FINGERPRINT BASED ATTENDANCE SYSTEMbiometric (fingerprint) identification based system is used. This...

International Journal of Emerging Technology in Computer Science & Electronics (IJETCSE) ISSN: 0976-1353 Volume 14 Issue 2 –APRIL 2015

902

FINGERPRINT BASED ATTENDANCE SYSTEM

PROF. MEGHA.A.PATIL

Assistant Professor Department of Electronics and Communication Engineering

BLDEA’s V P.Dr P.G.Halakatti college of Engineering and Technology

Vijayapur-586101

Karnataka, India [email protected]

Abstract— Managing staff attendance during working days has

become a difficult challenge. The ability to compute the

attendance percentage becomes a major task as manual

computation produces errors, and also wastes a lot of time. For

the stated reason, an efficient attendance management system

using biometrics is designed. This system takes attendance

electronically with the help of a finger print device and the

records of the attendance are stored in a database. Attendance is

marked after staff identification.For student identification, a

biometric (fingerprint) identification based system is used. This

process however, eliminates the need for stationary materials and

personnel for the keeping of records. Eighty candidates were

used to test the system and success rate of 94% was recorded.

The manual attendance system average execution time for eighty

students was 17.83 seconds while it was 3.79 seconds for the

automatic attendance management system using biometrics. The

results showed improved performance over manual attendance

management system. Attendance is marked after staff

identification.

(Keywords: fingerprints, attendance, enrollment, authentication,

identification)

I INTRODUCTION

In an increasingly digital world, reliable personal authentication has become an important human computer

interface activity. National security, e-commerce, and access

to computer networks are some examples where establishing a

person‟s identity is vital. Existing security measures rely on

knowledge-based approaches like passwords or token-based

approaches such as swipe cards and passports to control access

to physical and virtual spaces. Though ubiquitous, such

methods are not very secure. Tokens such as badges and

access cards may be shared or stolen. Passwords and PIN

numbers may be stolen electronically. Furthermore, they

cannot differentiate between authorized user and a person having access to the tokens or knowledge. Biometrics such as

fingerprint, face and voice print offers means of reliable

personal authentication that can address these problems and is

gaining citizen and government acceptance.

1.1 Biometrics

Biometrics is the science of verifying the identity of an

individual through physiological measurements or behavioral

traits[1][2]. Since biometric identifiers are associated

permanently with the user they are more reliable than token or

knowledge based authentication methods. Biometrics offers

several advantages over traditional security measures. These

include a. Non-repudiation:[1] With token and password based

approaches, the perpetrator can always deny committing the

crime pleading that his/her password or ID was stolen or

compromised even when confronted with an electronic audit

trail. There is no way in which his claim can be verified

effectively. This is known as the problem of deniability or of

‟repudiation‟. However, biometrics is indefinitely associated

with a user and hence it cannot be lent or stolen making such

repudiation infeasible.

b. Accuracy and Security: [1]Password based systems are

prone to dictionary and brute force attacks. Furthermore, such systems are as vulnerable as their weakest password. On the

other hand, biometric authentication requires the physical

presence of the user and therefore cannot be circumvented

through a dictionary or brute force style attack. Biometrics has

also been shown to possess a higher bit strength compared to

password based systems and is therefore inherently secure.

c. Screening: In screening applications[2], we are interested in

preventing the users from assuming multiple identities (e.g. a

terrorist using multiple passports to enter a foreign 3 country).

This requires that we ensure a person has not already enrolled

under another assumed identity before adding his new record

into the database. Such screening is not possible using traditional authentication mechanisms and biometrics provides

the only available solution. The various biometric modalities

can be broadly categorized as

Physical biometrics: This involves some form of physical

measurement and includes modalities such as face,

fingerprints, iris-scans, hand geometry etc.

Behavioral biometrics: These are usually temporal in nature

and involve measuring the way in which a user performs

certain tasks. This includes modalities such as speech,

signature, gait, keystroke dynamics etc.


903

Chemical biometrics: This is still a nascent field and involves

measuring chemical cues such as odor and the chemical

composition of human perspiration.

It is also instructive to compare the relative merits and de-

merits of biometric and password/cryptographic key based systems. Table 1.1 provides a summary of them. Depending

on the application, biometrics can be used for identification or

for verification. In verification, the biometric is used to

validate the claim made by the individual. The biometric of

the user is compared with the biometric of the claimed

individual in the database. The claim is rejected or accepted

based on the match. (In essence, the system tries to answer the

question, “Am I whom I claim to be?”). In identification, the

system recognizes an individual by comparing his biometrics

with every record in the database.

Table1.1: Comparison of Biometric and Password/Key based authentication[12].

Biometrics Authentication Password/Key based

authentication

Based on physiological

measurements or behavioral

traits

Based on something that the

use has or knows

Authenticates the user Authenticates the

password/key

Is permanently associated

with the user

Can be lent, lost or stolen

Biometric templates have

high uncertainty

Have zero uncertainty

Utilizes probabilistic

matching

Requires exact match for

authentication

1.2 Biometrics and Pattern Recognition

As recently as a decade ago, biometrics did not exist as a

separate field. It has evolved through interaction and

confluence of several fields. Fingerprint recognition[3]

emerged from the application of pattern recognition to

forensics. Speaker verification evolved out of the signal

processing community. Face detection and recognition was

largely researched by the computer vision community. While

biometrics is primarily considered as application of pattern

recognition techniques, it has several outstanding differences

from conventional classification problems as enumerated

below:

1. In a conventional pattern classification problem such

as Optical Character Recognition (OCR) recognition,

the number of patterns to classify is small (A-Z)

compared to the number of samples available for

each class. However in case of biometric recognition,

the number of classes is as large as the set of

individuals in the database. Moreover, it is very

common that only a single template is registered per

user.

2. The primary task in biometric recognition is that of

choosing a proper feature representation. Once the

features are carefully chosen, the act of performing

verification is fairly straightforward and commonly

employs simple metrics such as Euclidean distance.

Hence the most challenging aspects of biometric identification involve signal and image processing for

feature extraction.

3. Since biometric templates represent personally

identifiable information of individuals, security and

privacy of the data is of particular importance unlike

other applications of pattern recognition.

4. Modalities such as fingerprints, where the template is

expressed as an unordered point set (minutiae) do not

fall under the category of traditional multi-

variate/vectorial features commonly used in pattern

recognition.

1.3 Fingerprints as Biometric

Fingerprints were accepted formally[1] as valid

personal identifier in the early twentieth century

and have since then become a de-facto authentication

technique in law-enforcement agencies worldwide.

The FBI currently maintains more than 400 million

fingerprint records on file.

Fingerprints have several advantages over other

biometrics, such as the following:

1. High universality: A large majority of the human

population has legible fingerprints and can therefore be easily authenticated. This exceeds the extent of the

population who possess passports, ID cards or any

other form of tokens.

2. High distinctiveness: Even identical twins who share

the same DNA have been shown to have different

fingerprints, since the ridge structure on the finger is

not encoded in the genes of an individual. Thus,

fingerprints represent a stronger authentication

mechanism than DNA. Furthermore, there has been

no evidence of identical fingerprints in more than a

century of forensic practice. There are also

mathematical models that justify the high distinctiveness of fingerprint patterns.

3. High permanence: The ridge patterns on the surface

of the finger are formed in the womb and remain

invariant until death except in the case of severe

burns or deep physical injuries.

4. Easy collectability: The process of collecting

fingerprints has become very easy with the advent of

online sensors. These sensors are capable of

capturing high resolution images of the finger surface

within a matter of seconds. This process requires

minimal or no user training and can be collected easily from co-operative or non co-operative users. In

contrast, other accurate modalities like iris

recognition require very co-operative users and have

considerable learning curve in using the identification

system.


904

5. High performance: Fingerprints remain one of the

most accurate biometric modalities available to date

with jointly optimal FAR (false accept rate) and FRR

false reject rate). Forensic systems are currently

capable of achieving FAR of less than 10-4.

6. Wide acceptability: While a minority of the user population is reluctant to give their fingerprints due

to the association with criminal and forensic

fingerprint databases, it is by far the most widely

used modality for biometric authentication.

A fingerprint is the feature pattern of one finger (Figure 1.1).

It is believed with strong evidences that each fingerprint is

unique. Each person has his own fingerprints with the

permanent uniqueness. So fingerprints have being used for

identification and forensic investigation for a long time.

Fig 1.1 A fingerprint image acquired by an Optical Sensor

A fingerprint is composed of many ridges and furrows. These

ridges and furrows present good similarities in each small

local window, like parallelism and average width.

However, shown by intensive research on fingerprint

recognition, fingerprints are not distinguished by their ridges

and furrows, but by Minutia, which are some abnormal points

on the ridges (Figure 1.2). Among the variety of minutia types reported in literatures, two are mostly significant and in heavy

usage: one is called termination, which is the immediate

ending of a ridge; the other is called bifurcation, which is the

point on the ridge from which two branches derive.

Fig 1.2 Minutia.[2] (Valley is also referred as Furrow, Termination

also called Ending,

and Bifurcation is also called Branch)

Fingerprinting has been around for a long period of time.

Police departments have been using fingerprinting to identify

criminals. The advancement in technology has allowed for the digitizing of fingerprints. There are devices that emulate the

methods of the police by matching minutiae – the ridges and

swirls on the bottom of a person‟s finger.

Is this person authorized to enter this facility? Is this

individual entitled to access privileged information? Is the

given service being administered exclusively to the enrolled

users? Answers to questions such as these are valuable to

business and government organizations. Because biometric

identifiers cannot be easily misplaced, forged, or shared, they

are considered more reliable for person recognition than

traditional token- or knowledge-based methods.

The objectives of biometric recognition are user convenience,

better security (e.g., difficult to forge access), and higher efficiency. The tremendous success of fingerprint based

recognition technology in law enforcement applications,

decreasing cost of fingerprint sensing devices, increasing

availability of inexpensive computing power, and growing

identity fraud/theft have all ushered in an era of fingerprint-

based person recognition applications in commercial, civilian,

and financial domains. There is a popular misconception in the

pattern recognition and image processing academic

community that automatic fingerprint recognition is a fully

solved problem in as much as it was one of the first

applications of machine pattern recognition almost fifty years

ago. On the contrary, fingerprint recognition is still a challenging and important pattern recognition problem.

1.4 History of Fingerprints

Human fingerprints have been discovered on a large number

of archaeological artifacts and historical items. Although these

findings provide evidence to show that ancient people were

aware of the individuality of fingerprints, such awareness does

not appear to have any scientific basis (Lee and Gaensslen

(2001) and Moenssens (1971)). It was not until the late

sixteenth century that the modern scientific fingerprint

technique was first initiated (see Cummins and Midlo (1961),

Galton (1892), and Lee and Gaensslen (2001)). [2][3].

Fig 1.3 Archeological finger prints

Fig 1.3 gives examples of archaeological fingerprint carvings

and historic fingerprint impressions: a) Neolithic carvings b)

standing stone c) a Chinese clay seal (300 B.C.) d) an

impression on a Palestinian lam(400 A.D.)

Fingerprint recognition problem can be grouped into two sub-

domains: one is fingerprint verification and the other is fingerprint identification (Figure 1.1.2). In addition, different

from the manual approach for fingerprint recognition by

experts, the fingerprint recognition here is referred as AFRS

(Automatic Fingerprint Recognition System), which is

program-based.


905

Fig 1.4 Verification vs. Identification

Fingerprint verification is to verify the authenticity of one

person by his fingerprint. The user provides his fingerprint

together with his identity information like his ID number. The fingerprint verification system retrieves the fingerprint

template according to the ID number and matches the template

with the real-time acquired fingerprint from the user. Usually

it is the underlying design principle of AFAS (Automatic

Fingerprint Authentication System). Fingerprint identification

is to specify one person‟s identity by his fingerprint(s).

Without knowledge of the person‟s identity, the fingerprint

identification system tries to match his fingerprint(s) with

those in the whole fingerprint database. It is especially useful

for criminal investigation cases. And it is the design principle

of AFIS (Automatic Fingerprint Identification System). However, all fingerprint recognition problems, either

verification or identification, are ultimately based on a well-

defined representation of a fingerprint. As long as the

representation of fingerprints remains the uniqueness and

keeps simple, the fingerprint matching, either for the 1-to-1

verification case or 1-to-m identification case, is

straightforward and easy.

Two representation forms for fingerprints separate the two

approaches for fingerprint recognition. The first approach,

which is minutia-based, represents the fingerprint by its local

features, like terminations and bifurcations. This approach has

been intensively studied, also is the backbone of the current available fingerprint recognition products. We concentrate on

this approach in our project.

The second approach, which uses image-based methods [6][7],

tries to do matching based on the global features of a whole

fingerprint image. It is an advanced and newly emerging

method for fingerprint recognition. And it is useful to solve

some intractable problems of the first approach. But my

project does not aim at this method, so further study in this

direction is not expanded in my thesis.

1.5 Biometric Systems

A biometric system[1] is essentially a pattern recognition system that recognizes a person by determining the

authenticity of a specific physiological and/or behavioral

characteristic possessed by that person. An important issue in

designing a practical biometric system is to determine how an

individual is recognized. Depending on the application

context, a biometric system may be called either a verification

system or an identification system:

• A verification system authenticates a person‟s identity by

comparing the captured biometric characteristic with her own

biometric template(s) pre-stored in the system. It conducts one-to-one comparison to determine whether the identity

claimed by the individual is true. A verification system either

rejects or accepts the submitted claim of identity (Am I whom

I claim I am?).

• An identification system recognizes an individual by

searching the entire template database for a match. It conducts

one-to-many comparisons to establish the identity of the

individual. In an identification system, the system establishes

a subject‟s identity (or fails if the subject is not enrolled in the

system database) without the subject having to claim an

identity (Who am I?).

The term authentication is also frequently used in the biometric field, sometimes as a synonym for verification;

actually, in the information technology language,

authenticating a user means to let the system know the user

identity regardless of the mode (verification or identification).

Throughout this book we use the generic term recognition

where we are not interested in distinguishing between

verification and identification.

The block diagrams of a verification system and an

identification system are depicted in Figure 1.5; user

enrollment, which is common to both tasks is also graphically

illustrated. The enrollment module is responsible for registering individuals in the biometric system database

(system DB). During the enrollment phase, the biometric

characteristic of an individual is first scanned by a biometric

reader to produce a raw digital representation of the

characteristic. A quality check is generally performed to

ensure that the acquired sample can be reliably processed by

successive stages. In order to facilitate matching, the raw

digital representation is usually further processed by a feature

extractor to generate a compact but expressive representation,

called a template. Depending on the application, the template

may be stored in the central database of the biometric system

or be recorded on a magnetic card or smartcard issued to the individual.

The verification task is responsible for verifying individuals at

the point of access. During the operation phase, the user‟s

name or PIN (Personal Identification Number) is entered

through a keyboard (or a keypad); the biometric reader

captures the characteristic of the individual to be recognized

and converts it to a digital format, which is further processed

by the feature extractor to produce a compact digital

representation. The resulting representation is fed to the

feature matcher, which compares it against the template of a

single user (retrieved from the system DB based on the user‟s PIN). In the identification task, no PIN is provided and the

system compares the representation of the input biometric

against the templates of all the users in the system database;

the output is either the identity of an enrolled user or an alert

message such as “user not identified.”


906

Because identification in large databases is computationally

expensive, classification and indexing techniques are often

deployed to limit the number of templates that have to be

matched against the input.

These applications may be divided into the following groups:

i) applications such as banking, electronic commerce, and access control, in which biometrics will replace or enforce the

current token- or knowledge-based techniques and ii)

applications such as welfare and immigration in which neither

the token-based nor the knowledge-based techniques are

currently being used.

Two approaches for Fingerprint recognition

Two representation forms for fingerprints separate the two

approaches[4] for fingerprint recognition. The first approach,

which is minutia-based, represents the fingerprint by its local

features, like terminations and bifurcations. This approach has

been intensively studied, also is the backbone of the current

available fingerprint recognition products. I also concentrate on this approach in my honors project.

The second approach, which uses image-based methods, tries

to do matching based on the global features of a whole

fingerprint image. It is an advanced and newly emerging

method for fingerprint recognition. And it is useful to solve

some intractable problems of the first approach. But my

project does not aim at this method, so further study in this

direction is not expanded in my thesis.

1.7 Applications of Fingerprint Recognition Systems

Fingerprint recognition is a rapidly evolving technology that

has been widely used in forensics such as criminal recognition and prison security, and has a very strong potential to be

widely adopted in a broad range of civilian applications[5]

Forensic Government Commercial

Corpse

identification,

Criminal

Investigation,

Terrorist

Identification,

Parenthood

Determination,

Missing

Children,etc

National ID card,

Correctional

Facility,

Driver’s License

Social Security,

Welfare

Disbursement,

Border Control,

Passport

Control,etc

Computer

Network Logon,

Electronic Data

Security,

E-Commerce,

Internet Access,

ATM, Credit

Card,

Physical Access

Control,

Cellular Phones,

Personal Digital

Assistant,

Medical Records

Management,

Distance

Learning, etc

Table1.2 Most of the fingerprint recognition applications are

divided here into three categories.

Traditionally, forensic applications have used manual

biometrics, government applications have used token-based

systems, and commercial applications have used knowledge-

based systems. Fingerprint recognition systems are now being

increasingly used for all these sectors.

Note that over one billion dollars in welfare benefits are

annually claimed by “double dipping” welfare recipients in the

United States alone. These applications may be divided into the following groups: i) applications such as banking,

electronic commerce, and access control, in which biometrics

will replace or enforce the current token- or knowledge-based

techniques and ii) applications such as welfare and

immigration in which neither the token-based nor the

knowledge-based techniques are currently being used.

Information system/computer network security, such as user

authentication and access to databases via remote login, is one

of the most important application areas for fingerprint

recognition. It is expected that more and more information

systems/computer networks will be secured with fingerprints with the rapid expansion of the Internet. Applications such as

medical information systems, distance learning and e-

publishing are already benefiting from deployment of such

systems. Electronic commerce and electronic banking are also

important and emerging application areas of biometrics due to

the rapid progress in electronic transactions. These

applications include electronic fund transfers, ATM security,

check cashing, credit card security, smartcard security, on-line

transactions, and so on. Currently, there are several large

fingerprint security projects under development in these areas,

including credit card security (MasterCard) and smartcard

security (IBM and American Express). The physical access control market is currently dominated by token-based

technology. However, it is increasingly shifting to fingerprint-

based biometric techniques. The introduction of fingerprint-

based biometrics in government benefits distribution programs

such as welfare disbursement has already resulted in

substantial savings in deterring multiple claimants. In addition,

customs and immigration initiatives such as the INS Passenger

Accelerated Service System (INSPASS) which permits faster

immigration procedures based on hand geometry will greatly

increase operational efficiency. Fingerprint-based national ID

(Aadhar) systems provide a unique ID to the citizens and integrate different government services. Fingerprint-based

voter and driver registration provides registration facilities for

voters and drivers. Fingerprint-based time/attendance

monitoring systems can be used to prevent any abuses of the

current token based/ manual systems. Fingerprint-based

recognition systems will replace passwords and tokens in a

large number of applications. Their use will increasingly

reduce identity theft and fraud and protect privacy. As


907

fingerprint technology matures, there will be increasing

interaction among market, technology, and applications. The

emerging interaction is expected to be influenced by the added

value of the technology, the sensitivities of the user

population, and the credibility of the service provider. It is too

early to predict where and how fingerprint technology would evolve and be mated with which applications, but it is certain

that fingerprint-based recognition will have a profound

influence on the way we will conduct our daily business.

II LITERATURE SURVEY

In the field of fingerprint identification, different types of

work have been done so far. We had gone through various

research papers, the work done till today and the methods used

in each work are shown under this section

Fast Fourier Transform and Gabor Filters

The paper [18] uses a combination of two algorithms, the Fast

Fourier Transform and gabor Filters to enhance and

reconstruct the image‟s information. The system consists of eight steps: Acquisition, Noise reduction and enhancement

with gabor filters. Enhancement with Fast Fourier Transform,

Binarization, Thinning, Minutia detection and recognition.

This is used to enhance and reconstruct the information of the

finger print image as well as to extract two fundamental types

of minutiae, ending points and bifurcations. Finally the

extracted features are used to perform the fingerprint

recognition. In this method the image is divided into small

processing blocks and then fourier transform is performed.

Minutiae-Based Algorithms Using CLAHE

Histogram equalization is a general process used to enhance the contrast of images by transforming its intensity values . As

a secondary result, it can amplify the noise producing worse

results than the original image for certain fingerprints.

Therefore, instead of using the histogram equalization which

affects the whole image,[17] uses CLAHE (contrast limited

adaptive histogram equalization) which is applied to enhance

the contrast of small tiles and to combine the neighboring tiles

in an image by using bilinear interpolation, which eliminates

the artificially induced boundaries. In addition, the 'Clip Limit'

factor is applied to avoid over-saturation of the image

specifically in homogeneous areas that present high peaks in the histogram of certain image tiles due to many pixels falling

inside the same gray level range . Additionally, a combination

of filters in both domains, spatial and Fourier is used to obtain

a proper enhanced image.

Segmentation Algorithm

[20] Proposed an algorithm for segmentation that employs

feature dots, which are then used to obtain a close

segmentation curve. The authors claim that their method

surpasses directional field and orientation based methods

Segmentation is one of the first and most integral pre-

processing steps for any fingerprint verification and it

determines the result of fingerprint analysis and recognition. Segmentation refers to the separation of finer print area from

the image background. A good segmentation method should

have the following characteristics:

1. It should be insensitive to image contrast.

2.It should detect the smudged or noisy regions.

3.It should be independent of the image quality.

Thinning Algorithm

The paper [19] uses a modified thinning algorithm is that can

be used to thin any symbol, Characters and also fingerprint images regardless of their shape and orientation. Usually in

fingerprint images, Thinning seen as a preprocess for minutiae

extraction. The Proposed algorithm identifies the

unrecoverable corrupted areas in the fingerprint and does not

thin them; this is an important advantage of the proposed

method because such corrupted areas are extremely harmful to

the extraction of minutiae points. Moreover, this advantage

helps remove the spurious minutiae points which are harmful

to fingerprint matching.

III REQUIREMENTS ANALYSIS

Software requirements

Operating System - Windows XP/7

Tool – MATLAB R2010b

Visual basic 2008

SQL server

Hardware Requirements

PC with Processor - Pentium 4 or Higher

PC RAM – 2 GB or Higher

Hard Disk Drive – 40GB or Higher

Fingerprint scanner

IV PROPOSED SYSTEM

4.1 System Overview The proposed system provides solution to lecture attendance

problems through the use of attendance management software

that is interfaced to a fingerprint device. The staff bio-data

(Matriculation number, Name, Gender and Date of Birth) and

the fingerprint are enrolled first into the database. The

fingerprint is captured using a fingerprint scanner device.

For attendance, the staff places his/ her finger over the

fingerprint device and the staff‟s matriculation number is sent

to the database as having attended that particular working day.

At the end of every month, reports are generated and sent to

mail ids of all the staff. A simple architecture is shown below.

Fingerprint

input

Biometric

sensor

Feature

Extraction

Database

Matching

Feature

Extraction Biometric

sensor

Fingerprint

input


908

Fig 4.1 General Architecture of a Biometric System

4.2 System Level Design

An Automated Fingerprint Attendance System (AFAS) is a

highly specialized system that records staff attendance by

comparing a single fingerprint image with the fingerprint

images previously stored in a database. The Automated

Fingerprint Identification system (AFIS) is the principle behind the AFAS.

The major factors in designing a fingerprint attendance system

include: choosing the hardware and software components and

integrating both to work together, defining the system working

mode (verification or identification), dealing with poor quality

images and other programming language exception, and

defining administration and optimization policy [5],[9].

Staff attendance system framework is divided into three parts:

Hardware design, Software design, Attendance Management

Approach and Report Generation.

A fingerprint recognition system constitutes of fingerprint

acquiring device, minutia extractor and minutia matcher [Figure 4.2].

Fig 4.2 Simplified Fingerprint Recognition System

For fingerprint acquisition, optical or semi-conduct sensors are

widely used. They have high efficiency and acceptable

accuracy except for some cases that the user‟s finger is too

dirty or dry. However, the testing database for my project is

from the available fingerprints provided by FVC2002

(Fingerprint Verification Competition 2002). So no acquisition

stage is implemented.

The minutia extractor and minutia matcher modules are

explained in detail in the next part for algorithm design and other subsequent sections.

Hardware

The hardware to be used can be divided into two categories –

fingerprint scanner which captures the image and a personal

computer which: houses the database, runs the comparison

algorithm and simulates the application function. The

fingerprint scanner is connected to the computer via its USB

interface. Basically this work does not involve the

development of hardware. Using the Secugen Fingerprint

Reader, the GrFinger Software Development Kit (SDK)

toolbox provided by the Griaule (will explain the detail) can

be used as an interface between the fingerprint reader and the attendance software.

Fig 4.3 Fingerprint Device.

Software

The software architecture consists of: the database and the

application program:-

Database: The database consists of tables that stores records

implemented in Microsoft SQL Server database. However,

this can be migrated to any other relational database of choice.

SQL Server is fast and easy, it can store a very large record

and requires little configuration.

Application Program: The application program used is

MATLAB.

V FINGERPRINT IMAGE PREPROCESSING

5.1 Fingerprint Image Enhancement

Fingerprint Image enhancement[16][17] is to make the image

clearer for easy further operations. Since the fingerprint

images acquired from sensors or other medias are not assured

with perfect quality, those enhancement methods, for

increasing the contrast between ridges and furrows and for

connecting the false broken points of ridges due to insufficient

amount of ink, are very useful for keep a higher accuracy to

fingerprint recognition. Two Methods are adopted in my fingerprint recognition

system: the first one is Histogram Equalization; the next one is

Fourier Transform.

5.1.1 Histogram Equalization

Histogram equalization[17] is to expand the pixel value

distribution of an image so as to increase the perceptional

information. The original histogram of a fingerprint image has

the bimodal type [Figure 5.1], the histogram after the

histogram equalization occupies all the range from 0 to 255

and the visualization effect is enhanced [Figure 5.2].

Figure 5.1.1 the Original histogram

of a fingerprint image

Figure 5.1.2 Histogram after the

Histogram Equalization


909

The right side of the following figure [Figure 5.3] is the output

after the histogram equalization.

Fig 5.3 Histogram Enhancement.Original Image (Left). Enhanced

image (Right)

5.1.2 Fingerprint Enhancement by Fourier Transform

We divide the image into small processing

block s (32 by 32 pixels) and perform the Fourier transform[16] according to:

F(u,v)=

+ )}

For u = 0, 1, 2, ..., 31 and v = 0, 1, 2, ..., 31.

In order to enhance a specific block by its dominant

frequencies, we multiply the FFT of the block by its

magnitude a set of times. Where the magnitude of the original

FFT = abs(F(u,v)) = |F(u,v)|.

Get the enhanced block according to

ˆk} (2)

where F-1(F(u,v)) is done by

F(u,v)=

+

)} (3)

for x = 0, 1, 2, ..., 31 and y = 0, 1, 2, ..., 31.

The k in formula (2) is an experimentally determined constant,

which we choose k=0.45 to calculate. While having a higher

"k" improves the appearance of the ridges, filling up small

holes in ridges, having too high a "k" can result in false

joining of ridges. Thus a termination might become a bifurcation. Figure 5.4 presents the image after FFT

enhancement.

Fig 5.4 Fingerprint enhancements by FFT Enhanced image (left),

Original image (right)

The enhanced image after FFT has the improvements to

connect some falsely broken points on ridges and to remove

some spurious connections between ridges. The shown image

at the left side of figure 5.4 is also processed with histogram

equalization after the FFT transform. The side effect of each block is obvious but it has no harm to the further operations

because I find the image after consecutive binarization

operation is pretty good as long as the side effect is not too

severe.

5.2 Fingerprint Image Binarization

Fingerprint Image Binarization[16] is to transform the 8-bit

Gray fingerprint image to a 1-bit image with 0-value for ridges

and 1-value for furrows. After the operation, ridges in the

fingerprint are highlighted with black color while furrows are

white.

A locally adaptive binarization method is performed to

binarize the fingerprint image. Such a named method comes from the mechanism of transforming a pixel value to 1 if the

value is larger than the mean intensity value of the current

block (16x16) to which the pixel belongs [Figure 5.5].

Fig 5.5 The Fingerprint image after adaptive binarization, Binarized

image(left), Enhanced gray image(right)

5.3 Fingerprint Image Segmentation

In general, only a Region of Interest (ROI) is useful to be

recognized for each fingerprint image. The image area without

effective ridges and furrows is first discarded since it only

holds background information. Then the bound of the

remaining effective area is sketched out since the minutia in

the bound region is confusing with those spurious minutias

that are generated when the ridges are out of the sensor.

To extract the ROI, a two-step method is used. The first step is

block direction estimation and direction variety check [15], while the second is intrigued from some Morphological

methods.

Block direction estimation

1.1 Estimate the block direction for each block of the

fingerprint image with WxW in size(W is 16 pixels by

default). The algorithm is:

I. Calculate the gradient values along x-direction (gx)

and y-direction (gy) for each pixel of the block. Two

Sobel filters are used to fulfill the task.

II. For each block, use following formula to get the

Least Square approximation of the block direction.

tg2ß = 2 (gx*gy)/(gx2-gy2) for all the pixels

in each block.

The formula is easy to understand by regarding gradient

values along x-direction and y-direction as cosine value and


910

sine value. So the tangent value of the block direction is

estimated nearly the same as the way illustrated by the

following formula.

tg2= 2sincos/(cos2 -sin2 ) 1.2 After finished with the estimation of each block direction,

those blocks without significant information on ridges and

furrows are discarded based on the following formulas:

E = {2 (gx*gy)+(gx2-gy2)}/W*W*(gx2+gy2)

For each block, if its certainty level E is below a threshold, then the block is regarded as a background block. The

direction map is shown in the following diagram. We assume

there is only one fingerprint in each image.

Fig 5.6 Direction map. Binarized fingerprint (left), Direction

map (right)

ROI extraction by Morphological operations

Two Morphological operations called „OPEN‟ and „CLOSE‟

are adopted[15]. The „OPEN‟ operation can expand images and remove peaks introduced by background noise [Figure

5.7]. The „CLOSE‟ operation can shrink images and eliminate

small cavities [Figure 5.8].

Figure 5.7,5.8,5.9,5.10 show the interest fingerprint image

area and its bound. The bound is the subtraction of the closed

area from the opened area. Then the algorithm throws away

those leftmost, rightmost, uppermost and bottommost blocks

out of the bound so as to get the tightly bounded region just

containing the bound and inner area.

Fig 5.7 Original Image Area Fig 5.8 After close operation

Fig 5.9 After open operation Fig5.9 ROI+Bound

VI MINUTIA EXTRACTION

6.1 Fingerprint Ridge Thinning

Ridge Thinning is to eliminate the redundant pixels of ridges

till the ridges are just one pixel wide. [12] [14]uses an

iterative, parallel thinning algorithm. In each scan of the full

fingerprint image, the algorithm marks down redundant pixels

in each small image window (3x3). And finally removes all

those marked pixels after several scans. In my testing, such an iterative, parallel thinning algorithm has bad efficiency

although it can get an ideal thinned ridge map after enough

scans. [2] uses one-in-all method to extract thinned ridges

from gray-level fingerprint images directly. Their method

traces along the ridges having maximum gray intensity value.

However, binarization is implicitly enforced since only pixels

with maximum gray intensity value are remained. Also in my

testing, the advancement of each trace step still has large

computation complexity although it does not require the

movement of pixel by pixel as in other thinning algorithms.

Thus the third method is bid out which uses the built-in

Morphological thinning function in MATLAB.

6.2 Minutia Marking

After the fingerprint ridge thinning, marking minutia points is

relatively easy. But it is still not a trivial task as most

literatures declared because at least one special case evokes

my caution during the minutia marking stage.

In general, for each 3x3 window, if the central pixel is 1 and

has exactly 3 one-value neighbors, then the central pixel is a

ridge branch [Figure 4.2.1]. If the central pixel is 1 and has

only 1 one-value neighbor, then the central pixel is a ridge

ending[14] [Figure4.2.2].

Fig 6.1: Bifurcation Fig 6.2: Termination

Figure 6.3 Triple counting branches

0 0 0

0 1 0

0 0 1

0 1 0

0 1 0

1 0 1

0 1 0

0 1 1

1 0 0


911

Figure 6.3 illustrates a special case that a genuine branch is

triple counted. Suppose both the uppermost pixel with value 1

and the rightmost pixel with value 1 have another neighbor

outside the 3x3 window, so the two pixels will be marked as

branches too. But actually only one branch is located in the

small region. So a check routine requiring that none of the neighbors of a branch are branches is added.

Also the average inter-ridge width D is estimated at this stage.

The average inter-ridge width refers to the average distance

between two neighboring ridges. The way to approximate the

D value is simple. Scan a row of the thinned ridge image and

sum up all pixels in the row whose value is one. Then divide

the row length with the above summation to get an inter-ridge

width. For more accuracy, such kind of row scan is performed

upon several other rows and column scans are also conducted,

finally all the inter-ridge widths are averaged to get the D.

Together with the minutia marking, all thinned ridges in the

fingerprint image are labeled with a unique ID for further operation. The labeling operation is realized by using the

Morphological operation: BWLABEL.

VII MINUTIA MATCH

Given two set of minutia of two fingerprint images, the minutia match algorithm determines whether the two minutia

sets are from the same finger or not.

An alignment-based match algorithm partially derived from

the [1] is used in my project. It includes two consecutive

stages: one is alignment stage and the second is match stage.

1. Alignment stage. Given two fingerprint images to be

matched, choose any one minutia from each image;

calculate the similarity of the two ridges associated with

the two referenced minutia points. If the similarity is

larger than a threshold, transform each set of minutia to

a new coordination system whose origin is at the

referenced point and whose x-axis is coincident with the direction of the referenced point.

2. Match stage: After we get two set of transformed

minutia points, we use the elastic match algorithm to

count the matched minutia pairs by assuming two

minutia having nearly the same position and direction

are identical.

7.1 Alignment Stage

1. The ridge associated with each minutia is represented as a

series of x-coordinates (x1, x2…xn) of the points on the ridge.

A point is sampled per ridge length L starting from the minutia point, where the L is the average inter-ridge length. And n is

set to 10 unless the total ridge length is less than 10*L.

So the similarity of correlating the two ridges is derived from:

S = mi=0xiXi/[

mi=0xi

2Xi2]^0.5,

where(xi~xn) and (Xi~XN) are the set of minutia for each

fingerprint image respectively. And m is minimal one of the n

and N value. If the similarity score is larger than 0.8, then go

to step 2, otherwise continue to match the next pair of ridges.

2. For each fingerprint, translate and rotate all other minutia

with respect to the reference minutia according to the

following formula:

xi_new

yi_new

i_new

xi x( )

yi y( )

i

=TM *

,

where (x,y,) is the parameters of the reference minutia, and TM is

TM =

cos

sin

0

sin

cos

0

0

0

1

Fig 7.1: the effect of translation and rotation

The new coordinate system is originated at minutia F and the

new x-axis is coincident with the direction of minutia F. No

scaling effect is taken into account by assuming two

fingerprints from the same finger have nearly the same

size.My method to align two fingerprints is almost the same

with the one used by [1] but is different at step 2. Lin‟s

method uses the rotation angle calculated from all the sparsely

sampled ridge points. My method use the rotation angle

calculated earlier by densely tracing a short ridge start from

the minutia with length D. Since I have already got the minutia direction at the minutia extraction stage, obviously my

method reduces the redundant calculation but still holds the

accuracy.

Also Lin‟s way to do transformation is to directly align one

fingerprint image to another according to the discrepancy of

the reference minutia pair. But it still requires a transform to

the polar coordinate system for each image at the next minutia

match stage. My approach is to transform each according to its

own reference minutia and then do match in a unified x-y

coordinate. Therefore, less computation workload is achieved

through my method.

7.2 Match Stage

The matching algorithm for the aligned minutia patterns needs

to be elastic since the strict match requiring that all parameters

(x, y, ) are the same for two identical minutia is impossible due to the slight deformations and inexact quantizations of

minutia.

My approach to elastically match minutia is achieved by

placing a bounding box around each template minutia. If the

minutia to be matched is within the rectangle box and the

direction discrepancy between them is very small, then the

two minutia are regarded as a matched minutia pair. Each


912

minutia in the template image either has no matched minutia

or hasonly one corresponding minutia.

The final match ratio for two fingerprints is the number of

total matched pair over the number of minutia of the template

fingerprint. The score is 100*ratio and ranges from 0 to 100. If

the score is larger than a pre-specified threshold, the two fingerprints are from the same finger. However, the elastic

match algorithm has large computation complexity and is

vulnerable to spurious minutia.

VIII IMPLEMENTATION

USER MANUAL

1. Type command start_gui_single_mode in MATLAB

Figure M.1 the User Interface of the Fingerprint Recognition System. The series of buttons on the left side will be invoked

sequentially in the consequent demonstration. The two blank

areas are used to show the fingerprint image before and after a transaction respectively.

2.Click Load Button

Figure M.2 Load a gray level fingerprint image from a drive specified by

Users. Multiple formats are supported and the image size is not

limited. But the fingerprint ridges should have large gray

intensity comparing with the background and valleys.

3. Click his-Equalization Button

Figure M.3 After Histogram Equalization.

The image on the left side is the original fingerprint.

The enhanced image after the Histogram

Equalization is shown on the right side.

4. Click fft Button

Figure M.4 Captured window after click „FFT‟ button. The pop-up dialog accepts the parameter k (please refer

the formula 2). The experimental optimal k value is 0.45.

Users can fill any other constant in the dialog to get a


913

better performance. The enhanced image will be shown in

the left screen box, which however is not shown here.

5. Click Binarization Button

6. Click Direction Button

Figure M.5. Screen capture after binarization (left) and block direction

estimation (right).

7. Click ROI Area Button

Figure M.7 ROI extraction(right).

The intermediate steps for all the morphological operations

such close and open are not shown. The right screen box

shows the final region of interest of the fingerprint image. The subsequent operations will only operate on the region of

interest.

8. Click Thinning Button

9. Click Remove H breaks Button

10. Click Remove spikes Button

Figure M.10 the Fingerprint image after thining, H breaks removal, isolated

peaks removal and spike removal.(right).

11. Click Extract Button

12. Click Real Minutia Button

Figure M.12 Minutia Marking (right) and False Minutia Removal (Left). Bifurcations are located with yellow crosses and terminations

are denotes with red stars. And the genuine minutia (left) are

labeled with orientations with green arrows.

13. Click Save Button

Figure M.13 Save minutia to a text file.


914

The saved text file stores the information on all genuine

minutia. The exact format of the files are explained in the

source code.

14. Click Match Button

Figure M.14 Load two minutia files and do matching. Users can open two minutia data files from the dialog invoked

by clicking the „Match‟ button. The match algorithm will

return a prompt of the match score. But be noted that matching

in the GUI mode is not encouraged since the match algorithm

relies on heavy computation. Unpredicted states will happen

after a long irresponsive running time. Batch testing is

prepared for testing match. Please refer the source files for batch testing.

IX EXPERIMENTATION RESULTS

9.1 Evaluation Indexes for Fingerprint Recognition

Two indexes are well accepted to determine the performance

of a fingerprint recognition system: one is FRR (false rejection

rate) and the other is FAR (false acceptance rate). For an

image database, each sample is matched against the remaining

samples of the same finger to compute the False Rejection

Rate. If the matching g against h is performed, the symmetric

one (i.e., h against g) is not executed to avoid correlation. All the scores for such matches are composed into a series of

Correct Score. Also the first sample of each finger in the

database is matched against the first sample of the remaining

fingers to compute the False Acceptance Rate. If the matching

g against h is performed, the symmetric one (i.e., h against g)

is not executed to avoid correlation. All the scores from such

matches are composed into a series of Incorrect Score.

9.2 Experimentation Results

A fingerprint database from the FVC2000 (Fingerprint

Verification Competition 2000) is used to test the experiment performance. My program tests all the images without any

fine-tuning for the database. I have taken 100 inputs for

checking the false accept rate and false reject rate. The

accuracy of the system is quantified in terms of false

acceptance ratio (FAR) and the false rejection ratio (FRR). An

FAR of 1% was obtained for an FRR of 8% for this database.

Table of FAR and FRR

Number

of inputs

FAR FRR

100

1%

8%

35

40

45

50

55

image 1 image 2 image 3 image 4 image 5

Figure 9.1: Graph of image degradation v/s percentage of matching

Above graph shows the degradation of single fingerprint v/s

percentage of matching.

0%

50%

100%

150%

0 10 20 30 40

Y-Values

Figure9.2: graph of threshold value v/s system efficiency

The above figure 9.2 shows graph of threshold values v/s the

system efficiency. System will be more efficient at the

threshold value 26.

38

40

42

44

46

48

50

image 1image 2image 3image 4image 5image 6

Figure9.3: graph of different fingerprint images v/s percentage of matching

The above figure 9.3 shows the graph of different fingerprint

images v/s percentage of matching.


915

X CONCLUSION AND FUTURE WORK

12.1 Conclusion

We have combined many methods to build a minutia extractor and a minutia matcher. The combination of multiple methods

comes from a wide investigation into research paper. Also

some novel changes like segmentation using Morphological

operations, minutia marking with special considering the triple

branch counting, minutia unification by decomposing a branch

into three terminations, and matching in the unified x-y

coordinate system after a two-step transformation are used in

my project, which are not reported in other literatures I

referred to.

Also a program coding with MATLAB going through all the

stages of the fingerprint recognition is built. It is helpful to

understand the procedures of fingerprint recognition. And demonstrate the key issues of fingerprint recognition.

12.2 Future Work

The following are the some of the interesting extensions of the

present work:

1) An investigation into a filter whose primary aim is to

specifically enhance the minutia points. This project has

followed the approach adopted by most previous work where

the emphasis is on enhancing the ridge structures using Gabor,

or Gabor-like filters. However, while the ridge structures are

enhanced, this approach has shown to be less effective in enhancing areas containing minutiae points, which are the

points of main interest.

2) The current minutiae verification algorithm is applied on

the minutiae extracted using the algorithm that detects the

minutiae in the thinned binarized fingerprint ridges. The

minutiae patterns that are learnt during the training can be

used to detect the minutiae in the gray scale fingerprint image

directly.

3) The algorithms suggested in the thesis can also be

implemented using neural networks and fuzzy logic

techniques. It is also possible to compare and analyze the

algorithms by using neural network.

REFERENCES [1] Lin Hong. "Automatic Personal Identification Using Fingerprints", 1998.

[2] D.Maio and D. Maltoni. Direct gray-scale minutiae detection in

fingerprints.IEEE Trans. Pattern Anal. And Machine Intell., 19(1):27-40,

1997.

[3] Jain, A.K., Hong, L., and Bolle, R.(1997), “On-Line Fingerprint

Verification,” IEEE Trans. On Pattern Anal and Machine Intell, 19(4), pp.

302-314.

[4] N. Ratha, S. Chen and A.K. Jain, "Adaptive Flow Orientation Based

Feature Extraction in Fingerprint Images", Pattern Recognition, Vol. 28, pp.

1657-1672, November 1995.

[5] Alessandro Farina, ZsoltM.Kovacs-Vajna, Alberto leone, Fingerprint

minutiae extraction from skeletonized binary images, Pattern Recognition,

Vol.32, No.4, pp877-889, 1999.

[6] Lee, C.J., and Wang, S.D.: Fingerprint feature extration using Gabor

filters, Electron. Lett., 1999, 35, (4), pp.288-290.

[7] M. Tico, P.Kuosmanen and J.Saarinen. Wavelet domain features for

fingerprint recognition, Electroni. Lett., 2001, 37, (1), pp.21-22.

[8] L. Hong, Y. Wan and A.K. Jain, "Fingerprint Image Enhancement:

Algorithms and Performance Evaluation", IEEE Transactions on PAMI ,Vol.

20, No. 8, pp.777-789, August,1998.

[9] Image Systems Engineering Program, Stanford University. Student project

By Thomas Yeo, Yu

Tai.http://ise0.stanford.edu/class/ee368a_proj01/dropbox/project22/finger/

[10] FVC2000. http://bias.csr.unibo.it/fvc2000/

[11] FVC2002. http://bias.csr.unibo.it/fvc2002/[12] L.C. Jain, U.Halici, I.

Hayashi, S.B. Lee and S.Tsutsui. Intelligent biometric techniques in

fingerprint and face recognition. 1999, the CRC Press. [13] R.Vinothkanna,

Dr.Amitabh Wahi. Feature Extraction from Blurred Fingerprints Using Image

Negative Method.2012.[14] Feng Zhao. A Brief Introduction to Skeleton-

Based Fingerprint Minutiae Extraction.

[15] Dr. Neeraj Bhargava#1, Dr. Ritu Bhargava*2, Manish Mathuria$3, Pooja

Dixit#45 Fingerprint Minutiae Matching using Region of Interest.

[16] R.Dharmendra Kumar, Kaliyaperumal Karthikeyan,

T.Ramakrishna.FINGER PRINT IMAGE ENHANCEMENT USING FFT

FOR MINUTIA MATCHING WITH BINARIZATION

.[17] M. Sepasian, W. Balachandran and C. Mares. Image Enhancement for

Fingerprint Minutiae-Based Algorithms Using CLAHE, Standard Deviation

Analysis and Sliding Neighborhood.

[18] Gualberto Aguilar,Gabriel Sánchez,Karina Toscano,Mariko Nakano-

Miyatake,Héctor Pérez-Meana Automatic Fingerprint Recognition System

Using Fast Fourier Transform and Gabor Filters.

[19] Sasan Golabi, Saiid Saadat, Mohammad Sadegh Helfroush, and Ashkan

Tashk.A Novel Thinning Algorithm with Fingerprint Minutiae Extraction

Capability.

[20] Mrs. P.Balaramudu M.Tech1 ,J.Nageswara Rao M.Tech

Fingerprint Identification and Verification System using Minutiae Matching

http://ise0.stanford.edu/class/ee368a_proj01/dropbox/project22/finger/

http://bias.csr.unibo.it/fvc2000/

http://bias.csr.unibo.it/fvc2002/

FINGERPRINT BASED ATTENDANCE SYSTEMbiometric (fingerprint) identification based system is used. This...

Documents

Transcript of FINGERPRINT BASED ATTENDANCE SYSTEMbiometric (fingerprint) identification based system is used. This...