13-10-2012, 04:30 PM
WIRELESS FINGERPRINT BASED STUDENT ATTENDANCE SYSTEM
WIRELESS FINGERPRINT.pdf (Size: 2.82 MB / Downloads: 207)
Abstract
Our B. Tech. Project aims at introducing biometric capable technology for use in automating the entire attendance system for the students pursuing courses at an educational institute. The goal can be disintegrated into finer sub-targets; fingerprint capture & transfer, fingerprint image processing and wireless transfer of data in a server-client system. For each sub-task, various methods from literature are analyzed. From the study of the entire process, an integrated approach is proposed.
Biometrics based technologies are supposed to be very efficient personal identifiers as they can keep track of characteristics believed to be unique to each person. Among these technologies, Fingerprint recognition is universally applied. It extracts minutia- based features from scanned images of fingerprints made by the different ridges on the fingertips. The student attendance system is very relevant in an institute like ours since it aims at eliminating all the hassles of roll calling and malpractice and promises a full-proof as well as reliable technique of keeping records of student’s attendance.
Introduction
The human body has the privilege of having features that are unique and exclusive to each individual. This exclusivity and unique characteristic has led to the field of biometrics and its application in ensuring security in various fields. Biometrics has gained popularity and has proved itself to be a reliable mode of ensuring privacy, maintaining security and identifying individuals. It has wide acceptance throughout the globe and now is being used at places like airports, hospitals, schools, colleges, corporate offices etc.
Biometrics is the very study of identifying a person by his/her physical traits that are inherent and unique to only the person concerned. Biometric measurement and assessment include fingerprint verification, iris recognition, palm geometry, face recognition etc. The above mentioned techniques work with different levels of functionality and accuracy.
Accuracy and reliability are the two most important parameters when it comes to biometric applications. Fingerprint verification is one of the oldest known biometric techniques known but still is the most widely used because of its simplicity and good levels of accuracy. It’s a well known fact that every human being is born with a different pattern on the fingers and this feature is exploited to identify and differentiate between two different persons.
FINGERPRINT
What is a fingerprint?
A fingerprint, as the name suggests is the print or the impression made by our finger because of the patterns formed on the skin of our palms and fingers since birth. With age, these marks get prominent but the pattern and the structures present in those fine lines do not undergo any change. For their permanence and unique nature, they have been used since long in criminal and forensic cases.
Shown below, is a fingerprint pattern obtained from an optical sensor. The figure shows faint and dark lines emerging from a particular point and spiraling around it all over the finger.
Every fingerprint consists of ridges and furrows. These ridges and furrows are known to show good similarities but when it comes to identifying a person or distinguishing between two different prints, these do not prove efficient enough. Research shows that fingerprints are not distinguished by ridges and furrows but by Minutia. Minutia refers to some abnormalities in a ridge, which shall be discussed in detail in the following pages.
Approach to fingerprint recognition
The approach that we have concentrated on in recognition of the fingerprints is the minutia based approach. In this approach the ridge bifurcations and terminations are taken into consideration for analyzing each fingerprint. The representation is based on these local features.
The scanner system uses highly complex algorithms to recognize and analyze the minutia. The basic idea is to measure the relative portion of minutia. Simply, it can be thought of as considering the various shapes formed by the minutia when straight lines are drawn between them or when the entire image is divided into matrix of square sized cells. If two fingerprints have the same set of ridge endings and bifurcations forming the same shape with the same dimension, there’ s a huge likelihood that they are of the same fingerprint.
So, to find a match the scanner system has to find a sufficient number of minutia patterns that the two prints have in common, the exact number being decided by the scanner programming.
Minutia marking
This follows the ridge thinning process. The mechanism behind the minutia marking process is described as follows.
For every 3x3 window, if the pixel at the middle is one and has exactly three single-value neighbors, then the pixel is a ridge branch. If the pixel at the middle is 1 and has only one single-valued neighbor, then it means the central pixel is ridge ending.
The mean ridge width D is calculated at this point. The mean inter-ridge width is the mean distance between two nearby ridges. The method to approximate the D is easy. A row of the thinned ridge is scanned and the pixels with value one re summed up. Then the row length is divided with the summation above to get inter ridge width. For better reults, such row scans are performed several times and column scans too are conducted. Finally the mean of all the widths are calculated to get the D.