ATM Simulation Using Fingerprint Verification

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ATM Simulation with Fingerprint Verification Submitted to Amity University Uttar Pradesh

in partial fulfillment of the requirements for the award of the Degree of Bachelor of Technology in Computer Science & Engineering by Shashank Verma Tanya Gupta under the guidance of Mrs. Divya Sharma




We, Tanya Gupta and Shashank Verma student(s) of B.Tech (Computer Science And Engineering) hereby declare that the project titled “ATM Simulation with Fingerprint Verification” which is submitted by us to Department of Computer Science And Engineering, Amity School of Engineering and Technology, Amity University Uttar Pradesh, Noida, in partial fulfillment of requirement for the award of the degree of Bachelor of Technology in Computer Science And Engineering, has not been previously formed the basis for the award of any degree, diploma or other similar title or recognition.



On the basis of declaration submitted by Tanya Gupta and Shashank Verma, student(s) of B. Tech Computer Science And Engineering, I hereby certify that the project titled “ATM simulation using Fingerprint Verification” which is submitted to Department of Computer Science And Engineering, Amity School of Engineering and Technology, Amity University Uttar Pradesh, Noida, in partial fulfillment of the requirement for the award of the degree of Bachelor of Technology in Information Technology is an original contribution with existing knowledge and faithful record of work carried out by them under my guidance and supervision.

To the best of my knowledge this work has not been submitted in part or full for any Degree or Diploma to this University or elsewhere.




Department of Computer Science And Engineering



The need to control access to certain information and resources has been taken seriously nowadays due to fraud and other threats to current security systems. This research believes that that no single method, algorithm, key or procedure is entirely secure. Hence a combination of multiple security components is mandatory to provide a high level of protection against fraud and other threats. This project is about enhancing the security feature of an ATM feature by fingerprint verification. It looks into the vulnerabilities of ATM cards, Personal Identification numbers (PIN) or passwords widely used in systems today. As a result, the aim of the project is to propose a framework for user identification and authentication in automated teller machines (ATM) as opposed to PIN. This robust method of user identification and authentication would hopefully reduce the vulnerabilities of ATM in the future.


Our sincere gratitude goes to all those who cooperated and showed unconditional interest in helping us out in this project work.


This Project is a result of the persistent guidance, enthusiastic views and provoking suggestions of our Faculty Guide, Mrs.Divya Sharma. We well appreciate the time she took out from his busy schedules to tend to all our queries. We express our genuine gratitude to him for welcoming and accepting our ideas, for providing all the facilities needed during the project development and for always bringing out the best in us. We must acknowledge our deep debt of gratitude to numerous faculty members whose great and masterly work we have consulted during the preparation of this Final Year Project. Last but never the least, we would like to acknowledge the ongoing support of our respective family members as well as friends who helped us throughout our project by providing moral support and helping us in solving problems we faced during the project.

Date –


Tanya Gupta

Shashank Verma


CONTENTS Candidate’s Declaration










List of Figures List of Tables CHAPTER 1



1.1 Problem Statement . . . . . . . . . . . . . . . . . . . . . . . . . . . .


1.2 Motivation and Challenges . . . . . . . . . . . . . . . . . . . . . . . .


1.3 Using Biometrics . . . . . . . . . . . . . . . . . . . . . . . . . . . . .


1.4 What is fingerprint?

. . . . . . . . . . . . . . . . . . . . . . . . . . .


1.5 Why use fingerprints? . . . . . . . . . . . . . . . . . . . . . . . . . . .


1.6 Using fingerprint recognition system for ATM System. . . . . . . . .


1.7 Organization of the thesis



. . . . . . . . . . . . . . . . . . . . . . . .



2.1 Hardware - Software Level Design . . . . . . . . . . . . . . . . . . . .


2.2 Attendance Management Approach . . . . . . . . . . . . . . . . . . .



2.3 On-Line Attendance Report Generation . . . . . . . . . . . . . . . . .


2.4 Network and Database Management


. . . . . . . . . . . . . . . . . .

2.5 Using wireless network instead of LAN and bringing portability

. . .


Using Portable Device . . . . . . . . . . . . . . . . . . . . . .


2.6 Comparison with other student attendance systems . . . . . . . . . .





3.1 How Fingerprint Recognition works? . . . . . . . . . . . . . . . . . .


3.2 Fingerprint Identification System Flowchart


. . . . . . . . . . . . . .

9 Conclusion


9.1 Outcomes of this Project . . . . . . . . . . . . . . . . . . . . . . . . .


10 Future Work and Expectations


10.1 Approach for Future Work


. . . . . . . . . . . . . . . . . . . . . . .


List of Figures

1.1 Example of a ridge ending and a bifurcation . . . . . . . . . . . . . .


2.1 Hardware present in classrooms . . . . . . . . . . . . . . . . . . . . .


2.2 Classroom Scenario . . . . . . . . . . . . . . . . . . . . . . . . . . . .


2.3 Network Diagram . . . . . . . . . . . . . . . . . . . . . . . . . . . . .


2.4 ER Diagram . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .


2.5 Level 0 DFD . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .


2.6 Level 1 DFD . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .


2.7 Level 2 DFD . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .


2.8 Portable Device . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .


3.1 Fingerprint Identification System Flowchart



. . . . . . . . . . . . . .

List of Tables


2.1 Estimated Budget . . . . . . . . . . . . . . . . . . . . . . . . . . . . .



Chapter 1 Introduction

Over the past three decades, consumers have been largely depending on and trust the Automatic Teller Machine, better known as ATM machine to conveniently meet their banking needs. Using an ATM, customers can access their bank accounts in order to make cash withdrawals, debit card cash advances, and check their account balances as well as purchase prepaid cellphone credit. Most ATMs are connected to interbank networks, enabling people to withdraw and deposit money from machines not belonging to the bank where they have their accounts or in the countries where their accounts are held (enabling cash withdrawals in local currency).


Problem Statement

Designing a prototype model of ATM system and enhanching its security with the help of fingerprint recognition technique.


Figure 1. Context diagram for the prototype system 1.2

Motivation and Challenges

Despite the numerous advantages of ATM system, ATM fraud has recently become more widespread. Fraud techniques such as card skimming, shoulder surfing etc. have been observed recently. In order to increase the level of security of the ATM networks use of a biometric technique for verification along with existing PIN has been thought of a solution to decrease the increasing number of frauds. Also in rural areas people are not educated enough to use the ATM system. So, use of only biometric verification can help those people access the ATMs in an easier manner and hence increase its popularity among rural masses. We tried to develop a prototype model for the same, which would use PIN number along with the fingerprint verification scheme to verify the user before he/she can access his /her account and make the transactions. However ATMs using single layer of verification ie. Biometric verification can also be developed using our prototype model.


Using fingerprint recognition system for ATM system


Managing the security of the ATM system is a challenging task. This could be achieved only by increasing the levels of protection of the system. It can be done with the help of a fingerprint identification system developed in this project. This fingerprint identification system uses existing as well as new techniques in fingerprint recognition and matching. A new one to many matching algorithm for large databases has been introduced in this identification system.


Chapter 2 Literature Review 2.1 Biometrics Biometrics is a technique that analyzes human characteristics to distinguish one person from another. It uses unique, measurable characteristics or traits of a human being for automatically recognizing or verifying identity. Measurable biometrics characteristics can be divided into two categories. The first category is the physical characteristics consisting of the eye, the iris, the retina, the face, the fingerprints, hand geometry, finger geometry, palm print, vein patterns, and ear shape. The second category is the behavioral characteristics such as signature, voice, keystroke, and body odor. Nine biometrics technologies have been compared and it was concluded that fingerprinting is the only technology that is legally accepted, readily automated and matured which has been used and accepted 14 in forensic application since the 1970s. Although signatures are also legally acceptable biometrics, they are facing issues on accuracy, forgery and behavioral variability for automatic identification. Therefore, the best biometrics technique is fingerprint recognition, since it is the most mature technology and has been accepted all over the world.

2.1.1 The Fingerprint The fingerprint is the easiest „something you are‟ characteristic to capture and process. It is also very easy for a user to supply and the technology is neither invasive nor inconvenient. In fact, among all the biometrics techniques, fingerprint based identification is the oldest method which has been successfully used in numerous applications. Fingerprinting is one of the most mature technologies and considered legitimate evidence in courts of law all over the world (Jain er al., 2001). It is also used in forensic investigations. Recently, an increasing number of civilian and commercial applications are either using or actively considering 14

using fingerprint based identification because of a better understanding of 15fingerprints and furthermore, its matching perfonnance is better than any other existing biometrics technologies (Jain er al. , 2001). A fingerprint is believed to be unique to each person and also each finger. It is unique in terms of the arrangement of its minutiae. Even identical twins have different fingerprints and they do not change over time. This can be described by the probabilistic model as: P(C) = P(N).P(M).P(A) (2.1) where: P(N) = f(Poisson‟s Law) P(M) = f(frequency of appearance of minutiae type) P(A) = f(no. of possible permutations of minutiae) From the probabilistic model, it is calculated that the probability of finding two identical fingerprints is one over eight billions fingerprints.

2.1.2 Capturing Fingerprint Methods There are two ways of capturing the fingerprint image: inked (offline) and live scan (ink less). For the inked methods, a fingerprint is obtained by an impression of it on a paper and then scanned using a flatbed document scanner. This method is usually used in law enforcement to identify suspects from the crime scene. In the live scanning, there are three main methods to capture fingerprint images: the optical, the capacitive and the thermo conductive. The optical method is implemented with a small camera and light source to capture an image of a fingerprint. The capacitive method makes full use of the human body‟s natural electrical charge to measure the differences in capacitance value between ridges and valleys in a fingerprint; algorithms are then used to construct an image from the capacitance values. The last method, which is the thermo conductive method, is done by measuring the human tissue characteristic thermal conductivity differences between the ridges and the valleys of a fingerprint. In other words, the ridges and valleys conduct heat at different rates and these differences can be registered. The last two methods are reliable for differentiating a living 15

finger and a dead finger.

2.1.3 Fingerprint Representation Fingerprint representation, commonly known as template, can be classified into two types: local and global. For the local type of fingerprint representation, the information of the fingerprint is based on the entire image, finger ridges, and pores on the ridges or salient features derived from the ridges (Jain et al., 2001). Representation predominantly based on ridge endings or bifurcations is the most common.

Figure 2 1: Ridge ending and ridge bifurcation. The combination of ridge endings and ridge bifurcations is known as minutiae. Representations of fingerprints based on minutiae are made because the captured minutia consist of individual information and is storage efficient and its detection is relatively robust to various sources of fingerprint degradation. The template relies on the minutiae locations and the directions of the ridges. For the global type, on the other hand, the information is contained in the global pattems of ridges, which provides more infonnation including the fingerprint critical points such as core and delta (Figure 2 2). It can be used in a large scale fingerprint identification system which classified the fingerprints into categories based on the information contained in the global patterns of ridges. The classification elaborates methods of manual systems to index individuals into bins based classification of their fingerprint. These methods eliminate the need to match an input fingerprint(s) to the entire fingerprint database in identification system, hence reducing the computing requirements. Figure 2 2: Sample fingerprint with core and delta marked 16

2.1.4 Feature Extraction Feature extraction deals with detecting the ridge endings and the ridge bifurcations from the input fingerprint images. In practice, it is not always possible to obtain a perfect ridge map in an input fingerprint due to a number of factors such as aberrant formations of epidermal ridges of fingerprints, postnatal marks, occupational marks, etc.. Therefore in order to get a better feature of the fingerprint, there are five methods to extract the feature on the fingerprint images:

i. Orientation Estimation Orientation estimation deals with the orientation field of a fingerprint image which represents the directionality of ridges in the fingerprint image. The fingerprint image is divided into a non overlapping block such as 32 by 32 pixels and an orientation representative of the ridges in the block. The block orientation could be determined from the pixel gradient using either averaging method, voting method or optimization method (Jain et al., 2001). ii. Segmentation Segmentation is done to localize the portion of a fingerprint image depicting the finger (foreground). Two ways of segmentation are known as global or adaptive thresholding. A reliable approach for segmentation exploits the fact that there is a significant difference in the magnitudes of variance in the gray levels along and across the flow of a fingerprint ridge. The block size for variance calculation typically spans one to two inter ridge distances.

iii. Ridge Detection Ridge detection can be done by either the simple or the thresholding approach. These approaches might not work for noisy and low contrast portions of a fingerprint image. The important criterion is the gray level values of the ridges on a fingerprint image which attain their local maxima along a direction normal to the local ridge orientation. Based on these criteria, pixels are identified to be the ridge pixels. Extraction of ridges can 17

be thinned or cleaned using standard thinning and cormected components algorithms.

iv. Minutiae Detection After the thinning process is done, the ridge pixels with three ridge pixel neighbors are identified as ridge bifurcations and those with one ridge pixel neighbor is identified as ridge endings. However, the minutiae are not genuine due to image processing artifacts and noise present in the fingerprint image.

v. Postprocessing Finally, genuine minutiae are gleaned from the extracted minutiae using a number of heuristics. For example, too many minutiae in a small neighborhood may indicate the presence of noise and can be discarded. Very close ridge endings oriented anti parallel to each other may indicate spurious minutiae generated by a break in the ridge due to poor contrast or a cut in the finger. Two closely located bifurcations sharing a common short ridge suggest the presence of extraneous minutiae generated by bridging of adjacent ridges as a result of dirt or image processing artifacts.

2.1.5 Fingerprint Matching The fingerprint is matched by comparing the captured image and the present image provided by the user. The objective of fingerprint matching is to determine whether the prints represent the same finger or not. Users are identified by using several approaches either image based, ridge pattem based or point (minutiae) pattern based fingerprint representations. The point pattern matching (minutiae matching) approach facilitates the design of a robust, simple and fast verification algorithm while maintaining a small template size. The matching phase defines the distance metric between two fingerprint representations and determines whether a given pair of representations is captured from the same finger (known as mated pair). The determination is based on whether this quantified distance is greater than a certain threshold. 18

21Its distance metric or similarity is based on the concept of correspondence in minutiae based matching. A minutia in the presented fingerprint and a minutia in the stored fingerprint template are said to be corresponding if they are identical. Characteristics registered for fingerprint matching includes the core, which approximates the centre of the pattern, and the axis, which represents the vertical orientation of the finger Figure 2 3: A fingerprint image showing core, axis marker, and marked minutae


Chapter 3 Project Design and Implementation The application will employ on the front-end and Microsoft Access on the back-end. 4.1 Hardware Requirements (i) Any screen resolution (more than or equal to 800 X 600) would work. (ii) Pentium IV processor, or above (iii) Fingerprint scanner – URU4000B

4.2 Software Requirements (i) Windows XP/Vista (ii) Microsoft Visual Studio 2008 (iii) Microsoft Access


Hardware - Software Level Design

Required hardware used should be easy to maintain, implement and easily available. Proposed model consists following parts: (1)Fingerprint Scanner, (2)Computer

Fingerprint scanner

will be used to input

fingerprint of customers into the

computer software. LCD display will be displaying the facilities that the customer can avail and make the transactions. Computer Software will be interfacing fingerprint scanner and LCD. It will input fingerprint, will process it and extract features for matching. After matching, it will update database entries of the customer and keep a record of any transaction made by him/her.


Figure 2.1: Fingerprint scanner



Our system integrate biometric identification into normal, traditional authentication technique use by electronic ATM machines nowadays to ensure a strong, unbreakable security and also non-repudiate transactions. In order to demonstrate the strength of our proposed authentication protocol using the combination of three authentication methods of card, PIN and fingerprint, we used U.are.U 4000 fingerprint biometrics development kit manufactured by Digital Persona Software Limited.

The proposed design involves two phases namely registration phase and verification phase. Each of the phases is briefly describe below.

Registration Phase Prior to an individual being identified or verified by a biometric device, the registration process must be completed. The objective of this registration process is to create a profile of the user. This process is carried out by the administrator of the system. The process consists of the following two steps:

1. Sample Capture: The user allows three biometric readings by placing a finger on a fingerprint reader. The quality of the samples, together with the number of samples taken, will influence the level of accuracy at the time of validation. Not all samples are stored; the technology analyzes and measures various data points unique to each individual. The number of measured data points varies in accordance to the type of device. 21

2. Conversion and Encryption: The individual‟s measurements and data points are converted to a mathematical algorithm and encrypted. These algorithms are extremely complex and cannot be reversed engineered to obtain the original image. This algorithm is further stored in the database or server.

Figure. Flowchart for the registration process

Identification and Verification - Once the individual has been enrolled in a system, he/she can start to use biometric technology to have access to his account via the ATM machine to authorize transactions.


1. Identification: a one-to-many match. The user provides a biometric sample and the system looks at all user templates in the database. If there is a match, the user is granted access, otherwise, it is declined.

2. Verification: a one-to-one match requiring the user provides identification such as a PIN and valid ATM card in addition to the biometric sample. In other words, the user is establishing who he/she is and the system simply verifies if this is correct. The biometric sample with the provided identification is compared to the previously stored information in the database. If there is a match, access is provided, otherwise, it is declined.

Figure. Flowchart for the verification process

After the verification process, the user can carry on with his/her transactions such as balance inquiry, balance withdrawal, balance transfer etc. 23

3.1 User Interfaces 

Administrator interface. Will enable administrator to add new users, view existing users and delete users.

User interface. Will enable users to acess their accounts and make necessary transactions.

3.2 Operations The product release covers all automated aspects of the database. The tables in the database have to be maintained on the server side.

3.3 Product Functions The System will allow access to two kind of users(administrator and customers).The administrator shall enter the details of the customers with the primary key being their scanned fingerprint images . The Log-in times shall be monitored as a real time system, since as soon as the customer authenticates himself/herself he/she is allowed to make the required transactions. The system shall have all the transaction details entered into the database, hence a proper account would be maintained of the customers transactions.

3.4 Constraints Due to limited features in the Standalone Development, simultaneous log-ins of the Users (i.e user and Administrator) is not feasible.

3.10 EXTERNAL INTERFACES (i) Login Screen: Input: login PIN Data format: text or numeric Output destination: Database table

(ii) Registration Screen: 24

Input: Username , fingerprint image, balance Data format: text or numeric Output destination: Database table

(iii)Home Page: Displays the basic information of the user logged in along with the facilities the user can avail. Data format : text, numeric , pictorial.

(iv)Withdraw Amount Screen: Requests you to enter the amount you want to withdraw from your account. Data format : text, numeric , pictorial.

(v)Balance Inquiry Screen: Informs you how much balance you have in your account. Data format : text, numeric , pictorial.

(vi)Mini Statement Screen: Informs you about your last 5 transactions. Data format : text, numeric , pictorial.

(vii)Funds transfer Screen: Requests you to enter the account number and the amount of money you want to transfer to that account. Data format : text, numeric , pictorial.

. 5.6

Comparison with other ATM systems


Typically, a user inserts into the ATM a special plastic card that is encoded with information on a magnetic strip. The strip contains an identification code that is transmitted to the bank's central computer by modem. To prevent unauthorized transactions, a personal identification number (PIN) must also be entered by the user using a keypad. The computer then permits the ATM to complete the transaction; most machines can dispense cash, accept deposits,









Figure. ATM authentication process

Integrating ATM system with the biometric authentication techniques is a solution to avoid the fraud. Biometric authentication ensures that a person is actually present rather than their cards and passwords without requiring the user to remember anything. Among all the biometrics, fingerprint based identification is one of the most mature and proven technique. Banks can choose different authentication schemes for their customers at their ATM‟s ie single level (only biometric authentication) or dual level authentication (PIN combined with biometric authentication).

Figure. Proposed prototype


5.7 Fingerprint Identification System

An identification system is one which helps in identifying an individual among many people when detailed information is not available.

It may involve matching

available features of customer like fingerprints with those already enrolled in database.


How Fingerprint Recognition works?

Fingerprint images that are found or scanned are not of optimum quality.

So we

remove noises and enhance their quality. We extract features like minutiae and others for matching. If the sets of minutiae are matched with those in the database, we call it an identified fingerprint.

After matching, we perform post-matching steps which

may include showing details of identified candidate, marking attendance etc. A brief flowchart is shown in next section.

5.8. Fingerprint Identification System Flowchart

A brief methodology of our Fingerprint Identification System is shown here in following flowchart. Each of these are explained in the later chapters.


Figure 3.1: Fingerprint Identification System Flowchart



Set of definable

Start Date

End Date

Cost estimated






Rs. 5800






Rs. 500

tasks 1.



reviewing literature for the project 2.

Purchasing the device






coding modules 3.



error correction; debugging; Change management 4.

Report generation

TABLE 1: Schedule for the project completion


Chapter 4 Simulation 4.1 VB.NET Visual Basic .NET is one of the two flagship languages (with C#) for the .NET framework from Microsoft. Despite being called Visual Basic, it is actually not backwards-compatible with VB6, and any code written in the old version will not compile under VB.NET. As a language, Visual Basic.NET has the following traits: Object-Oriented As with all .NET languages, VB.NET includes full-blown support for object-oriented concepts, including simple inheritance. Everything in VB.NET is an object, including all of the primitives (Short, Integer, Long, String, Boolean, etc.) as well as types, events, and even assemblies. Everything inherits from the Object base class. Event-Driven All previous versions of Visual Basic were event-driven, but this feature is heavily enhanced under the .NET framework. Events are no longer recognized because they use a certain naming convention (ObjectName_EventName), but now are declared with a Handles ObjectName.EventName clause. Event handlers can also be declared at runtime using the AddHandler command. .NET Framework As the name implies, VB.NET runs on top of Microsoft's .NET framework, meaning the language has full access to all of the supporting classes in the framework. It's also possible to run VB.NET programs on top of Mono, the open-source alternative to .NET, not only under Windows, but even Linux or Mac OSX. 4.2 Microsoft Access Microsoft Office Access, previously known as Microsoft Access, is a database management system from Microsoft that combines the relational Microsoft Jet Database Engine with a graphical user interface and software-development tools. It is a member of the Microsoft Office suite of applications, included in the Professional and higher editions or sold separately. On May 12, 2010, the current version of Microsoft Access 2010 was released by Microsoft in Office 2010; Microsoft Office Access 2007 was the prior version. 30

MS Access stores data in its own format based on the Access Jet Database Engine. It can also import or link directly to datastored in other applications and databases.[1] Software developers and data architects can use Microsoft Access to develop application software, and "power users" can use it to build software applications. Like other Office applications, Access


supported by Visual

Basic for Applications, an object-

oriented programming language that can reference a variety of objects including DAO (Data Access Objects), ActiveX Data Objects, and many other ActiveX components. Visual objects used in forms and reports expose their methods and properties in the VBA programming










Windows operating-system functions.

4.3 U.are.U 4000B Fingerprint Scanner The U.are.U 4000B is a USB fingerprint reader designed for use with DigitalPersona‟s enterprise software applications and developer tools. The user simply places their finger on the glowing reader window, and the reader quickly and automatically scans the fingerprint. On-board electronics calibrate the reader and encrypt the scanned data before sending it over the USB interface. DigitalPersona products utilize optical fingerprint scanning technology for superior quality and product reliability. The U.are.U 4000B Reader and DigitalPersona fingerprint recognition software engine have an unmatched ability to recognize even the most difficult fingerprints.


Chapter 5 Discussion of Results


Chapter 6 Conclusion Throughout the project, the focus has been on enhancing the process of identification and authentication on the automatic teller machine (ATM). The project has proposed the use of the fingerprint as a suitable substitution for the personal identification number (PIN). This chapter summarizes the analysis for justifying the use of fingerprint and smart card, then concludes the project and states future directions.

6.1 The Fingerprint vs. the Personal Identification Number (PIN)

Fingerprints are the most acceptable biometrics all over the world in identifying a person. It is the characteristic that can prove a person is the person he/she claims to be. It is also the mature technology for automated identification systems because it has evolved way back since 1970. Until now some governments in the world are still implementing fingerprint techniques to identify their citizens, and the criminal from the scene of crimes in forensic work. In this research, fingerprint is chosen for its uniqueness, ease of use and also convenience to the user. From the experience and the analysis on the prototype, the advantages of the fingerprint are listed below:

a. Fingerprints cannot be stolen, lost or inadvertently passed to others as the fingerprint is always possessed by its owner. b. It is not transferable, as it always attached to the body. c. The user does not have to memorize the fingerprint. The only memory needed by the user is which finger they need to use to gain access. d. The uniqueness of a finger can be used repeatedly to gain access to other applications as well without fear of it being duplicated. The prototype system has implemented fingerprint identification to carefully identify the authorized user for accessing the system, gain services offered and access their account information. 33

From the analysis and experience gained throughout the research, it is found that the fingerprint technology is the best technology to be used in identifying user. With the help of the SDK, the implementation of the fingerprint was relatively easy. Without the SDK, the prototype development process would be time consuming and complicated. The prototype system as a whole has integrated the fingerprint technologies for user identification and authentication in accessing the ATM. Database used in the research provides some data for the client system to do the operations normally done by the ATM. Although it is a simple database, it is adequate enough for the user to carry out the transaction using the client system. Even though the system has not been tested using the actual machine, the system was found to be better than the conventional ATM system based on the advantages possessed by the main components (i.e., fingerprint). The research has shown that using „something you are‟ (i.e., fingerprint), would be a better identification method rather than using „something you know‟ (i.e., PIN). This robust prototype system that relies on some unchanging, difficult to forge entity will hopefully reduce the ATM vulnerabilities.


Chapter 7 Future Prospects


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