ABSTRACT This project aims at introducing biometric capable technology for use in automating the entire examination registration 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 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 1
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ABSTRACT
This project aims at introducing biometric capable technology for use in
automating the entire examination registration 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 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’s
biometric exam registration 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.
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CHAPTER ONE
1.1 Introduction
1.1 Background of Study
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, 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.2
1.2 Objective of the Project
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. The main objective this work is to build a
biometric exam registration and verification system based on the finger print
technology. The system will be capable of taking samples of the fingerprint of
students – a process called scanning; it will also process and store it in the
database alongside with the student’s passport photograph. During
examination every student is required to verify his/her identity before
granted access to the exam hall.
1.3 Justification for Study
The application in an educational institute is worth noting because of the
benefits it brings along with it. The fingerprint recognition and verification
technique can easily replace manual registration and save time wasted on
calling out roll numbers in the class during exams. A fingerprint detecting
device needs to be placed in the exam hall and students would be made to
swipe their finger over the sensor so as to verify their identity. The database
would contain all the fingerprints beforehand. So, the moment a finger would
be swiped, a check would be carried out with the existing database and the
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corresponding student would get a present mark on his attendance record
maintained in a server.
The transfer of the fingerprint from the device to the server can be carried
out wirelessly using certain wireless adapters which can together form a
wireless network in a short range and carry out the verification process. The
communication channel needs to be secured and should be kept free from
interference as far as possible. For further security of the entire system and
to detect illegal activities, a security camera can be installed to keep track of
the enrollments made in the classroom.
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CHAPTER TWO
2.0 History of Fingerprinting
There are records of fingerprints being taken many centuries ago, although
they weren't nearly as sophisticated as they are today. The ancient
Babylonians pressed the tips of their fingertips into clay to record business
transactions. The Chinese used ink-on-paper finger impressions for business
and to help identify their children.
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Fig 2.0: Traditional fingerprinting required careful analysis to
match prints. George Skadding/Time Life Pictures/Getty Images
However, fingerprints weren't used as a method for identifying criminals
until the 19th century. In 1858, an Englishman named Sir William Herschel
was working as the Chief Magistrate of the Hooghly district in Jungipoor,
India. In order to reduce fraud, he had the residents record their fingerprints
when signing business documents.
A few years later, Scottish doctor Henry Faulds was working in Japan when
he discovered fingerprints left by artists on ancient pieces of clay. This
finding inspired him to begin investigating fingerprints. In 1880, Faulds
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wrote to his cousin, the famed naturalist Charles Darwin, and asked for help
with developing a fingerprint classification system. Darwin declined, but
forwarded the letter to his cousin, Sir Francis Galton.
Galton was a eugenicist who collected measurements on people around the
world to determine how traits were inherited from one generation to the
next. He began collecting fingerprints and eventually gathered some 8,000
different samples to analyze. In 1892, he published a book called
"Fingerprints," in which he outlined a fingerprint classification system -- the
first in existence. The system was based on patterns of arches, loops and
whorls.
Meanwhile, a French law enforcement official named Alphonse Bertillon was
developing his own system for identifying criminals. Bertillonage (or
anthropometry) was a method of measuring heads, feet and other
distinguishing body parts. These "spoken portraits" enabled police in
different locations to apprehend suspects based on specific physical
characteristics. The British Indian police adopted this system in the 1890s.
Around the same time, Juan Vucetich, a police officer in Buenos Aires,
Argentina, was developing his own variation of a fingerprinting system. In
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1892, Vucetich was called in to assist with the investigation of two boys
murdered in Necochea, a village near Buenos Aires. Suspicion had fallen
initially on a man named Velasquez, a love interest of the boys' mother,
Francisca Rojas. But when Vucetich compared fingerprints found at the
murder scene to those of both Velasquez and Rojas, they matched Rojas'
exactly. She confessed to the crime. This was the first time fingerprints had
been used in a criminal investigation. Vucetich called his system comparative
dactyloscopy. It's still used in many Spanish-speaking countries.
Sir Edward Henry, commissioner of the Metropolitan Police of London, soon
became interested in using fingerprints to nab criminals. In 1896, he added to
Galton's technique, creating his own classification system based on the
direction, flow, pattern and other characteristics of the friction ridges in
fingerprints. Examiners would turn these characteristics into equations and
classifications that could distinguish one person's print from another's. The
Henry Classification System replaced the Bertillonage system as the primary
method of fingerprint classification throughout most of the world.
In 1901, Scotland Yard established its first Fingerprint Bureau. The following
year, fingerprints were presented as evidence for the first time in English
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courts. In 1903, the New York state prisons adopted the use of fingerprints,
followed later by the FBI.
2.1 Modern Fingerprinting Techniques
The Henry system finally enabled law enforcement officials to classify and
identify individual fingerprints. Unfortunately, the system was very
cumbersome. When fingerprints came in, detectives would have to compare
them manually with the fingerprints on file for a specific criminal (that's if
the person even had a record). The process would take hours or even days
and didn't always produce a match. By the 1970s, computers were in
existence, and the FBI knew it had to automate the process of classifying,
searching for and matching fingerprints. The Japanese National Police Agency
paved the way for this automation, establishing the first electronic
fingerprint matching system in the 1980s. Their Automated Fingerprint
Identification Systems (AFIS), eventually enabled law enforcement officials
around the world to cross-check a print with millions of fingerprint records
almost instantaneously.
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Fig 2.2 A background and identity check fingerprint capture machine.
AFIS collects digital fingerprints with sensors. Computer software then looks
for patterns and minutiae points (based on Sir Edward Henry's system) to
find the best match in its database. The first AFIS system in the U.S. was
speedier than previous manual systems. However, there was no coordination
between different agencies. Because many local, state and federal law
enforcement departments weren't connected to the same AFIS system, they
couldn't share information. That meant that if a man was arrested in Phoenix,
Ariz. and his prints were on file at a police station in Duluth, Minn., there
might have been no way for the Arizona police officers to find the fingerprint
record. That changed in 1999, with the introduction of Integrated AFIS
(IAFIS). This system is maintained by the FBI's Criminal Justice Information
Services Division. It can categorize, search and retrieve fingerprints from 10
virtually anywhere in the country in as little as 30 minutes. It also includes
mug shots and criminal histories on some 47 million people. IAFIS allows
local, state and federal law enforcement agencies to have access to the same
huge database of information. The IAFIS system operates 24 hours a day, 365
days a year. But IAFIS isn't just used for criminal checks. It also collects
fingerprints for employment, licenses and social services programs (such as
homeless shelters). When all of these uses are taken together, about one out
of every six people in this country has a fingerprint record on IAFIS.
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CHAPTER THREE
3.0 RESEARCH METHODOLOGY
Basically, the research on this work was done both on the internet and on
various computer and electrical/electronic textbooks including also some
other electronic circuit designing book. The fingerprint module was sourced
from online market, and after its arrival, the programing commenced. A
computer system was assembled to use in storing the database. More detail
of the work, its principle of operation and implementation are described
below.
3.1 Fingerprint Recognition
Once the fingerprint is captured, the next step is the recognition procedure.
The recognition procedure can be broadly sub grouped into
Fingerprint Identification and
Fingerprint Verification
Fingerprint identification refers to specifying one’s identity based on his
fingerprints. The fingerprints are captured without any information about
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the identity of the person. It is then matched across a database containing
numerous fingerprints. The identity is only retrieved when a match is found
with one existing in the database. So, this is a case of one-to-n matching
where one capture is compared to several others. This is widely used for
criminal cases.
Fingerprint verification is different from identification in a way that the
person’s identity is stored along with the fingerprint in a database. On
enrolling the fingerprint, the real time capture will retrieve back the identity
of the person. This is however a one-to-one matching. This is used in
offices like passport offices etc. where the identity of a person has to be
checked with the one provided at a previous stage.
Fig 3.1: Verification Vs Identification
Irrespective of the procedure carried out, the fingerprint recognition has to
be such that the fingerprint is well- represented and retains its uniqueness
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during the process. In the following pages, an approach to fingerprint
recognition has been discussed that will deal with the representation of the
same.
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3.2 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.
3.3 FINGERPRINT IMAGE PROCESSINGThe fingerprint image is processed through a three step procedure. The
image undergoes pre-processing, minutia extraction and post-processing.
The three stages involve different steps and procedures which need to be
discussed in detail.
Pre-processing The pre-processing stage makes use of image
enhancement, image binarization and image segmentation.
3.4 Image Enhancement
Image enhancement is necessary to make the image clearer for further
operations. The fingerprint images obtained from sensors are not likely
to be of perfect quality. Hence, enhancement methods are used for
making the contrast between ridges and furrows higher and for
maintaining continuity among the false broken points of ridges, which
prove to ensure a higher accuracy for recognition of fingerprint. Generally
two types of procedures are adopted for image enhancement:
1) Histogram Equalization;
2) Fourier Transform.
Histogram Equalization
Histogram equalization is responsible for expanding the pixel
distribution of an image in order to increase perceptional improvement.
The pictorial description is given below. The fingerprint initially has a
bimodal type histogram as shown in fig 3.2. After histogram equalization is
carried out, the image occupies the entire range from zero to 255,
enhancing the visualization effect in the process.
Figure 3.2: (a)Fingerprint with original histogram
(b)After histogram equalization
Figure 3.3 Effect of Histogram equalization
Original Image Enhanced Image
3.3 Using
Fourier
Tansform
In this process of enhancement the image is divided into small processing
blocks (32 x 32 pixels) and Fourier transform is performed.
The function is as
follows:
For u= 0,1,2, … ,31 v=0,1,2, ….,31
For enhancing a particular block by its dominant frequencies, the FFT os
the block is multiplied by its magnitude a few times. Where the magnitude
of the FFT is given by abs F(u,v) = |F(u,v)|.
The enhanced block can be obtained as per
(2) , where the inverse of (F(u,v)) is
found by:
(3)
for x = 0, 1, 2, ..., 31 & y = 0, 1, 2, ..., 31 .
The k is a constant whose value has been experimentally found. Here, k is
chosen as 0.45. When k is higher, the ridges appear improved, since the
holes in the ridges are filled up, but at the same time a very high value