Biometric Class Attendace System

CHAPTER ONE

INTRODUCTION

1.1 BACKGROUND TO THE STUDY

Living in the information age, individuals have vast amounts of information that they wish to

keep private. Much of this information is protected by the use of passwords. Although this

approach is satisfactory with most individuals, some seek more secure methods. One approach is

using characteristics of individuals as the form of authentication, known as biometrics. Biometric

security is based on something you know or have. Fingerprints are the most common form of

biometrics and have several measurable distinctive characteristics. The biometrics industry is

growing fast because of new technology and the need for a more secure authentication system.

(Ling and Tamer, 2009).

Although many individuals feel that passwords are enough to protect our information, there are

several problems. People often forget passwords or worse, they can be stolen and then used by

other individuals. A person might have several different passwords used for different

applications. Passwords are often poorly chosen because individuals incorporate personal

information or use common dictionary words.

An alternative to passwords is using human characteristics for the purposes of identification; this

is known as biometrics. The individuals have these distinctive features; they can be used as a

form of identification. According to a study, as many as 80% of the public has allowed a

biometric feature to be recorded. Although there are several human characteristics that can be

measured for authentication including the face, eye, and voice, the fingerprint is the most

commonly used characteristic. Everyone is born with a fingerprint. They cannot be forgotten at

home or left in the car. Fingerprints are the oldest form of biometrics that has been used

successfully.

According to Wells (2001), in the 14th century, parents in China used the fingerprints and

footprints of their children as a form of identification. Since then, fingerprints have been studied

and their characteristics have been catalogued.

Each individual fingerprint is unique; everyone has an immutable fingerprint. A fingerprint

consists of several lines that produce patterns, called ridges, which can be used to verify and

authorize an individual.

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The most common system used to classify the ridge patterns in fingerprints are known as the

Galton features. There are six classes of patterns. These are known as

Arch

Tented arch

Left loop

Right loop

Whorl

Twin loop.

Each pattern has its own distinct design that distinguishes them apart. The several features that

are classified are known as minutiae. These are the irregularities in the otherwise smooth pattern

of ridges in a fingerprint. The minutiae include characteristics called the

Crossover

Core

Bifurcation

Ridge ending

Island

Delta

Pore.

The crossover pattern is created when two different ridges cross each other. The point in which

swirls or other patterns often center around is known as the core. Bifurcation is the point at

which one ridge separates into two separate ridges. A ridge ending is the end point of a ridge.

An island is small ridge in the space between two other ridges and does not touch any other

ridges. The space in between ridges where several ridges surround is known as a delta.

Occurring inside ridges at steady intervals are pores. Ralph E. Johnson (1996).

A device is used to capture an image of the pattern of an individual’s fingerprint. There are two

main technologies used to capture the image of the fingerprint. The first technology involves

optical technologies using a prism in which a source of light is refracted. Using this light, the

device is able to take an accurate fingerprint image.

The second technology used is capacitive-based semiconductors. The fingerprint is obtained by

having the subject place the finger on a sensor chip. The chip then detects capacitance changes

between the ridges and valleys between the chip and skin and uses this to construct an image

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according to the variance of voltages. According to Kroenke and David in Database Concepts.

3ed. New York: Prentice, 2007.

1.2 STATEMENTS OF THE PROBLEM

The problems associated with face to face attendance impersonation and impersonation at

examination hall are enormous, it is time consuming, strenuous, requires physical identification

and biasness. For these reasons, a secured biometric authentication system introduced to meet

with the latest technology in security considering the rate at which impersonation in classrooms

and examination hall are going. The existing method of identifying the students for class room

attendance and even examination writing is not reliable; as students can collect their colleagues’

identification without the lecturer or invigilator knowing. Friends can answer presence or write

attendance for their friends and write their examination for them.

1.3 AIM AND OBJECTIVES OF THE STUDY

The overall aim of the proposed system is to develop a desktop based application that can handle

student’s attendance system with a fingerprint biometric authentication system to prevent

impersonation in Computer Science Department of MoshoodAbiola Polytechnic, Abeokuta. To

be able to achieve this, the following objectives must be met:

To examine the major biometric technologies of today i.e. fingerprint biometric

authentication.

To be able to identify every student with a unique identity.

To relief the lecturer from the stress of manual attendance collection method and confirm

each and every student for attendance at required time.

To be able to use the latest technology for security measures.

To develop a system of examination model devoid of irregularities and fair to all

participants.

1.4 SIGNIFICANCE OF THE STUDY

The proposed system has a significant to the institution as well as to the students of Computer

Science, MoshoodAbiola Polytechnic, Abeokuta.

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A. To the Institution

It will eradicate attendance impersonation.

It could also eradicate examination malpractice.

The fingerprint collected can be used as a form of identification when there is a

fraudulent act among the student.

The fingerprint of every student will be recorded in the database and as such it could be

used as a means of identification when there is need for it.

B. To the Student

There will be no mix up of student details as seen in the manual method, i.e. student record

search and sorting will be very efficient as no one shares same bio-data with another in the

world.

1.5 SCOPE OF THE STUDY

The proposed system is attendance system for the student with fingerprint biometric

authentication model for Computer Science Department, MoshoodAbiola Polytechnic,

Abeokuta. It allows for adding of student details with each person’s fingerprint. It as well has the

ability to calculate attendance values of the current students.

1.6 DEFINITION OF TERMS

These are words to be encountered in the proposed attendance system for the students.

Fingerprint:An impression or mark made on a surface by a person's fingertip, as used

for identifying individuals from the unique pattern of the finger.

Biometrics:Biometric is the science of measuring physical and behavioral characteristics

that uniquely identify individuals.

Fingerprint Authentication:Refers to the automated method of verifying a match

between two human fingerprints.

Fingerprint Scanning:Is the process of electronically obtaining and storing human

fingerprints.

Database:A database is the collection of information that has been organized so that it

can be access easily, manages, updated and retrieved by authorized user.

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Response time:This system may take hours to match a candidate, while fingerprint

systems respond with seconds or fractions of seconds.

Capture:This system is designed to use the entire fingerprint, rolled from nail to nail,

and often capture all ten fingerprints. Fingerprint systems use only the center of the

fingerprint, capturing only a small fraction of the overall fingerprint data.

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CHAPTER TWO

LITERATURE REVIEW

2.1 Brunelli (1993), used template matching for biometric recognition of human fingerprint.

The algorithm prepares a set of two masks representing skin and nail for each registered person.

To identify the unknown person in the image, the algorithm will first detect the skin using

template matching and then normalize position, scale and thickness of the finger skin and nail.

Next for each person in the database, the algorithm will place the two masks i.e. finger skin and

nail on their position.

Sato et al.,(1998) used neural networks instead of template matching to recognize a fingerprint.

In the neural networks, output unit corresponds to registered person and input units correspond to

pixels of the input image. In the biometric recognition of human fingerprint phase, the neural

network computes an output vector for each test image and the unknown person in the image is

classified as the person corresponding to the output unit that has the maximum value of the

output vector if the maximum value is greater than the threshold value.

Kwawaguchi, ( 2000) proposed a new algorithm to detect fingerprint nail of an individual in an

intensity image. They implemented the separability filter and though transform to measure the fit

of pair of fingers to the image. The algorithm then selects a pair of finger with the smallest cost

from the five fingers.

Kirby S., (1990) developed the first introduction of a low dimensional characterization of

fingers. This is meant to differentiate various fingers from each other.

Turk, (1991) used eigenspace method instead of template marching. This method constructs an

eigenspace for each registered person using sample finger images. In the biometric recognition of

fingerprint phase, the tested image is projected onto the eigenspaces of all registered person to

compute the matching errors. The unknown person in the image is classified as the person

corresponding to the eigenspace giving the smallest matching error.

Moon P., (2001) investigates principal component analysis using ferret database to examine the

Eigen finger performance through the changing illumination, compression algorithm, varying the

number of eigenvector and changing the similarity in the classification process.

Yang (2000)demonstrated the successful result in fingerprint recognition, detection and tracking

with representing the PCA second order statistics of the finger image. He also implements

several image processing such as segmentation, desk Ewing, zooming, rotation and warping to

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observe the Eigen finger capability. The capability of neural network in a pattern classification

enables it to be chosen in finger recognition experiment.

Ahmad Fadzil (1994) also developed a biometric recognition system for scanning human

fingerprint using multilayer perception artificial neural network.

Biometrics originated from two Greek words: “bios” (life) and “metrikos” (measure). Biometrics

is defined as the identification of an individual based on physiological and

behavioralcharacteristics. Physiological characteristics include face (2D/3D facial images, facial

IR thermo gram), hand (fingerprint, hand geometry, palm print, hand IR thermo gram), eye (iris

and retina), ear, skin, odor, dental, and DNA, while behavioral characteristics include voice, gait,

keystroke, signature, mouse movement, and pulse. And multimodal biometrics can be combined

in a system to improve the recognition accuracy. In addition, some soft biometric traits like

gender, age, height, weight, ethnicity, and eye color can also be used to assist in identification

(Qinghai, 2010).

Generally, a biometric system is designed to solve a matching problem through the live

measurements of human body features. It operates in two stages. First, a person must register a

biometric (physiological and behavioral) in a system where biometric templates will be stored.

Second, the person must provide the same biometric for new measurements. The output of the

new measurements will be processed with the same algorithms as those used at registration and

then compared to the stored template. If the similarity is greater than a system-defined threshold,

the verification is successful; otherwise it will be considered unsuccessful (Qinghai, 2010).

Biometric technologies enable automatic personal recognition based on physiological or

behavioral characteristics (Prabakaretal, 2003). Biometric is defined as the "automated

identification or verification of human identity through the measurement of repeatable

physiological and behavioral characteristics" (Association of Biometric, 2004).

2.2 FEATURES OF BIOMETRIC AUTHENTICATION TO PREVENT

IMPERSONATION

The primary advantage of biometric authentication methods over other methods of user

authentication is that it is simple, easier to use and its portable in size. These methods use real

human physiological or behavioral characteristics to authenticate users. These biometric

characteristics are (more or less) permanent and not changeable. It is also not easy (although in

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some cases not principally impossible) to change one’s fingerprint, iris or other biometric

characteristics. Users cannot pass their biometric characteristics to other users as easily as they

do with their cards or passwords. Biometric objects cannot be stolen as tokens, keys, cards or

other objects used for the traditional user authentication, yet biometric characteristics can be

stolen from computer systems and networks. Biometric characteristics are not secret and

therefore the availability of a user’s fingerprint or iris pattern does not break security the same

way as availability of the user’s password. Even the use of dead or artificial biometric

characteristics should not let the attacker in.

Most biometric techniques are based on something that cannot be lost or forgotten. This is an

advantage for users as well as for system administrators because the problems and costs

associated with lost, reissued or temporarily issued tokens/cards/passwords can be avoided, thus

saving some costs of the system management. Another advantage of biometric authentication

systems may be their speed. The authentication of a habituated user using an iris-based

identification system may take 2 (or 3) seconds while finding your key ring, locating the right

key and using it may take some 5 (or 10) seconds.

2.3 TECHNICAL DEVELOPMENT

2.3.1 Classification

An important issue when considering biometric technology is to address the distinct

classifications formally defined through application and implementation. The majority of the

literature makes a distinction between the two general categories of biometric identifiers, namely

physiological and behavioral.

i. Physiological methods include: DNA, ear, infrared thermo grams, hand/finger geometry,

iris scan, odor and retinal scan.

ii. Behavioral methods include: gait, keystroke dynamics, signature and voice.

Further comparative sub-classification helps to clarify biometric application categories

conceptually and also defines the specifics of usage within an application domain. Maltoni et al.,

provides the most comprehensive guidance in this regard. Whether behavioral or physical,

biometric systems will have usage permutations based on the following sub-categories, a

verification or identification system, on-line or off-line system and positive or negative modes of

operation.

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Verification systems are often referred to as ‘Am I who I claim I am?’ systems. A user’s

captured biometric is authenticated against the biometric template provided by the user during

prior enrolment. So verification is a one-to-one comparison that usually requires two pieces of

information at the point of entry. The information required is either a user name or unique PIN

number and the necessary biometric. In a verification system the enrolment information for a

given user can be held on a database or on a smartcard issued to the user. (Note that verification

is also referred to as authentication).

Identification systems are often referred to as ‘Who am I’ systems. Essentially, an individual’s

biometric data is presented to the system as anonymous and comparison is carried out on a one-

to-many basis. In other words, the presented template is compared with all templates in the

entire database to find a possible match. The exhaustive search nature of this operation may

create a significant problem in computational complexity when dealing with very large stores of

biometric information.

Although there are possible exceptions to the rule, on-line systems usually require fast

recognition and speedy response e.g. network logon applications. However, off-line systems

may tolerate relatively long response periods such as crime scene fingerprint processing through

a forensic fingerprint application. In the case of identification systems, search efficiency will

generally be a bigger issue if the system is to be on-line rather than off-line. Usually, every

aspect of an on-line system is fully automatic and scanning is done using a ‘live-scan’ scanner.

Whereas an off-line system may contain some manual procedures, e.g. for AFIS, a duty officer

may process a suspect by first taking inked fingerprints whilst checking for quality acquisition at

the same time. The prints may then be added to a biometric system through a cold or off-line

scanner. It is interesting to note that such a system may also require an element of semi-

automatic functionality when producing matching results. A latent fingerprint processed through

the system may produce a list of possible candidates for identification. The list could then be

analyzed by a forensic expert before a final ‘human’ decision is made. Note also that this is an

example of an application requiring storage of the raw image data of the captured biometric.

In positive mode the system establishes whether a given individual is the identity being,

(implicitly or explicitly) claimed. A positive application makes sure that a given identity is used

by only one person and can operate in both verification and identification mode.

In negative mode the system establishes whether the person is who he (implicitly or explicitly)

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denies being. A negative application makes sure a given individual cannot use multiple

identities within the system and operates only in identification mode. Another interesting point to

note about positive and negative recognition is that, although traditional systems of username

and password verification etc work for positive recognition, negative recognition can only be

achieved through biometrics, for example face recognition systems for airport security.

2.4 KEY TECHNOLOGIES

The fundamental computing concepts at the core of modern biometrics include image

processing, pattern recognition, statistics, basic signaling and some machine learning models

such as knowledge based systems and neural nets. This section gives the technological

background to the most common biometric identifiers as drawn from the literature.

Iris

Widely regarded as potentially the most robust of all biometric identifiers, iris recognition

systems are said to be distinct for both each person and in each eye. Even identical twins have

differing iris features and matching is extremely fast and accurate. Dr. John Daugman of

Cambridge University’s computer laboratory developed the key algorithms for image capture,

feature extraction and matching during the early 1990’s. Daugman gives a comprehensive

account of the technical and performance aspects of his algorithms.

The key problems during feature extraction are detecting the pupil, (which can vary up to 15%

from a central position in the eye) and removing noise created by eye-glasses or light reflection

on the cornea as well as parts of the iris obscured by the eyelashes or drooping of the eyelid. This

is achieved by using edge detection to create zones of texture across the iris by differentiating

between the sclera, white of the eye, on the outer zone and the varying dilation of the pupil on

the inner zone. The isolated image is demodulated using 2 dimensional Gabor wavelets to

extract the phase information only. Phase information is more discriminating because phase

angles can be assigned without the reliance that amplitude has on contrast and illumination.

After masking to reduce noise, a 2k, (256 byte) iris code is produced.

During matching, a normalized Hamming distance measure – (or count of bit differences

between two iris templates when masked) – is calculated to determine a match or non-match.

This matching principle Daugman calls, “the failure of a test of statistical independence on iris

phase structure…” with a failure signifying a match. The sheer “combinatorial complexity” of

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the phase information produced by Daugman’s algorithms in all practical systems tested has yet

to produce one false match between two different iris templates.

Further, it is claimed that under ideal conditions enrolment can take less than a second and that

when matching, hamming distances can be calculated quickly, so large databases can be

searched at a rate of 40,000 templates in less than a second. Therefore, iris recognition systems

are the only biometric method that allow the same algorithms to be used for both verification and

identification

Voice

Voice is an acceptable biometric for many and in fact is the only possible biometric for most

audio-technologies. It is important to note that there is a distinction made between voice

verification or speaker recognition, (i.e. identifying a specific speaker) and speech recognition,

(i.e. identifying what is being said). Research into speaker recognition goes back over forty

years and relies on both behavioral and physical traits. Physical traits include such properties as

the size and shape of the vocal chords, vocal tract, palate etc and learned behaviors include style

of speech, voice pitch and timbre. The fact that behavioral as well as physical traits combine in

resulting speaker system templates or “voice prints”, leads to the method being classified as a

behavioral biometric in general, (Ibid). The ubiquity of acoustic technology such as telephony

makes speaker recognition an attractive security option. This is because it is often possible to

take advantage of existing audio hardware when deploying such systems.

Generally, speaker recognition systems must first convert captured analogue speech signals to

digital and further process them using spectral analysis principles. Typically, Fourier transforms

can be used to derive coefficients for complex audio wave functions which in turn can be used to

isolate the ceptstral feature vector for representing the human voice, (Ibid). For matching

algorithms, a number of techniques can be used, from machine learning, e.g. k-nearest neighbor,

Statistical models, e.g. Hidden Markov, to neural nets.

Face

Face recognition is considered one of the most non-intrusive of biometric methodologies because

we naturally use distinguishing facial characteristics to differentiate between people every day.

For image capture, standard optical scanners can be used for legacy data, i.e. still photos and live

capture can be done with ordinary photographic equipment. Certain newer technologies acquire

a 3D image of the face using stereo, structured light or phase-based ranging and near infrared can

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be used to supplement face detection in poor lighting conditions.

Processing proceeds by applying any necessary linear transformations in order to normalize a

captured image. The next step is to detect the face within the image; this can be achieved by

running a generic algorithm to detect the face shape through facial textures. Image encoding can

be either localized or global. Local models are based on establishing the relationship between a

number of facial features, such as the distance between the eyes, or the distance between each

eye and the nose etc. The global model is template-based, such as the eigenface approach and

relies on general facial patterns for classification.

During the 1990’s Turk and Pentland popularized the eigenface method. The process works by

first deriving the standardized set of eigenfaces over a dataset of normalized face images using

matrix algebra. Each image is converted to a vector of intensity values with a length equal to the

number of pixels within the image. Next a mean “average face” is determined using all image

vectors in the dataset. Then the difference from the average is calculated for each image and

used to compute a covariance matrix. This provides correlation information across the dataset

and it is the eigenvectors in the matrix that represent the eigenfaces. However, as the dimensions

of the image vectors increase the number of generated eigenfaces grows exponentially. Principal

Component Analysis, (PCA), is a statistical model that provides the mechanism to reduce the

number of eigenvectors derived from the covariance matrix from the size of the image

dimension, to the size of the dataset. PCA can be used in face recognition because faces have

similar patterns in general and PCA tells us that for a number of images N, there are only N non-

trivial eigenvectors, i.e. those with the highest correlation values.

Any human face can be considered a combination of the standard sub-set of these eigenfaces.

Each eigenface represents a pattern of evaluation for different facial features, e.g. symmetry, size

of the nose etc. Therefore, a given facial image can be compressed to a list of correlation values

corresponding to each eigenface and in the range 1 to -1. For face recognition, a test image can

be projected onto the standard set and the closest image match located using Euclidean distance

measures. Hybridized approaches based on both local and global methods, called eigen features,

have also been developed and another technique, fisherfaces, is said to be less sensitive to light

variation and facial angle, (Ibid). Surface texture analysis, (STA), relies on the skin features of

an image, which contains the most detailed information and Kung et al have proposed algorithms

for probabilistic neural nets for localized feature extraction.

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Hand

Hand geometry measures Length of fingers and/or width of hand to harvest an identifier that may

be invariant and specific to a user. However, hand/finger geometry is limited in distinctiveness.

For this reason it is only useful for small scale non-critical use. Hand readers are

computationally very efficient, easy to use and have a widespread application. For a given

system there is an associated feature set of hand measurements. When enrolling on a system a

user places their hand on the platen, which may use guiding pegs to make sure that a hand is

consistently presented. The captured hand image is processed and reduced to a feature vector, a

value based on the feature set of hand measurements.

For matching, a test feature vector is compared with the claimed identity feature vector from the

system database. Usually a distance metric model is used for comparison. Jain et al, tested four

different distance metrics, absolute, weighted absolute, Euclidian and weighted Euclidian. The

results showed that over a feature set of 16 measures the weighted Euclidian metric performed

best, although the researchers point out that better performance could be achieved with higher

level features, such as skin colour, wrinkles and folds on skin.

Fingerprint

Fingerprint-based identification is the oldest biometric system in terms of successful practical

application. The invariant and immutable aspects of a fingerprint supposedly lie in the patterns

of ridges and furrows, as well as the ridge characteristics occurring at either a ridge bifurcation

or a ridge ending – the so called minutiae points. A ridge ending is, obviously, where a ridge

line terminates and a bifurcation is where a ridge line splits into two separate ridge lines. In

terms of fingerprint representation, ridge and furrow patterns are global features and minutiae are

local features.

The traditional Henry system is based on identifying the global patterns observed in fingerprints

and classifying them based on the distinct flow of each pattern. Examples of classification are:

arch, tented arch, left whorl etc. These flow patterns are usually formed around other features

called singular point, which takes the form of a loop or delta shape. Where the singular points lie

is very important in classifying and indexing fingerprints. As it turns out, global patterns are not

particularly distinct between individuals. For AFIS applications an individual’s ten fingers are

classified and a signature vector is created known as a ten-print-card. Although not unique to an

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individual, the ten-print-card can be used to index and create a sub-set of the AFIS database, thus

reducing the search space.

2.5 TYPES OF BIOMETRICS

Data Matching – channel biometric, the identification of an individual using the analysis

of segments from DNA.

Ear – Visual biometric, the identification of an individual using the shape of the ear.

Eyes-Iris recognition – Visual biometric, the use of pattern of veins in the back of the eye

to accomplish recognition.

Face recognition – Visual biometric, the analysis of facial feature patterns for

authentication or recognition of an individual’s identity. Most face recognition system

either use Eigen faces or local feature analysis.

Finger print recognition – Visual biometric, the use of the ridges and valleys (minutiae)

found on the surface tips of human finger to identify and individual.

Finger geometry recognition – Visual/spatial biometric, the use of 3D geometry of the

finger to determine identity.

Gait – behavior biometric, the use of individuals walking style or gaits to determine

identity.

Odor – olfactory biometric, the authentication of an individual odor to determine identity.

Signature recognition – Visual/ Behavioral biometric, the authentication of an individual

by the analysis of hand writing style, in particular the signature.

Typing recognition – Behavioral biometric, the use of the unique characteristics of a

person typing for establishes identity.

Vein recognition – Vein recognition is a type of biometric that can be used to identify

individuals based on the vein patterns in the human finger or palm.

Voice/speaker recognition – There are two major applications of speaker recognition.

i. Voice-speaker recognition/authentication – Auditory biometric, the use of the voice as a

method of determining the identity of a speaker for access control.

ii. Voice speaker identification – Auditory biometric identification, is the task of

determining an unknown speaker identity.

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2.6 FINGER PRINT AUTHENTICATION

Finger print authentication or finger print recognition refers to the automated method of verifying a match

between two human finger prints. Finger prints are one of many forms of biometrics used to identify

individuals and verify their identity.

2.6.1 Background

The analysis of finger prints for matching purposes generally requires the comparison of several features

of the print pattern. These includes pattern, which are aggregate characteristics of ridges, and minutia

points, which are unique features found within the patterns.

It is also necessary to know the structure and properties of human skin in order to successfully employ

some of the imaging technologies.

2.6.2 Patterns

The three basic patterns of fingerprints ridges are the arch, loop and whorl:

Arch – The ridges enter from one side of the finger, rise in the center forming an arc, and

then exist the other side of the finger.

Loop – The ridges enter from side of a finger, from a curve, and then exit on that same

side.

Whorl: Ridges from circularly around a central point of the finger.

2.6.3 Minutia features

The major minutia features of finger print ridges are ridge ending, bifurcation, and short ridge (or dot).

The ridge ending is the point at which a ridge terminates. Bifurcations are points at which a ridge splits

into two ridges. Short ridges (or dots) are ridges which are significantly shorter than the average ridge

length on the finger print. Minutiae and patterns are very important in the analysis of finger prints since

no two fingers have been shown to be identical.

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CHAPTER THREE

DESIGN METHODOLOGY

3.1 With this system 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. 1990’s to the present),

with the necessary overall historical context being provided in the main introduction.

Maltonietal, Bolle et al and Wikipedia 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.

Collectability, 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.

3.2 INFORMATION GATHERING PROCESS

In the course of writing this project, several tools were used during the information gathering

process so as to successfully design and implement a functional System. Some of the tools used

are listed below:

Interview: Some information needed for the project was gathered by conducting interviews which helped

to gather facts about the conventional method of student attendance keeping.

Observation: As an Analyst, I made personal observations of the current method involved in keeping

student attendance.

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The Web: The Internet has always provided information needed to build a user-friendly and effective

System.

3.3 SYSTEM DESIGN

The System design includes input to the System, the process, and output.

3.3.1 Inputs to the System

The inputs to this System are from the application users which are fully discussed under

the System’s data elements.

3.3.2 Data Elements

Data elements of a System are fields present in the System’s database. The followings are the

data elements of the Student clock-in System:

Full name: This is the name of the application user and it is required for identification

purpose.

User name: This is the username of the application user.

Password: Password of the application user.

Mobile number: This is the phone number of the application user required for contact

purpose.

Time-in: This is the exact time the application user clocks-in.

Time-out: This is the exact time the application user clocks-out.

Date of birth: This is the date of birth of the application user.

3.3.3 Process Of The System

The process of the system includes user registration, user clock-in, user clock-out, record update and

searching user records.

3.3.4 Output from the System

The outputs of this System are shown in Chapter 4, which is the Implementation Phase and it

also contains screen shots of the System.

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3.4 MODULES OF THE BIOMETRIC SYSTEM

Any biometric system is basically made of the following component which is illustrated in

the Figure 1 below:

Figure 1: Components of Biometric System.

1. PORTAL: Its purpose is to protect some assets. An example of a portal is the gate at an

entrance of a building. If the user has been successfully authenticated and is authorized to

access an object then access is granted.

2. CENTRAL CONTROLLING UNIT: receives the authentication request, controls the

biometric authentication process and returns the result of user authentication.

3. INPUT DEVICE: The aim of the input device is biometric data acquisition. During the

acquisition process user’s aliveness and quality of the sample may be verified.

4. FEATURE EXTRACTION MODULE: processes the biometric data. The output of the

module is a set of extracted features suitable for the matching algorithm. During the

feature extraction process the module may also evaluate quality of the input biometric

data.

5. STORAGE OF BIOMETRIC TEMPLATES: This will typically be some kind of a

database. Biometric templates can also be stored on a user-held medium (e.g., smartcard).

18

Portal

Central Controlling

Unit

Storage

In that case a link between the user and her biometric template must exist (e.g., in the

form of an attribute certificate).

6. THE BIOMETRIC MATCHING ALGORITHM: compares the current biometric

features with the stored template. The desired security threshold level may be a parameter

of the matching process. In this case the result of the matching will be a yes/no answer.

Otherwise a score representing the similarity between the template and the current

biometric sample is returned. The central unit then makes the yes/no decision.

3.4.1Model ofthe Biometric Authentication System

3.4.1.1 System Architecture

Input of student

Bio-data

3

4

1

Input of student

Passportphotograph

Figure 2: Biometric System Architecture.

The diagram labeled 1 is the input of the student bio-data set to the system

The diagram labeled 2 is the scanner that takes the biometric property (finger print image) into

the system.

The diagram labeled 3 is the part of system that triggers the scanner to take image of finger print

and set it to the system.

The diagram labeled 4 is the input of the student passport photograph set to the system

The diagram label 5 is the main controlling unit that receives the image and some other

information about the user. The unit can create a new user and as well majorly authenticate and

existing user.

19

Create New Entity Database

Capture Image

The diagram label 6 is the back end database where all the system information is stored.

DESIGN APPROACH

The purpose of this is to use the information gathered in the analysis phase to design the System.

3.5 SYSTEM REQUIREMENTS

3.5.1 Hardware Requirements

The hardware requirements for this application are:

60GB hard disk or higher,

Monitor,

Uninterrupted power supply,

Random Access Memory size of 512MB or higher.

A Bio-metric scanner (preferably digital personnel)

3.5.2 Software Requirement

The software requirements for this application are:

Windows XP, Windows Vista, Windows 7 or 8,

Mysql Server,

Java Runtime (version 5.0 and above),

Net Beans IDE,

Biometric scanner setup (digital personal)

3.6 DATABASE DESIGN

A database is defined as a repository for stored data. Files stored in the System are the application user

files and these files contain information about the application user authentication details. Mysql is used

for the database design.

3.7 SYSTEM FLOW CHART

Below is the data flow diagram of this System. This data flow diagram consists of, an external

entity, a database, processes, outputs and stored data. The external entity that exists in this

System is the Student. The student fills out required information in the student registration form

which undergoes validation before submission to database. The clock-in and clock-out records

are also stored in the database along with the matching student information.

20

Wait for print Is option =check out

Accept print

Did print match a Student

Is option =check in report

Display check in report

Display check outreport

Is option =check outreport

is require input supply

Save student

C

AB

No

No

Yes

Yess

Start

Wait for input

Accept input

Did accept input match

Fingerprint enrollment Is option =clock in Wait for inputMain page

No

No

Yes

21

No

Yes

Yes

Is option logout

Is option =registersave to DBWait for input

Update student

Wait for update input

Accept input

CA B

register

Is option view

Stop

Update DB

No

No

No

No

Yes

Yes

Yes

Fig 3.1 System Flow Chart Diagram

3.8 METHOD OF DATA COLLECTION

22

is require input supply

No

Finger Print Image

Bio-data

Other records

The proposed system is developed with Visualbasic.net and Microsoft Access as the database.

Research methodology refers to the methods or tools that I used during the research of this work.

They are as follows:

For Data Gathering: the method I used in gathering of data is direct.

Technique Used: the technique that I will use in the proposed system is the parallel method of

implementation simply because parallel supports the use of a system with the existing one in

case of system failure so that it will not be back to square one for the user of such system.

Tools: The tools used are Visualbasic.Net development environment and MYSQL as the back

end.

3.9 SYSTEM DATABASE STRUCTURE

The database is the back end storage that consists of all the information in the system, the

database consists of record about every entity. An entity is something that has a distinct feature

among other. This Biometric Student Attendance System database is structured to have four

tables that fully describe every entity that exist. Below in Figure 3 is a diagram that describes the

database mode

Figure 4: The Database Model.

CHAPTER FOUR

23

IMPLEMENTATION AND EVALUATION

4.1 IMPLEMENTATION ENVIRONMENT

This project was implemented using java programming language to write codes at the backend

and also to design the front end of the application. The database used for record storage is

MYSQL. The implementation phase also involves the use of a biometric scanner which enables

users to scan their finger prints.

4.1.1 Implementation Phase

The implementation of this project is divided into two phases:

Studentand Administrator registration phase: This phase allows the students and

administrators to register their details to enable them gain access to the application’s

main page. After an administrator has successfully registered, he can then register a

student.

User authentication phase: This phase enables a user to access the application's main

page provided the user has registered and has correct login details to the applications

main page and correct finger print image to that effect.

4.2.1 Implementation Screenshots

This describes what each page of the application looks like. It describes how the user interacts

with the System and how the software communicates within itself. The followings are the

interfaces that will be interacting with the application user whenever it is run.

4.2.2 Application's Login Form

24

This is where the application user supplies his or her login details. It enables the user to access

the main page provided the user’s login details exists in the database with the proper case.

Fig 5: Application’s login form

4.2.3 Application's Main Page

This is the page that links other pages in the System. It is the first page that appears after a user’s

successful login .This page also displays different times an clocks in and out.

25

Fig 6: Application’s main page

4.2.4 Administrator Registration Form

This is a sub panel under the main page where records of new administrators are registered. The

process of registration is carried out by another administrator of the System.

26

Fig

7: Admin registration form

4.2.5 Pre-Populated Administrator Update Form

This form enables an administrator details to be updated. Before update, an administrator is

selected by username and the records of the administrator are populated in fields provided.

27

Fig 8: Pre-populated administrator update form

4.2.6 Populated Administrator Update Form

This form displays the interface of an already populated administrator form. The fields where

required details are populated are then edited. In some cases, some individuals may undergo

gender change which is why the gender filed is not disabled. This form also enables a user to

update their passport photograph.

28

Fig 9: Populated administrator update form

4.2.7 Administrator Finger Print Update Form

This form enables the thumb pint of an administrator to be updated provided the scanner was

connected to the System. The administrator may decide to start using the left thumb instead of

the right thumb should incase he/she was involved in an accident that caused his/her right thumb

to be set aside. This page enables a username to be selected such that the user’s finger print

image is fetched from database and replaced with a new image.

29

Fig 10: Form to update administrator finger print

4.2.8 Finger Print Scan Page

This form is used for the purpose of getting an image of a users finger print. This form also

furnishes the user with information about the scanner. The information enables the user to know

when the scanner is connected, when the scanner is disconnected from the System, when the

scanner interface is touched and when the scanner is refreshed.

30

Fig 11: Interface that enables user’s finger print scanning

4.2.9 Testing

After implementation, the following tests will take place:

Alpha test: is performed by the software developers before deployment.

Beta test: is performed by the users of the System.

31

CHAPTER FIVE

RECOMMENDATION AND CONCLUSION

5.1 RECOMMENDATION

The testing and evaluation of the result of this project show that adopting a student clock-in System will

greatly improve the keeping and management of Student records, reduce the cost of managing such

records, reduce paper work associated with keeping Student records, and improves data consistency. It

also ensures that Students constantly maintain punctuality at work. Therefore, I strongly recommend that

all establishments adopt and use a Student clock-in System to keep track of when students resume and

close for work.

5.2 CONCLUSION

In this project, we have presented a fingerprint-based attendance system. The developed system is an

embedded system that is part of a fingerprint recognition/authentication system based on. The system

extract the local characteristic of a fingerprint and templates are matched during both registration and

verification processes. The developed system is very helpful in saving valuable time of lecturer and

student. It also helps in generating reports at required time. The system can record the clock in and clock

out time of students in a very convenient manner using their fingerprint to prevent buddy-punching and

reduce level of absence. Also, it reduces most of the administrative jobs and minimizes human errors,

eliminates time-related disputes and helps to update and maintain attendance records.

32

REFERENCES

Automatic Face and Gesture Recognition (FG), 2000. pp. 462-467.

BioAPI (2001), BioAPI Specification, American National Standards Institute.

Brunelli (1993) - Introduction to Biometrics. Springer

In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 1999.

Vol. 1, pp. 274-280.

Jain, A. K. and Uludag, U. (2003), Hiding biometric data, IEEE Transactions on Pattern

Analysis and Machine Intelligence.

Jones M. andRehg J. (1999). Statistical color models with application to skin detection.

Kroenke, David M. and David J. Auer. Database Concepts. 3rd ed. New York: Prentice, 2007.

Kwawaguchi (2000) – Practical Demonstration of Biometric Recognition. India

Moon P. (2001) – Hand Geometry and Palm Print. USA

Rehg J. Rehg, T. Kanade. DigitEyes: Vision-Based Human Hand-Tracking. School of

Computer Science Technical Report CMU- CS-93-220, Carnegie Mellon University, December

1993.

Sato Y., Kobayashi Y., H. Koike. Fast tracking of hands and fingertips in infrared

Schneier, B. (1999), Inside Risk: The uses and abuses of biometrics.

Segen J. Segen, S. Kumar. Shadow gestures: 3D hand pose estimation using a single camera. In

Proceedings of IEEE Conference on Computer.

Trucco E. Trucco (1999), A. Verri. Introductory Techniques for 3D Computer Vision. Prentice

Hall, 1998.

Vision and Pattern Recognition (CVPR), 1999. Vol. 1, pp. 479-485.Images for augmented desk

interface. In proceedings of IEEE International Conference.

Zhang Z., Y. Wu, Y. Shan, S. Shafer (2001). Visual panel: Virtual mouse keyboard and 3rd controller

with an ordinary piece of paper. In Proceedings of Perceptual User Interfaces, 2001.

33

APPENDIX

Login Code

public class Login extends javax.swing.JFrame {

/**

* Creates new form Login

*/

Connection con;

PreparedStatementps;

ResultSetrs;

Statement st;

DefaultComboBoxModelcombomodel;

static String rowData[];

static Vector<String> data2 = new Vector<String>();

public static String fullname, user, pw;

public Login() {

initComponents();

URL url = this.getClass().getClassLoader().getResource("ThInc.png");

Image im = Toolkit.getDefaultToolkit().getImage(url);

setIconImage(im);

// jTextField1.setText(user);

}

/**

* This method is called from within the constructor to initialize the form.

* WARNING: Do NOT modify this code. The content of this method is always

* regenerated by the Form Editor.

*/

34

@SuppressWarnings("unchecked")

// <editor-fold defaultstate="collapsed" desc="Generated Code">

private void initComponents() {

jDesktopPane1 = new javax.swing.JDesktopPane();

jTextField1 = new javax.swing.JTextField();

jLabel1 = new javax.swing.JLabel();


jTextField2 = new javax.swing.JPasswordField();

jButton1 = new javax.swing.JButton();




setDefaultCloseOperation(javax.swing.WindowConstants.EXIT_ON_CLOSE);

setTitle("TheSA Login");

setLocationByPlatform(true);

setResizable(false);

addFocusListener(new java.awt.event.FocusAdapter() {

public void focusGained(java.awt.event.FocusEventevt) {

formFocusGained(evt);

}

});

addWindowListener(new java.awt.event.WindowAdapter() {

public void windowClosing(java.awt.event.WindowEventevt) {

formWindowClosing(evt);

}

});

jDesktopPane1.setBackground(new java.awt.Color(255, 255, 255));

jDesktopPane1.add(jTextField1);

35

jTextField1.setBounds(150, 40, 260, 30);

jLabel1.setFont(new java.awt.Font("BatangChe", 0, 18)); // NOI18N

jLabel1.setText("PASSWORD:");

jDesktopPane1.add(jLabel1);

jLabel1.setBounds(20, 100, 130, 30);

jLabel2.setFont(new java.awt.Font("BatangChe", 0, 18)); // NOI18N

jLabel2.setText("USERNAME:");



jTextField2.addKeyListener(new java.awt.event.KeyAdapter() {

public void keyPressed(java.awt.event.KeyEventevt) {

jTextField2KeyPressed(evt);

}

});

jDesktopPane1.add(jTextField2);

jTextField2.setBounds(150, 100, 260, 30);

jButton1.setText("Clear");

jButton1.addActionListener(new java.awt.event.ActionListener() {

public void actionPerformed(java.awt.event.ActionEventevt) {

jButton1ActionPerformed(evt);

}

});

jDesktopPane1.add(jButton1);

jButton1.setBounds(160, 170, 140, 30);

jButton2.setText("Exit");



36


}

});



jButton3.setText("Log In");




}

});



jLabel17.setFont(new java.awt.Font("Bodoni MT Poster Compressed", 1, 18)); // NOI18N

jLabel17.setHorizontalAlignment(javax.swing.SwingConstants.CENTER);

jLabel17.setBorder(javax.swing.BorderFactory.createTitledBorder(javax.swing.BorderFactory.cr

eateLineBorder(new java.awt.Color(0, 0, 0)), "STUDENT ATTENDANCE SYSTEM"));



javax.swing.GroupLayout layout = new javax.swing.GroupLayout(getContentPane());

getContentPane().setLayout(layout);

layout.setHorizontalGroup(

layout.createParallelGroup(javax.swing.GroupLayout.Alignment.LEADING)

.addComponent(jDesktopPane1, javax.swing.GroupLayout.DEFAULT_SIZE, 444,

Short.MAX_VALUE)

);

layout.setVerticalGroup(

layout.createParallelGroup(javax.swing.GroupLayout.Alignment.LEADING)

37

.addComponent(jDesktopPane1, javax.swing.GroupLayout.DEFAULT_SIZE, 235,

Short.MAX_VALUE)

);

pack();

}// </editor-fold>

private void jButton1ActionPerformed(java.awt.event.ActionEventevt) {

jTextField1.setText("");

jTextField2.setText("");// TODO add your handling code here:

}


int y = JOptionPane.showConfirmDialog(this, "Are you sure", "Exit",

JOptionPane.YES_NO_OPTION);

if (y == JOptionPane.YES_OPTION) {

System.exit(0);

} else {

setDefaultCloseOperation(DO_NOTHING_ON_CLOSE);

} // TODO add your handling code here:

}


try {

setCursor(Cursor.getPredefinedCursor(Cursor.WAIT_CURSOR));

Thread.sleep(400l);

} catch (Exception e) {

System.out.println("");

}

if (jTextField1.getText().isEmpty() || jTextField2.getText().isEmpty()) {

38

JOptionPane.showMessageDialog(this, "UserName Or Password Field Is Empty", "ERROR",

JOptionPane.INFORMATION_MESSAGE);

} else {

try {

LoadDriver();

String SQLCommand2 = "select username,pswd from admin";

rs = st.executeQuery(SQLCommand2);

ResultSetMetaData md2 = rs.getMetaData();

int nColumns2 = md2.getColumnCount();

while (rs.next()) {

rowData = new String[nColumns2];

for (int i = 0; i < nColumns2; i++) {

rowData[i] = rs.getObject(i + 1).toString();

data2.addElement(rowData[i]);

}

}

System.out.println("ok oo<><> " + data2);// && data2.contains(jTextField2.getText())

if (data2.contains(jTextField1.getText())) {

String SQLCommand = "select * from admin WHERE username='" +

jTextField1.getText() + "'";

rs = st.executeQuery(SQLCommand);

rs.next();

fullname = rs.getString(2);

user = rs.getString(3);

pw = rs.getString(4);

if (pw.equals(jTextField2.getText())) {

System.out.println("Na ur name be dix oo " + fullname + " " + user);

JOptionPane.showMessageDialog(this, fullname + ", Welcome User...", "Welcome",



jTextField1.setText("");

39

dispose();

new Main().setVisible(true);

} else {

JOptionPane.showMessageDialog(this, "Password Not Correct", "LogIn Error",

JOptionPane.ERROR_MESSAGE);

}

} else {

JOptionPane.showMessageDialog(this, "UserName Not Correct", "LogIn Error",


}

// con.commit();

con.close();

} catch (Exception g) {

System.out.print(g.getMessage());

}

}

MAINPAGE SOURCE CODE

public static DPFPTemplate template;

public static String cardno, company;

publicDPFPTemplategetTemplate() {

return template;

}

public void setTemplate(DPFPTemplate template) {

DPFPTemplate old = this.template;

this.template = template;

firePropertyChange(TEMPLATE_PROPERTY, old, template);

}

40

public void setTableAll() {

dataSet = new DefaultTableModel();

dataSet.addColumn("Fullname");

dataSet.addColumn("Matric Number");

dataSet.addColumn("Month");

dataSet.addColumn("TIME");

dis();

jTable2.setModel(dataSet);

}

public void setTable3() {

dataSet = new DefaultTableModel();

dataSet.addColumn("Fullname");

dataSet.addColumn("Matric Number");

dataSet.addColumn("Month");

dataSet.addColumn("TIME");

dis3();

jTable3.setModel(dataSet);

}

private void dis3() {

try {

LoadDriver();

int row = dataSet.getRowCount();

while (row > 0) {

row--;

dataSet.removeRow(row);

}

//execute query

// if (jList1.getSelectedValue().toString().equals("ALL")) {

41

// rs = st.executeQuery("Select surname,firstname,lastname,card,gender from member

ORDER BY card,dateOfRegistration,surname,firstname,lastname");

//

// } else {

rs = st.executeQuery("Select fullname,matricNo,month,timeOut from report where type='signout'

ORDER BY id");

// }

//get metadata

ResultSetMetaData md = rs.getMetaData();

intcolcount = md.getColumnCount();

Object[] data = new Object[colcount];

//extracting data

while (rs.next()) {

for (int i = 1; i <= colcount; i++) {

data[i - 1] = rs.getString(i);

}

dataSet.addRow(data);

}

// jLabel1.setText(" " + dataSet.getRowCount());

} catch (SQLException g) {

System.out.println(g);

}

}

private void dis() {

try {

LoadDriver();

int row = dataSet.getRowCount();

42

while (row > 0) {

row--;

dataSet.removeRow(row);

}

//execute query

// if (jList1.getSelectedValue().toString().equals("ALL")) {

// rs = st.executeQuery("Select surname,firstname,lastname,card,gender from member

ORDER BY card,dateOfRegistration,surname,firstname,lastname");

//

// } else {

rs = st.executeQuery("Select fullname,matricNo,month,timeIn from report where type='signin'

ORDER BY id");

// }

//get metadata

ResultSetMetaData md = rs.getMetaData();

intcolcount = md.getColumnCount();

Object[] data = new Object[colcount];

//extracting data

while (rs.next()) {

for (int i = 1; i <= colcount; i++) {

data[i - 1] = rs.getString(i);

}

dataSet.addRow(data);

}

//jLabel1.setText(" " + dataSet.getRowCount());

} catch (SQLException g) {

System.out.println(g);

}

}

43

STUDENT REGISTRATION SOURCE CODE

String sex;

if (jRadioButton1.isSelected()) {

sex = "Male";

} else {

sex = "Female";

}

if (jTextField10.getText().isEmpty() || jRadioButton1.getText().isEmpty() ||

jRadioButton2.getText().isEmpty()

|| jTextField9.getText().isEmpty()) {

JOptionPane.showMessageDialog(this, "Some Fields Are Empty...\n Recheck All Fields",

"Error", JOptionPane.ERROR_MESSAGE);

} else {

print = EnrollmentForm.print;

System.out.println("This is the finger gotten ma broda");

try {

LoadDriver();

// if (print == null || jLabel42.getText().isEmpty()) {

// JOptionPane.showMessageDialog(this, "Right thumb finger have not been

captured or Passport is Empty", "Error", JOptionPane.ERROR_MESSAGE);

if (jLabel42.getText().isEmpty()) {

JOptionPane.showMessageDialog(this, "Passport is Empty", "Error",


return;

} else {

File f = new File(jLabel42.getText());

FileInputStream in = new FileInputStream(f);

44

image = new byte[(int) f.length()];

in.read(image);

if (print != null) {

in.read(print);

}

}

// Below: the question marks are IN parameter placeholders.

// if (f.length() > 60000) {

// JOptionPane.showMessageDialog(this, "Cannot capture student image.", "Passport

Error", JOptionPane.WARNING_MESSAGE);

// return;

// }

String sql = "INSERT INTO registration VALUES(?,?,?,?,?,?,?,?,?,?,?,?)";

ps = con.prepareStatement(sql);

ps.setInt(1, 0);

ps.setString(2, jTextField1.getText());

ps.setString(3, jTextField10.getText().toUpperCase() + " " +

jTextField9.getText().toUpperCase());

ps.setString(4, sex);

ps.setString(5, jComboBox1.getSelectedItem().toString());

ps.setString(6, jTextField8.getText().toUpperCase());

ps.setString(7, jTextField2.getText().toUpperCase());

ps.setString(8, jFormattedTextField1.getText());

ps.setString(9, jDateChooser1.getDate().getDate() + " " + (jDateChooser1.getDate().getMonth()

+ 1) + " " + (jDateChooser1.getDate().getYear() + 1900));

ps.setString(10, jTextArea1.getText());

ps.setBytes(11, image);

ps.setBytes(12, print);

45

JOptionPane.showMessageDialog(this, "Student Has Been Registered.\n" +

jTextField10.getText().toUpperCase() + " is " + rand, "SUCCESSFUL",


ps.executeUpdate();

// con.commit();

System.out.println("you get muth");

con.close();

data.clear();

Orga2();

// enroller.clear();

// capturer.startCapture();

// jInternalFrame1.dispose();

// picture.setIcon(null);

// JOptionPane.showMessageDialog(rootPane,

jComboBox7.getSelectedItem().toString());


// }

} catch (HeadlessException | IOException | SQLException e) {

System.err.println("I hear Errors " + e);

}

}

FINGERPRINT CAPTURE FROM BIOMETRICS

public class CaptureFormEnrolling

extendsJDialog {

privateDPFPCapture capturer = DPFPGlobal.getCaptureFactory().createCapture();

privateJLabel picture = new JLabel();

privateJTextField prompt = new JTextField();

privateJTextArea log = new JTextArea();

46

privateJTextField status = new JTextField("[status line]");

Image im;

publicCaptureFormEnrolling(Frame owner) {

super(owner, true);

setTitle("ThInc Fingerprint Enrollment");

URL url = this.getClass().getClassLoader().getResource("ThInc.png");

Image im = Toolkit.getDefaultToolkit().getImage(url);

setIconImage(im);

log.setLineWrap(true);

setLayout(new BorderLayout());

rootPane.setBorder(BorderFactory.createEmptyBorder(10, 10, 10, 10));

picture.setPreferredSize(new Dimension(240, 280));

picture.setBorder(BorderFactory.createLoweredBevelBorder());

prompt.setFont(UIManager.getFont("Panel.font"));

prompt.setEditable(false);

prompt.setColumns(40);

prompt.setMaximumSize(prompt.getPreferredSize());

prompt.setBorder(

BorderFactory.createCompoundBorder(

BorderFactory.createTitledBorder(BorderFactory.createEmptyBorder(0, 0, 0, 0), "Prompt:"),

BorderFactory.createLoweredBevelBorder()

));

log.setColumns(40);

log.setEditable(false);

log.setFont(UIManager.getFont("Panel.font"));

JScrollPanelogpane = new JScrollPane(log);

logpane.setBorder(

BorderFactory.createCompoundBorder(

47

BorderFactory.createTitledBorder(BorderFactory.createEmptyBorder(0, 0, 0, 0), "Status:"),

BorderFactory.createLoweredBevelBorder()

));

status.setEditable(false);

status.setBorder(BorderFactory.createEmptyBorder(5, 5, 5, 5));

status.setFont(UIManager.getFont("Panel.font"));

JButton quit = new JButton("Exit");

// quit.setIcon(new javax.swing.ImageIcon(getClass().getResource("/images/Apps-session-

logout-icon.png")));

quit.addActionListener(new ActionListener() {

public void actionPerformed(ActionEvent e) {

setVisible(false);

}

});

48