Presented By
Vinay Goel

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Biometric Recognition – Fingerprints
Introduction
Finger print recognization system is under biometric application used to increase the user security.
Generally the biometric systems operating in two modes,
Verification
Identification.
Verification: The person to claim identity through an Number(identification number), user name etc…the system then gathers the input data and compares it in priviously stored data then give the result related data. if the data not related to template data it it simply denied.
Identification:
If the input data matches any of the template data sets, the system will allow access
OBJECTIVE
The system processes the data and collects the identifying features of the fingerprint. Next, it compares this information to previously stored information from various fingerprints. After making the comparison, the system determines if the input image matches the data of a fingerprint already in the database.
A few different processing methods are used to extract the identifying features, and the performance of each technique is analyzed.
FINGERPRINT
A fingerprint pattern is comprised of a sequence of Ridges and Valleys.
In a fingerprint image, the ridges appear as dark lines while the valleys are the light areas between the ridges.
The fingerprint image will have one or more regions where the ridge lines have a distinctive shape. These shapes are usually characterized by areas of high curvature or frequent ridge endings and are known as singular regions.
The project will be proceeded by using Matching techniques
There are two types of Finger print Matching techniques
Minutiae Base
Image Based
MINUTIAE
In this project we are implementing the Minutiae matching technique
It is first necessary to apply several pre-processing steps to the original fingerprint image to produce consistent results
Such steps generally include
Binarization
Noise removal
Thinning
Binarization
Image binarization is the process of turning a grayscale image to a black and white image.
In a gray-scale image, a pixel can take on 256 different intensity values while each pixel is assigned to be either black or white in a black and white image.
This conversion from gray-scale to black and white is performed by applying a threshold value to the image.
A critical component in the binarization process is choosing a correct value for the threshold.
The threshold values used in this study were selected empirically by trial and error.
After binarization, another major pre-processing technique applied to the image is thinning, which reduces the thickness of all ridge lines
This thinning method to be done with
Block Filtering method attempts to preserve the outermost pixels along each ridge
This is done with the following steps.
Step One: ridge width reduction
This step involves applying a morphological process to the image to reduce the width of the ridges
Morphological is a means of changing a stem to adjust its meaning to fit its syntactic and communicational context
Two basic morphological processes are
Erosion
Dilation
Dilation A dilation process is used to thicken the area of the valleys in the fingerprint.
Erosion:
Erosion thins objects in a binary image (ridge)
In this project we are using the Dilation
Original gray level image Image found after applying valley dilation
Step Two: passage of block filter
The next step involves performing a pixel-by pixel scan for black pixels across the entire image
Note that in MATLAB, image rows are numbered in increasing order beginning with the very top of the image as row one.
Similarly, columns are numbered in increasing order beginning with the leftmost side of the image as column one
The left to right scan continues until it covers the entire image. Next, a similar scan is performed across the image from right to left beginning at the pixel in row one and the last column.
Step Three: removal of isolated noise
Step Four: scan combination
A value of two means that the pixel from each scan was white, while a value of zero indicates the pixel from each scan was black. Meanwhile, a value of one means that the pixel from one scan was black while the same pixel from the other scan was white.
As a result, the new matrix needs to be adjusted to represent a valid binary image containing only zeros and ones. Specifically, all zeros and ones are assigned a value of zero (black pixel), and all twos are assigned a value of one (white pixel).
Step Five: elimination of one pixel from two-by-two squares of black
Next, a new scan is conducted on the combined image to detect two-by-two blocks of black pixels which represent a location where a ridge has not been thinned to a one-pixel width. It is likely that some of these two-by two blocks were created by the combination of the previous scans. This problem can be compensated for by changing one pixel within the block from black to white, which reduces the width at that particular point from two pixels to one. At the same time, this process needs to be implemented in a manner that preserves the overall ridge structure.
This operation can be performed by analyzing the pixels touching each individual black pixel. Note that each black pixel touches the three other black pixels within the two-by-two block. Therefore, there are only five other pixels thatcontain useful information.
Step Six: removal of unwanted spurs
Final Noise Removal
MINUTIAE EXTRACTION
The minutiae information can be extracted and stored after the image pre-processing is complete. This information consists of the following for each minutia:
• Location within the image
• Orientation angle
• Type (termination or bifurcation)