010, 03:27 PM
BIOMETRIC FACE AND IRIS AUTHENTICATION.ppt (Size: 1.94 MB / Downloads: 204) BIOMETRIC FACE AND IRIS AUTHENTICATION Presented by V. Divya M. Janani G. Lakshmi J. Magdalene Milan Project Guide Mrs. V. Vinodha Vijayaragavan. OBJECTIVE AND IDEA: Enhance security purpose and right to vote The face and iris images are taken and compared with the database images and turn on the polling machine if and only if it is authenticated. IRIS IS A UNIQUE KEY IN THE WORLD No two irises are alike. There is no detailed correlation between the iris patterns of even identical twins, or the right and left eye of an individual. Accuracy is greater than finger prints and DNA. APPROVAL GIVEN BY CENTRE OF SCIENCE AND MATHEMATICS CONGREGETION OF UK FLOW DIAGRAM: ALGORITHM USED: PCA: Principle Component Analysis PCA, commonly referred to as the use of Eigen faces. PCA approach is used to reduce the dimension of the data and removes the information that is not useful. Thus revealing the most effective low dimensional features of facial patterns. PCA algorithm is optimal in the sense of efficiency. FACE RECOGNITION Flow Diagram: Input Image rgb to gray DCT PCA LMS STEP1: INPUT IMAGE STEP 2: RGB TO GRAY STEP 3: DISCRETE COSINE TRANSFORM (DCT) Computers store images as an NxN matrix of values that represent pixels For example 256 grayscale image each pixel is stored as a value between 0 â€œ 255 0 = black pixel 255 = white pixel Value between are shades of gray. Hence we going to DCT. Two Dimensional DCT Example After subtracting, STEP 4: PRINCIPAL COMPONENT ANALYSIS(PCA) Principle Linear project and implimentationion method to reduce the number of parameters Transfer a set of correlated variables into a new set of uncorrelated variables Map the data into a space of lower dimensionality Form of unsupervised learning Properties It can be viewed as a rotation of the existing axes to new positions in the space defined by original variables New axes are orthogonal and represent the directions with maximum variability STEPS IN PCA: A.Calculate the covariance matrix For the dataset of p variables(dimensions) X1;X2; Xp for n individuals. Then we have a n x p data matrix X. The covariance matrix is Thus covariance and variance are calculated B.TO CALCULATE EIGEN VECTOR & EIGEN VALUE The Characteristics equation of Eigen Vectors are given as (A  lI)X = 0 This is a homogeneous system of equations, and from fundamental linear algebra, we know that a solution exists if and only if det (A leigenval I) = 0 Using PCA in the Face Recognition (FR) (1) STEP 5: LMS ALGORITHM It calculates the difference between eigen vectors for the face. (Eigen vector1Eigen vector2)^2 If o/p =0 : authenticated If o/p != 0 : unauthenticated FACE RECOGNITION IRIS RECOGNITION: STEPS IN IRIS RECOGNITION: STEP1: INPUT IMAGE STEP 2: GAUSSIAN NOISE Noise by definition is just unwanted sound. It is added to reduce further addition of noise. Gaussian coefficent 0.0113 0.0838 0.0113 0.0838 0.6193 0.0838 0.0113 0.0838 0.0113 STEP 3: EDGE DETECTION: In our project and implimentation we use Canny edge detection as it overcomes various drawbacks present in other detectors. Canny coefficients are convolved with the filtered image. CANNY COEFFICIENTS: 1 1 1 1 1 1 1 1 1 1 1 1 20 1 1 1 1 1 1 1 1 1 1 1 1 STEPS 4: NORMALISATION STEP 5: IMAGE LOCALISATION The database contains already processed normalised value. The current normalised value is compared with the database , if found similar , the iris is recognised. COMPARING THE RECOGNITIONS If both comparison is positive, it is authenticated and turns on the polling machine. Or else it is unauthenticated. Software required: MATLAB MATLAB is a highperformance language for technical computing. It integrates computation, visualization, and programming in an easytouse environment where problems and solutions are expressed in familiar mathematical notation. MATLAB is an interactive system whose basic data element is an array that does not require dimensioning. GUI:GRAPHICAL USER INTERFACE How does it work 1. We can create buttons , axes( to display images) according to the task we perform and provides aesthetic look. 2. The corresponding codes will be generated in editor window and call back functions can be coded into it as per the need. 3. When we run the program, it calls the GUI automatically and we can display the output through it. FUTURE WORK: Last year of may 2009, only 36% voting takes place in TamilNadu and 60% voting amid violence. Polling rate has been declined. Thus this project and implimentation helps individuals by making it available at the door step. CONCLUSION: PCA algorithm which helps in identifying and distinguishing the unique features of all the individuals. It provides an enhanced security purpose to avoid the mal practices which is still taking place in our country. We have provided a helping hand to our government to have secured method of voting. 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