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7/28/2019 Attendance Fingerprint Verification
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Automatic Attendance System using FingerprintVerification Technique
1. INTRODUCTION
In many institutions and organization the attendance is a very important factor
for various purposes and its one of the important criteria that is to follow for
students and organization employees. The previous approach in which manually
taking and maintains the attendance records was very inconvenient task. After
having these issues in mind we develop an automatic attendance system which
automates the whole process of taking attendance and maintaining it.
We already know about some commonly used biometric techniques are used for
objective identification and verification are like iris recognition, voice identification,
facial recognition, fingerprint identification, DNA recognition, hand geometry
recognition, signature recognition, and gait recognition. Biometrics techniques are
widely used in various areas like building security, forensic science, ATM, criminal
identification and passport control . In our proposed automatic attendance system
we uses fingerprint recognition technique for obtaining the attendance. The
fingerprint recognition is widely used for many other purposes and it is widely
popular technique . Fingerprint verification is very convenient and reliable way to
verify the persons Identity. It is believed that no two people have identical
fingerprint in this world, so the fingerprint verification and identification is most
popular way to verify the authenticity or identity of a person wherever the security
is a problematic question. The reason for popularity of fingerprint technique isuniqueness of person arises from his behavior; personal characteristics are like, for
instance uniqueness, which indicates that each and every fingerprint is unique,
different from one other. Universality, that means every person hold the individual
characteristics of fingerprint. Permanence, means that fingerprint are permanent,
are impossible to change or forgot, and can never be stolen. Collectability means
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that we can measure fingerprint quantitatively .
In present scenario, the various uses of fingerprint verification are widespread
like authentication to logon machine and others but still majorly for law
enforcement applications. There are a lot of expectations that the use of fingerprint
recognition will increase which is dependent of some factor involved like small
fingerprint capturing devices, fast computing hardware, and awareness on easy to
use methods for security [3]. This paper cover the topics on fingerprint verification,
algorithm and our proposed system, the details of pre-processing of fingerprint
image including enhancement, binarization, segmentation, extracting minutiae from
image, post processing and matching, experiment and its result.
Fingerprint Recognition
The Fingerprint is the feature pattern of one finger or an impression of friction
ridges found on inner surface of finger as shown in figure 1(a). Everyone in this
world has his own fingerprint with the permanent uniqueness. A fingerprint is made
up of ridges and furrows, which shows good similarities like parallelism and average
width . However the research conducting on fingerprint verification and
identification shows that we can distinguish fingerprint with the help of minutiae,which are the some abnormal points on the ridges. There are two type of the
termination of minutiae, immediate ending of ridges or a point where ridge ends
abruptly called ending or termination and the point on the ridge from which other
branch drives or a point from where ridge splits into two or more branches is known
as bifurcation .
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2. LITERATURE REVIEW
This section provides an account of published work by academic scholars and accredited
commentators on the subject of modern biometrics. The intent is to provide the reader with the
pros and cons of the knowledge and ideas established on the topic. The material selected for
research should reflect the overall goals outlined in the aims and objectives for this project.
Those overall goals are primarily intent on evaluating the current state of biometric technologies
and their potential. Therefore, with this in mind and taking into account the Ashbourne definition
of a modern biometric system, it is possible to discern the more relevant material for appraisal.
Much of this material tends to be recent, reflecting the most significant period for the
development of biometrics as a usable modern technology , ( c . 1990s to the present), with the
necessary overall historical context being provided in the main introduction. Maltoni et al , [A][1]
Bolle et al, [A][3] and Wikipedia , [B][4] are in agreement as to the list of general characteristics a
biometric must meet in order to provide high level performance, these include:
Universality , a characteristic of everyone.
Distinctiveness , any two persons should be sufficiently different.
Permanence , i.e. invariant with respect to matching, over time.
Collectabillity , biometric can be measured quantitatively.
Performance , achievable recognition accuracy, speed, robustness.
Acceptability , the extent to which people are willing to accept the system.
Circumvention , how easy it is to fool the system.
As a gauge to performance these characteristics provide the context for understanding common
biometric identifiers in relation to the key aspects mentioned above. In summary, it is hoped
that the review will reflect the overall research objective, provide insight as to the current
level/limits of knowledge and identifying controversies and areas of further research.
Moore, G, 2005 stated that the picture writing of a hand ridge patterns was discovered in Nova
Scotia. In ancient Babylon, fingerprints were used on clay tablets for business transaction and in
ancient China, thumbs prints were found on clay seals. In 14th century Persia, various official
government papers had fingerprints and one government official, a doctor, observed that no two
fingerprints were exactly alike.
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Year Descriptions
1686 - Malpighi In 1686, Marcello Malpighi, a professor of anatomy at the University of
Bologna, noted in his treatise; ridges, spirals and loops in fingerprints. He made no mention of
their value as a tool for individual identification. A layer of skin was named after him; "Malpighi"
layer, which is approximately 1.8mm thick.1823 - Purkinji In 1823, John Evangelist Purkinji, a professor of anatomy at the
University of Breslau, published his thesis discussing fingerprint patterns, but he too made no
mention of the value of fingerprints for personal identification.
1856 - Hershel The English first began using fingerprints in July of 1858, when Sir
William Herschel, Chief Magistrate of the Hooghly district in Jungipoor, India, first used
fingerprints on native contracts. Sir Herschel's private conviction that all fingerprints were
unique to the individual, as well as permanent throughout that individual's life, inspired him to
expand their use.1880 - Faulds During the 1870's, Dr. Henry Faulds, the British SurgeonSuperintendent
of Tsukiji Hospital in Tokyo, Japan, took up the study of "skin-furrows" after noticing finger
marks on specimens of "prehistoric" pottery. In 1880, Dr. Faulds published an article in the
Scientific Journal, "Nature" (nature). He discussed fingerprints as a means of personal
identification, and the use of printers ink as a method for obtaining such fingerprints.
1882 - Thompson In 1882, Gilbert Thompson of the U.S. Geological Survey in New Mexico
used his own fingerprints on a document to prevent forgery. This is the first known use of
fingerprints in the United States. 1888 - Galton Sir Francis Galton, a British anthropologist anda cousin of Charles Darwin, began his observations of fingerprints as a means of identification
in the 1880's.
1891 - Vucetich Juan Vucetich, an Argentine Police Official, began the first fingerprint files
based on Galton pattern types. At first, Vucetich included the Bertillon System with the files.
1892 Vucetich & Galton Juan Vucetich made the first criminal fingerprint identification in
1892. Sir Francis Galton published his book, "Fingerprints", establishing the individuality and
permanence of fingerprints. The book included the first classification system for fingerprints.
While he soon discovered that fingerprints offered no firm clues to an individual's intelligence or genetic history, he was able to scientifically prove what Herschel and Faulds already suspected:
that fingerprints do not change over the course of an individual's lifetime, and that no two
fingerprints are exactly the same. According to his calculations, the odds of two individual
fingerprints being the same were 1 in 64 billion. Galton identified the characteristics by which
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fingerprints can be identified. These same characteristics (minutia) are basically still in use
today, and are often referred to as Galton's Details.
1897 Haque & Bose On 12th June 1987, the Council of the Governor General of
India approved a committee report that fingerprints should be used for classification of criminal
records. Later that year, the Calcutta (now Kolkata) Anthropometric Bureau became the world'sfirst Fingerprint Bureau. Working in the Calcutta Anthropometric Bureau (before it became the
Fingerprint Bureau) were Azizul Haque and Hem Chandra Bose. Haque and Bose are the two
Indian fingerprint experts credited with primary development of the Henry System of fingerprint
classification (named for their supervisor, Edward Richard Henry). The Henry classification
system is still used in all English-speaking countries.
1901 Henry Introduction of fingerprints for criminal identification in England and
Wales, using Galton's observations and revised by Sir Edward Richard Henry. 1902 First
systematic use of fingerprints in the U.S. by the New York Civil Service Commission for testing.Dr. Henry P. DeForrest pioneers U.S. fingerprinting. 1903 The New York State Prison system
began the first systematic use of fingerprints in U.S. for criminals. 1904 The use of fingerprints
began in Leavenworth Federal Penitentiary in Kansas, and the St. Louis Police Department.
They were assisted by a Sergeant from Scotland Yard who had been on duty at the St. Louis
World's Fair Exposition guarding the British Display. Sometime after the St. Louis World's Fair,
the International Association of Chiefs of Police (IACP) created America's first national
fingerprint repository, called the National Bureau of Criminal Identification. 1905 U.S. Army
begins using fingerprints. U.S. Department of Justice forms the Bureau of Criminal Identificationin Washington, DC to provide a centralized reference collection of fingerprint cards. Two years
later the U.S. Navy started, and was joined the next year by the Marine Corp. During the next 25
years more and more law enforcement agencies join in the use of fingerprints as a means of
personal identification. Many of these agencies began sending copies of their fingerprint
cards to the National Bureau of Criminal Identification, which was established by the
International Association of Police Chiefs. 1907 U.S. Navy begins using fingerprints. U.S.
Department of Justice's Bureau of Criminal Identification moves to Leavenworth Federal
Penitentiary where it is staffed at least partially by inmates. 1908 U.S. Marin Corps begins usingfingerprints. 1918 Edmond Locard wrote that if 12 points (Galton's Details) were the same
between two fingerprints, it would suffice as a positive identification. Locard's 12 points seems
to have been based on an unscientific "improvement" over the eleven anthropometric
measurements (arm length, height, etc.) used to "identify" criminals before the adoption of
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fingerprints. 1924 In 1924, an act of congress established the Identification Division of the FBI.
The IACP's National Bureau of Criminal Identification and the US Justice Department's
Bureau of Criminal Identification consolidated to form the nucleus of the FBI fingerprint files.
1946 By 1946, the FBI had processed 100 million fingerprint cards in manually maintained files
and by 1971, 200 million cards. With the introduction of AFIS technology, the files were split intocomputerized criminal files and manually maintained civil files. 2005 The FBIs Integrated AFIS
(IAFIS) in Clarksburg, WV has more than 49 million individual computerized fingerprint records
for known criminals. Old paper fingerprint cards for the civil files are still manually maintained in
a warehouse facility (rented shopping center space) in Fairmont, WV, though most enlisted
military service member fingerprint cards received after 1990, and all military-related fingerprint
cards received after 19 May 2000, have now been computerized and can be searched internally
by the FBI. In some future build of IAFIS, the FBI may make such civil file AFIS searches
available to other federal crime laboratories. All US states and larger cities have their own AFISdatabases, each with a subset of fingerprint records that is not stored in any other database.
Thus, law enforcement fingerprint interface standards are very important to enable sharing
records and mutual searches for identifying criminals.
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3. PROJECT OVERVIEW
The main aim of this paper is to develop an accurate, fast and very efficient
automatic attendance system using fingerprint verification technique. We propose a
system in which fingerprint verification is done by using extraction of minutiae
technique and the system that automates the whole process of taking attendance,
Manually which is a laborious and troublesome work and waste a lot of time, with
its managing and maintaining the records for a period of time is also a burdensome
task. For this purpose we use fingerprint verification system using extraction of
minutiae techniques. The experimental result shows that our proposed system is
highly efficient in verification of user fingerprint.
Figure 2 shows our proposed automatic attendance system using fingerprintverification technique. A fingerprint is captured by user interface, which are likely to
be an optical, solid state or an ultrasound sensor. Generally, there are two
approaches are used for fingerprint verification system among them first one is
Minutiae based technique, in which minutiae is represented by ending or
termination and bifurcations. Other one is Image based method or matching
pattern, which take account of global feature of any fingerprint image. This method
is more useful then the first one as it solve some intractable problem of method
one, but this paper talk about the minutiae based representation of fingerprint. Thefingerprint verification can be defined as the system that confirm the authenticity of
one person by comparing his captured fingerprint templates against the stored
templates in database. One to one comparisons are conducted to identify the
person authenticity. After this if the authenticity of person is verified then system
signal true else false.
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Automatic attendance system architecture representing the stages of
preprocessing, extraction of minutiae and matching of minutiae
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4. FINGER PRINT RECOGNITION PROCESS
PREPROCESSING OF FINGERPRINT IMAGE
The pre-processing of a fingerprint image comprises of procedures like, first the
enhancement of image is done by histogram equalization and Fourier transform.
After this the process of binarization is done on the enhanced fingerprint image by
using locally adaptive method. This binarized fingerprint image is segmented by
using threshold or region of interest techniques.
A. Enhancement of Image
Since the fingerprint image are acquired from high quality sensors but the
perfection of image quality is questionable. So the enhancement of fingerprint
image is done to improve image quality, without even knowing the source of
degradation, with this it increase the contrast between ridges and furrows and
connect the broken points of ridges. Enhancement of image is first done by
histogram equalization, which performed on input image based on calculated
probability density function, with the help of which noise is prevented from being
amplified and visualization effects are enhanced. After this Fourier transform isapplied on image small processing blocks [8, 16] (32 by 32 pixel) according to
where u=0, 1, 2....31, v=0, 1, 2........31.
To enhance the block by its dominant frequency we multiply FFT of block by its
magnitude a set of times. The original magnitude FFT = abs(F(u, v)) = |F(u, v)|.
We can get an enhanced block, by using formula
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where F-1 (F(u, v)) is calculated as:
for x = 0, 1, 2, ..., 31 and y = 0, 1, 2, ..., 31.
B. Binarization of Image
The Binarization of fingerprint image is to convert an image up to 256 gray levels
to white and black image. A locally adaptive binarization method is used in which
image binarization is done by choose mean intensity value or threshold value and
classify all pixels with or above threshold value as white and other pixels as black.
C. Segmentation of Image
Separating the fingerprint area from the background is always useful to avoid
extraction of noisy areas of fingerprint [17]. The segmentation of image is to
distinguish image object from the background. Only the region of interest is useful
for recognition, so image area without effective ridges and furrows are discarded
because it does not holds any important information and remaining effective area is
sketched out since minutiae in bound region are confusing with the initial minutiae
when image were generated. To extract region of interest we use two
methodologies, first is block direction estimation and direction variety check [9, 18]and second is extracting by morphological operations. Two morphological
operations are chosen OPEN, which remove expand image with removing of noise
and CLOSE, which shrink image with eliminating small cavities. The interest
fingerprint image area is found by subtraction of closed area from opened area.
RECOGNITION OF MINUTIAE
The recognition of minutiae is based on the extraction of minutiae in which binary
image obtained by binarization
process are submitted to fingerprint ridge thinning stage and marking of minutiae.
A. Thinning
Ridge Thinning or thinning is a process of reducing the width of the ridges in
fingerprint image to one pixel wide [10,11, 19]. Can say like this is to eliminate the
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redundant pixel of ridges till the ridges point is one pixel wide and it should thinned
to its central pixel. The minutiae points, which have pixel value one is ending and
more than two is bifurcation. Thinning algorithm is classified by iterative and non-
iterative algorithms which is a faster approach [10]. The purpose of thinning is to
preserve the fingerprint minutiae shape while eliminating extra information and
performed because of morphological filtering of segmented image, removal of
unwanted branches, and smoothing up the result central path. This algorithm
follows the three simple rules are first to remove the unwanted edge points, adding
new edge points and shift edge points to the new location. The Algorithm is: The
rules [12, 20] are here according to the number of edge point neighbors which an
edge point has, and with help of this algorithm erroneous pixels are removed.
STEP 1: An edge point has zero neighbors, then remove the edge point.
STEP 2: An edge point has one neighbour, then start search for the neighbour with
maximum edge response to continue the edge, fill the gaps. (A edge can be filled
by maximum of three pixel.)
STEP 3: An edge point has two neighbours, and then there are three cases,
If point is sticking out of an otherwise straight line, then compare its edge
response to the corresponding point.
If the point is adjoining a diagonal edge then remove it. Else, the point is valid edge point.
STEP 4: An edge point has more than two neighbours, and then if point is not
having any link between multiple edges then thin the edge in logical consistent
way. The figure 3 is showing the Algorithm cases of number of
edge points neighbour.
B. Enhanced Thinning
The fingerprint ridge thinning is to eliminate the redundant pixel of ridge, till the
ridges one pixel wide, but this not always happens. There are still some locations
where skeleton has two or more pixel width, some extra or erroneous pixel. An
extra or erroneous pixel is one with more than four connected neighbour, it can
destroy the integrity of bridges and spurs, miss detect and exchange type of
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minutiae points. So before extraction of minutiae we need to eliminate those extra
or erroneous pixels [4], for this purpose we use Enhanced Thinning Algorithm.
C. Marking of Minutiae
After Thinning of fingerprint image the important and next step is marking all
minutiae points. The maximum number of minutiae detected, increased the
probability of accurate results. The crossing number (CN) concept is widely use for
this purpose. Together with marking all thinned ridge and fingerprint image are
labeled with a unique ID for further process of matching and this labeling is done by
using morphological operation BWLABEL [4].
POST PROCESSING OF MINUTIAE
After the pre-processing stage on the binary and skeleton image, we extract almost
all minutiae from fingerprint skeleton using various method including Rutovitz
crossing numbers [14], due to various noises in fingerprint image it unable to heal
the image totally, like false ridge breaks, ridge cross connection and those
extraction algorithm produces a large number of spurious minutiae [11] such as
break, spur, merge, triangle, multiple break, ladder, lake, island, wrinkle, dot as
show in figure 4. So for accurate fingerprint verification post processing stage isvery necessary as it helps in differentiating spurious minutiae from genuine ones.
As we able to eliminate more unwanted or spurious minutiae chances of getting
better matching performance increase with the matching time will decreases.
For various types of false minutiae as in figure 4 shown, dot, spur, lake, island are
removed by pre processing algorithms, but bridge, triangle, ladder, wrinkle are not
which also known as H-points. If we able to remove H-points of image so we able to
eliminate most of spurious minutiae point. The process of eliminating the false
minutiae are consist of following steps first extract minutiae set, then remove short
breaks, after that removal of spurs if any, then removal of Hpoints [13] , after that
remove close minutiae and border minutiae and we get the true minutiae set. The
elimination process of false minutiae is already started by applying thinning
algorithms as shown in Figure 1 (extraction of minutiae steps), by applying the
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threshold concept and various thinning algorithms we already able to remove the
short breaks and spurs. The post processing starts from the next step is removals
of H-Points, where H-Points are detected and eliminated. To recognize the H-Points
we follow some rules like, the point of intersection should lie between the two
ending or two bifurcation points, the distance between bridge midpoint and break
midpoint should be in a threshold limit and then we remove minutiae that are very
close to each other or the minutiae points which are within the certain distance
threshold from image border [14]. After preprocessing, a large percentage of extra
or spurious minutiae are deleted and we can treat rest of the minutiae points as
genuine and which can be used for fingerprint matching purpose.
MATCHING OF MINUTIAE
Matching of minutiae is that when we have two set of minutiae of fingerprint image
and using a algorithm we determines whether the give set of minutiae is from the
same finger or not. There are some matching techniques as correlation based
matching in which two fingerprint images are superimposed and finding out the
correlation between corresponding pixel, Ridge feature based matching in which
feature extracted are compared to extracted ridge pattern and the other one is
Minutia based matching technique [3] in which minutiae extracted from twofingerprint and stored as sets of point in two dimensional plane. We described this
technique here
a) The stage of Alignment : In this stage anyone minutiae is choose from each
image then calculate the similarity of the two ridges associated with the two
referenced minutiae points [9]. If the threshold value is smaller than similarity
then transform each set of minutiae to new coordinate system whose origin is at
referenced point and x-axis is coincident with the direction of referenced point.
b) The stage of Matching: After deriving the set of transformed minutiae points,
an algorithm is used for matching the pairs, assuming that minutiae have nearly
identical direction and position.
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5. Tools Used
.Net Framework 3.5
C#
SQL Server 2005
.Net Framework 3.5
The main features of .Net Framework 3.5 are as below, which prompted us to
choose this platform to develop the application of this complex nature.
Interoperability
Because computer systems commonly require interaction between newer and
older applications, the .NET Framework provides means to access
functionality implemented in newer and older programs that execute outsidethe .NET environment. Access to COM components is provided in the
System.Runtime.InteropServices and System.EnterpriseServices namespaces
of the framework; access to other functionality is achieved using
the P/Invoke feature.
Common Language Runtime engine
The Common Language Runtime (CLR) serves as the execution engine of the
.NET Framework. All .NET programs execute under the supervision of the
CLR, guaranteeing certain properties and behaviors in the areas of memory
management, security, and exception handling.
Language independence
The .NET Framework introduces a Common Type System, or CTS. The
CTS specification defines all possible data types and programming constructs
supported by the CLR and how they may or may not interact with each other
conforming to the Common Language Infrastructure (CLI) specification.
Because of this feature, the .NET Framework supports the exchange of typesand object instances between libraries and applications written using any
conforming .NET language.
Base Class Library
The Base Class Library (BCL), part of the Framework Class Library (FCL), is a
library of functionality available to all languages using the .NET Framework.
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The BCL provides classes that encapsulate a number of common functions,
including file reading and writing, graphic
rendering, database interaction, XML document manipulation, and so on. It
consists of classes, interfaces of reusable types that integrates with
CLR(Common Language Runtime).
Simplified deployment
The .NET Framework includes design features and tools which help manage
the installation of computer software to ensure it does not interfere with
previously installed software, and it conforms to security requirements.
Security
The design addresses some of the vulnerabilities, such as buffer overflows,
which have been exploited by malicious software. Additionally, .NET providesa common security model for all applications.
Portability
While Microsoft has never implemented the full framework on any system
except Microsoft Windows, it has engineered the framework to be platform-
agnostic , [3] and cross-platform implementations are available for other
operating systems (see Silverlight and the Alternative
implementations section below). Microsoft submitted the specifications for
the Common Language Infrastructure(which includes the core class
libraries, Common Type System, and the Common Intermediate
Language), the C# language, and the C++/CLI language to both and the,
making them available as official standards. This makes it possible for third
parties to create compatible implementations of the framework and its
languages on other platforms.
C#
By design, C# is the programming language that most directly reflects the
underlying Common Language Infrastructure (CLI).Most of its intrinsic types
correspond to value-types implemented by the CLI framework. However, the
language specification does not state the code generation requirements of the
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compiler: that is, it does not state that a C# compiler must target a Common
Language Runtime, or generate Common Intermediate Language (CIL), or generate
any other specific format. Theoretically, a C# compiler could generate machine
code like traditional compilers of C++ or Fortran. Some notable features of C# that
distinguish it from C and C++ (and Java, where noted) are:
C# supports strongly typed implicit variable declarations with the keyword var ,
and implicitly typed arrays with the keyword new[] followed by a collection
initializer.
Meta programming via C# attributes is part of the language. Many of these
attributes duplicate the functionality of GCC's and VisualC++'s platform-
dependent preprocessor directives.
Like C++, and unlike Java, C# programmers must use the keyword virtual to
allow methods to be overridden by subclasses.
Extension methods in C# allow programmers to use static methods as if they
were methods from a class's method table, allowing programmers to add
methods to an object that they feel should exist on that object and its
derivatives.
The type dynamic allows for run-time method binding, allowing for JavaScript
like method calls and run-time object composition. C# has strongly typed and verbose function pointer support via the
keyword delegate .
Like the QT framework's pseudo-C++ signal and slot , C# has semantics
specifically surrounding publish-subscribe style events, though C# uses
delegates to do so.
C# offers Java like syncronized method calls, via the
attribute [ MethodImpl ( MethodImplOptions . Synchronized ) , and has support for
mutually-exclusive locks via the keyword lock .
The C# languages does not allow for global variables or functions. All methods
and members must be declared within classes. Static members of public classes
can substitute for global variables and functions.
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Local variables cannot shadow variables of the enclosing block, unlike C and
C++.
A C# namespace provides the same level of code isolation as a
Java package or a C++ namespace , with very similar rules and features to
a package .
C# supports a strict Boolean data type, bool . Statements that take conditions,
such as while and if , require an expression of a type that implements
the true operator, such as the boolean type. While C++ also has a boolean
type, it can be freely converted to and from integers, and expressions such
as if ( a ) require only that a is convertible to bool, allowing a to be an int, or a
pointer. C# disallows this "integer meaning true or false" approach, on the
grounds that forcing programmers to use expressions that returnexactly bool can prevent certain types of common programming mistakes in C
or C++ such as if (a = b) (use of assignment =instead of equality ==).
In C#, memory address pointers can only be used within blocks specifically
marked as unsafe , and programs with unsafe code need appropriate
permissions to run. Most object access is done through safe object references,
which always either point to a "live" object or have the well-defined null value;
it is impossible to obtain a reference to a "dead" object (one that has been
garbage collected), or to a random block of memory. An unsafe pointer can
point to an instance of a value-type, array, string, or a block of memory
allocated on a stack. Code that is not marked as unsafe can still store and
manipulate pointers through theSystem . IntPtr type, but it cannot dereference
them.
Managed memory cannot be explicitly freed; instead, it is automatically garbage
collected. Garbage collection addresses the problem of memory leaks by freeing
the programmer of responsibility for releasing memory that is no longer needed. In addition to the try ... catch construct to handle exceptions, C# has
a try ... finally construct to guarantee execution of the code in the finally block,
whether an exception occurs or not.
Multiple inheritance is not supported, although a class can implement any
number of interfaces. This was a design decision by the language's lead
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architect to avoid complication and simplify architectural requirements
throughout CLI. When implementing multiple interfaces that contain a method
with the same signature, C# allows the programmer to implement each method
depending on which interface that method is being called through, or, like Java,
allows the programmer to implement the method once and have that be the
single invocation on a call through any of the classes interfaces.
C#, unlike Java, supports operator overloading. Only the most commonly
overloaded operators in C++ may be overloaded in C#.
C# is more type safe than C++. The only implicit conversions by default are
those that are considered safe, such as widening of integers. This is enforced at
compile-time, during JIT, and, in some cases, at runtime. No implicit
conversions occur between booleans and integers, nor between enumerationmembers and integers (except for literal 0, which can be implicitly converted to
any enumerated type). Any user-defined conversion must be explicitly marked
as explicit or implicit, unlike C++ copy constructors and conversion operators,
which are both implicit by default.
C# has explicit support for covariance and contra variance, unlike Java which as
neither, and unlike C++ which has some degree of support for contra variance
simply through the semantics of return types on virtual methods.
Enumeration members are placed in their own scope.
C# provides properties as syntactic sugar for a common pattern in which a pair
of methods, accessor (getter) and mutator (setter)encapsulate operations on a
single attribute of a class. No redundant method signatures for the getter/setter
implementations need be written, and the property may be accessed using
attribute syntax rather than more verbose method calls.
Checked exceptions are not present in C# (in contrast to Java). This has been a
conscious decision based on the issues of scalability and versionability. Though primarily an imperative language, since C# 3.0 it supports functional
programming techniques through first-class function objects and lambda
expressions.
SQL Server 2005
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SQL Server 2005 (formerly codenamed "Yukon") was released in October 2005. It
included native support for managing XML data, in addition to relational data. For
this purpose, it defined an xml data type that could be used either as a data type in
database columns or as literals in queries. XML columns can be associated
with XSD schemas; XML data being stored is verified against the schema. XML is
converted to an internal binary data type before being stored in the database.
Specialized indexing methods were made available for XML data. XML data is
queried using XQuery; SQL Server 2005 added some extensions to the T-
SQL language to allow embedding XQuery queries in T-SQL. In addition, it also
defines a new extension to XQuery, called XML DML, that allows query-based
modifications to XML data. SQL Server 2005 also allows a database server to be
exposed over web services using Tabular Data Stream(TDS) packets encapsulatedwithin SOAP (protocol) requests. When the data is accessed over web services,
results are returned as XML.
Common Language Runtime (CLR) integration was introduced with this version,
enabling one to write SQL code as Managed Code by the CLR. For relational data, T-
SQL has been augmented with error handling features (try/catch) and support for
recursive queries with CTEs (Common Table Expressions). SQL Server 2005 has
also been enhanced with new indexing algorithms, syntax and better error recovery
systems. Data pages are check summed for better error resiliency, and optimistic
concurrency support has been added for better performance. Permissions and
access control have been made more granular and the query processor handles
concurrent execution of queries in a more efficient way. Partitions on tables and
indexes are supported natively, so scaling out a database onto a cluster is easier.
SQL CLR was introduced with SQL Server 2005 to let it integrate with the .NET
Framework.
SQL Server 2005 introduced "MARS" (Multiple Active Results Sets), a method of allowing usage of database connections for multiple purposes.
SQL Server 2005 introduced DMVs (Dynamic Management Views), which are
specialized views and functions that return server state information that can be
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used to monitor the health of a server instance, diagnose problems, and tune
performance.
Service Pack 1 (SP1) of SQL Server 2005 introduced Database Mirroring, a high
availability option that provides redundancy and failover capabilities at the database
level. Failover can be performed manually or can be configured for automatic
failover. Automatic failover requires a witness partner and an operating mode of
synchronous (also known as high-safety or full safety).
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6. APPLICATIONS
The application of fingerprint identification system are :
Finance, insurance, securities: Financial safe management, important systemand department staff authorized management, fingerprint drawing money
business, credit card of fingerprint identification, the securities and exchange
the identification, insurance beneficiary identification. Information industry.
Computer application system identification upgraded like this way:
(fingerprints instead of password), internet electronic trading system
identification, smart card password exchange (fingerprints instead of
password), administrator identification for communication and network
equipment (switch, internet, etc.). Security industry: Fingerprint access
control system, fingerprint door lock, fingerprint car lock, building fingerprint
door lock, treasury and guns warehouse fingerprint access control and so
on.
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7. LIMITATIONS
One of the open issues in ngerprint veri cation is the lack of robustness against
image quality degradation [80, 2]. The performance of a ngerprint recognition
system
is heavily affected by ngerprint image quality. Several factors determine the
q uality of a ngerprint image: skin conditions (e.g., dryness, wetness, dirtiness,
temporary or permanent cuts and bruises), sensor conditions (e.g., dirtiness, noise,
size),user cooperation, etc. Some of these factors cannot be avoided and some of
themvary along time. Poor quality images result in spurious and missed features,
thusdegrading the performance of the overall system. Therefore, it is very
important for a ngerprint recognition system to estimate the quality and validity of
the captured ngerprint images. We can either reject the degraded images oradjust someof the steps of the recognition system based on the estimated quality.
Several algorithms for automatic ngerprint image quality assessment have been
proposedin literature [2]. Also, the benets of incorporating automatic quality
measures in ngerprint verication have been shown in recent studies [28, 6, 32,
5].A successful approach to enhance the performance of a ngerprint verication
system is to combine the results of different recognition algorithms. A number of
simple fusion rules and complex trained fusion rules have been proposed in
literature [11, 49, 81]. Examples for combining minutia- and texture-based
approachesare to be found in [75, 61, 28]. Also, a comprehensive study of the
combination ofdifferent ngerprint recognition systems is done in [30]. However, it
has been foundthat simple fusion approaches are not always outperformed by more
complex fusionapproaches, calling for further studies of the subject.
Another recent issue in ngerprint recognition is the use of multiple sensors, either
for sensor fusion [60] or for sensor interoperability [74, 7]. Fusion of sensors offers
some important potentialities [60]: a) the overall performance can be improvedsubstantially, b) population coverage can be improved by reducing enrollment and
verication failures, and c) it may naturally resist spoong attempts against
biometric systems. Regarding sensor interoperability, most biometric systems are
designed under the assumption that the data to be compared is obtained uniquely
and the same for every sensor, thus being restricted in their ability to match or
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compare biometric data originating from different sensors in practice. As a result,
changing thesensor may affect the performance of the system. Recent progress has
been madein the development of common data exchange formats to facilitate the
exchange of feature sets between vendors [19]. However, little effort has been
invested in the development of algorithms to alleviate the problem of sensor
interoperability. Someapproaches to handle this problem are given in [74], one
example of which is thenormalization of raw data and extracted features. As a
future remark, interoperability scenarios should also be included in vendor and
algorithm competitions, as donein [37].
Due to the low cost and reduced size of new ngerprint sensors, several devices
in daily use already include embedded ngerprint sensors (e.g., mobile telephones,
PC peripherals, PDAs, etc.) However, using small-area sensors implies having lessinformation available from a ngerprint and little overlap between different
acquisitions of the same nger, which has great impact on the performance of the
recognition system [59]. Some ngerprint sensors are equipped with mechan ical
guides in order to constrain the nger position. Another alternative is to perform
several acquisitions of a nger, gathering (partially) overlapping information during
the enrollment, and reconstruct a full ngerprint image. In spite of the numerous
advantages of biometric systems, they are also vulnerable to attacks [82]. Recent
studies have shown the vulnerability of ngerprint systems to fake ngerprints [35,72, 71, 63]. Surprisingly, fake biometric input to the sensor is shown to be quite
successful. Aliveness detection could be a solution and it is receiving great attention
[26, 78, 8]. It has also been shown that the matching score is a valuable piece of
information for the attacker[82, 73, 62]. Using the feedback provided by this score,
signals in the channels of the verication systemcan be modied iteratively and the
system is compromised in a number of iterations. A solution could be given by
concealing the matching score and just releasing an acceptance/rejection decision,
but this may not be suitable in certain biometric systems [82]. With the advances in
ngerprint sensing technology, new high resolution sensors are able to acquire
ridge pores and even perspiration activities of the pores [40, 21]. These features
can provide additional disc riminative information to existing ngerprint recognition
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systems. In addition, acquiring perspiration activities of the pores can be used to
detect spoong attacks.
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8. CONCLUSION
This paper introduces the efficient automatic attendance system, by using minutiae
based fingerprint technique. We use the methods which are simple, effective and
accurate to do the faster execution of enhancement and thinning algorithm of
fingerprint image.
In addition, we examine the experimentally determined constant K during t he
enhancement of image with using
Fourier Transform, by which we able to differentiate the enhanced quality of image
that can lead to the best verification of extracted minutiae of image. The
performance evaluation of proposed system is done by using FVC 2000 database
(500 images) [21] and the used time taken for verification was very less and
verification rate is higher and accuracy is near about 92%.
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