8/14/2019 Ammad FYP

1/40

FINGERPRINT RECOGNITION AND PASSWORD SECURITY

SYSTEM

PROJECT SUPERVISOR

Dr. SYED WAQAR SHAH

PROJECT LEADER

AMMAD UDDIN

MEMBERS

AMEER ULLAH

RUMMAN KHAN

MUHAMMAD SHAKEEL

1

8/14/2019 Ammad FYP

2/40

INTRODUCTION

1.1 The era of biometrics

In the 21st century the use of biometric based systems have seen an

exponential growth. This is all because of tremendous progress in this field

making it possible to bring down their prices, easiness of use and its

diversified use in every day life. Biometrics is becoming new state of art

method of security systems. Biometrics are used to prevent unauthorized

access to ATM, cellular phones , laptops , offices, cars and many othersecurity concerned things. Biometric have brought significant changes in

security systems making them more secure then before, efficient and cheap.

They have changed the security system from what you remember (such as

password) or what you possess (such as car keys) to something you embody

(retinal patterns, fingerprints, voice recognition).

2

INTRODUCTION

8/14/2019 Ammad FYP

3/40

1.2 What is biometrics?

Biometrics is the science of verifying the identity of an individual

through physiological measurements or behavioral traits. Since biometric

identifiers are associated permanently with the user they are more reliable

than token or knowledge based authentication methods.

1.3 Advantages of Biometrics

Biometrics offers several advantages over traditional security

measures. Some of them are presented below.

1.3.1 Accuracy and Security

Biometrics based security systems are far most secure and accuratethan traditional password or token based security systems. For example a

password based security system has always the threat of being stolen and

accessed by the unauthorized user. Further more the traditional security

systems are always prone to accuracy as compared to biometrics which is

more accurate.

1.3.2 One individual, Multiple IDs

Traditional security systems face the problem that they dont give

solution to the problem of individuals having multiple IDs. For examples a

person having multiple passports to enter a foreign country. Thanks to

biometrics!!! They give us a system in which an individual cant possess

multiple IDs and cant change his ID through out his life time. Each individual

is identified through a unique Biometric identity throughout the world.

1.3.3 One ID, multiple individuals

In traditional security systems one ID can be used by multiple

individuals. For example in case of a password based security system a

single password can be shared among multiple individuals and they can

share the resources allotted to a single individual. Biometric based securitysystem doesnt allow such a crime. Here each individual has a single unique

ID and it cant be shared with any other individual.

1.4 Biometrics categories

Biometrics can be categorized in various categories as follow.

3

8/14/2019 Ammad FYP

4/40

1.4.1 Physical biometrics

This biometrics involves measurement of physical characteristics of

individuals. The most prominent of these include

Fingerprints

Face

Hand geometry

Iris scans

Fingerprints

Fingerprints recognition has been present for a few hundred years. Due

to tremendous research this field has reached such a point where the

purchase of fingerprint security system is quite affordable. For this reason

these systems are becoming more widespread in a variety of applications.

Fingerprint image.

Face

There has been significant achievement in face recognition system in

past few years. Due to these advancements this problem appears to be

eventually technologically feasible and economically realistic. In addition,

4

8/14/2019 Ammad FYP

5/40

current research involves developing more robust approaches that accounts

for changes in lighting, expression, and aging, where potential variations for

a given person are illustrated in Figure. Also, other problem areas being

investigated include dealing with glasses, facial hair, and makeup.

Facial Expression Image

Hand geometry

Hand geometry is one of the most basic biometrics in use today. A

two-dimensional system can be implemented with a simple document

scanner or digital camera, as these systems only measure the distances

between various points on the hand. Meanwhile, a three dimensional system

provides more information and greater reliability. These systems, however,

require a more expensive collection device than the inexpensive scanners

that can be used in a two-dimensional system. An example of a commercial

three-dimensional scanner is shown in Figure.

Commercial three-dimensional scanner

As seen in this image, the physical size of the scanner limits its

application in portable devices. The primary advantage of hand geometrysystems is that they are simple and inexpensive to use. Also, poor weather

and individual anomalies such as dry skin or cuts along the hand do not

appear to negatively affect the system. The geometry of the hand, however,

is not a very distinctive quality. In addition, wearing jewelry or other items on

the fingers may adversely affect the systems performance.

5

8/14/2019 Ammad FYP

6/40

Iris

Iris recognition has taken on greater interest in recent years. As this

technology advances, purchasing these systems has become more

affordable. These systems are attractive because the pattern variability of

the iris among different persons is extremely large. Thus, these systems canbe used on a larger scale with a small possibility of incorrectly matching an

imposter. Also, the iris is well protected from the environment and remains

stable over time. In terms of localizing the iris from a face, its distinct shape

allows for precise and reliable isolation. Figure shows the unique iris pattern

data extracted from a sample input.

Iris pattern

1.4.2 Behavioral biometrics

This category of biometrics is temporal in nature. They are evolved

during the life time of an individual. It involves measuring the way in which

an individual performs certain tasks. Behavioral biometrics include

Gait

6

8/14/2019 Ammad FYP

7/40

Handwriting

Speech

Signature

Now let discuss some the behavioral biometrics in a little detail.

Gait

Gait-based recognition involves identifying a persons walking style.

Although these systems are currently very limited, there is a significant

amount of research being conducted in this area. Furthermore, studies have

shown that gait changes over time and is also affected by clothes, footwear,

walking surfaces, and other conditions. Figure below outlines the various

stages of a gait cycle.

Samples recorded from a gait cycle

1.4.3 Chemical biometrics:

This is a new emerging field. It involves measuring of chemical or

biological composition of an individual different body parts such as

DNA

Blood glucose

1.5 Multi-biometric systems

Similar to multimodal systems, there are several other techniques

aimed at improving the performance of a biometric system, as outlined in

Figure below.

1.5.1 Multimodal

7

8/14/2019 Ammad FYP

8/40

Multimodal systems employ more than one biometric recognition

technique to arrive at a final decision. These systems may be necessary to

ensure accurate performance. Combining several biometrics in one system

allows for improved performance as each individual biometric has its own

strengths and weaknesses. Using more than one biometric also provides

more diversity in cases where it is not possible to obtain a particular

characteristic for a person at a given time. Although acquiring more

measurements increases the cost and computational requirements, the extra

data allows for much greater performance.

1.5.2 Multialgorithmic

These techniques acquire a single sample from one sensor and process

this signal with two or more different algorithms.

1.5.3 Multi-instance

These systems use a sensor to obtain data for different instances of

the same biometric, such as capturing fingerprints from different fingers of

the same person.

1.5.4 Multi-sensorial

8

8/14/2019 Ammad FYP

9/40

These systems sample the same biometric trait with two or more

different sensors, such as scanning a fingerprint using both optical and

capacitance scanners.

Multi-biometric categories

9

FINGERPRINT

IENTIFICATION

SYSTEM

8/14/2019 Ammad FYP

10/40

Fingerprint Identification system

2.1 A brief history

Fingerprints have been scientifically studied for many years in our

society. The characteristics of fingerprints were studied as early as 1600s.

Meanwhile, using fingerprints as a means of identification first occurred in

the mid-1800s. Sir William Herschel, in 1859, discovered that fingerprints do

not change over time and that each pattern is unique to an individual. With

these findings, he was the first to implement a system using fingerprints and

handprints to identify an individual in 1877. By 1896, police forces in India

realized the benefit of using fingerprints to identify criminals, and they began

collecting the fingerprints of prisoners along with their other measurements.

With a growing database of fingerprint images, it soon became

desirable to have an efficient manner of classifying the various images.

Between 1896 and 1897, Sir Edward Henry developed the Henry

Classification System, which quickly found worldwide acceptance within a

few years. This system allows for logical categorization of a complete set of

the ten fingerprint images for a person. By establishing groupings based on

fingerprint pattern types, the Henry System greatly reduces the effort of

searching a large database. Until the mid-1990s, many organizations

continued to use the Henry Classification System to store their physical files

of fingerprint images.

As fingerprints began to be utilized in more fields, the number of

requests for fingerprint matching began to increase on a daily basis. At the

same time, the size of the databases continued to expand with each passing

day. Therefore, it soon became difficult for teams of fingerprint experts to

provide accurate results in a timely manner. In the early 1960s, the FBI,

Home Office in the United Kingdom, and Paris Police Department began to

devote a large amount of resources in developing automatic fingerprint

identification systems. These systems allowed for an improvement in

10

8/14/2019 Ammad FYP

11/40

operational productivity among law enforcement agencies. At the same time,

the automated systems reduced funding requirements to hire and train

human fingerprint experts. Today, automatic fingerprint recognition

technology can be found in a wide range of civilian applications.

2.2 Fingerprint details

In this section structure and detail about fingerprint image will be

explained. We will emphasize and give greater detail of only those

terminologies which are related to our project.

2.2.1 What is a fingerprint?

A fingerprint is the feature pattern of ones finger as shown in figure

below. It is believed with strong evidence that these feature patterns areunique for each individual. So each individual has its own fingerprint with

permanent uniqueness. Thats why fingerprints have been used for

identification and forensic investigation for a long time.

A fingerprint image acquired by a sensor.

2.2.3 Fingerprint features

11

8/14/2019 Ammad FYP

12/40

A fingerprint pattern is composed of a sequence of ridges and valleys.

In a fingerprint image, the ridges appear as dark lines while the valleys are

the light areas between the ridges. A cut or burn to a finger does not affect

the underlying ridge structure, and the original pattern will be reproduced

when new skin grows. Ridges and valleys generally run parallel to each

other, and their patterns can be analyzed on a global and local level.

Ridges and valleys generally run parallel to each other, and their

patterns can be analyzed on a global and local level.

2.2.4 Global level

At the global level, the fingerprint image will have one or more regions

where the ridge lines have a distinctive shape.

2.2.5 Local level

While the global level allows for a general classification of fingerprints,

analyzing the image at the local level provides a significant amount of detail.

These details are obtained by observing the locations where a ridge becomes

discontinuous, known as minutiae points. Our project is also based on the

minutiae based recognition.

The most common types of minutiae are shown in Figure below.

12

8/14/2019 Ammad FYP

13/40

Ridge Types

In general, a ridge can either come to an end, which is called a

termination, or it can split into two ridges, which is called a bifurcation. The

other types of minutiae are slightly more complicated combinations of

terminations and bifurcations. For example, a lake is simply a sequence of

two bifurcations in opposing directions, while an independent ridge features

two separate terminations within a close distance. In our project we will use

only the ridge ending and ridge bifurcation for identification.

13

8/14/2019 Ammad FYP

14/40

Minutiae Types (Terminations and Bifurcations)

2.3 FINGERPRINT MATCHING TECHNIQUES

Two main approaches are used for fingerprint matching which are

described below.

2.3.1 Minutiae based

The first approach, which is minutia-based, represents the fingerprint

by its local features, like terminations and bifurcations. This approach has

been intensively studied, also is the backbone of the current available

fingerprint recognition products. I am also using this approach in my project.

2.3.2Image based

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

matching based on the global features of a whole fingerprint image. It is an

advanced and newly emerging method for fingerprint recognition. It is useful

to solve some problems of the first approach. But my project does not aim at

this method, so further study in this direction is not expanded in my thesis.

14

8/14/2019 Ammad FYP

15/40

15 SYSTEM DESIGN

8/14/2019 Ammad FYP

16/40

8/14/2019 Ammad FYP

17/40

Figure. Project

System Process flow Chart

Each of these stages will now be explained in detail in the next

chapter.

17

Post-processing

False minutiaeremoval

Minutiae Matching

8/14/2019 Ammad FYP

18/40

8/14/2019 Ammad FYP

19/40

0

00

00

00

000

500

3000

Histogram equalization is used to expand the pixels to all the intensity

values from 0 to 255. Some of the original images are very dark i.e. their

histogram is such that their intensity values are concentrated towards the

origin i.e. near zero intensity value. On the other hand some of the images

are very bright i.e. their intensity values are concentrated towards 255 and

nearby intensity values. After applying the histogram equalization the pixels

are distributed uniformly all over the intensity values from 0 to 255. Due to

this process the contrast of the image is increased and so the visual effect of

the image is increased.

0

200

400

600

800

1000

1200

1400

1600

1800

2000

0 50 100 150 200 250 Histogram of image before enhancement.

19

8/14/2019 Ammad FYP

20/40

Histogram of image after enhancement.

Fingerprint image

Before Histogram Equalization

Fingerprint image

After Histogram Equalization

4.2 Fingerprint Image Binarization

Fingerprint Image Binarization is to transform the 8-bit Gray fingerprint

image to a 1-bit image with 0-value for ridges and 1-value for furrows. After

20

8/14/2019 Ammad FYP

21/40

the operation, ridges in the fingerprint are highlighted with black color while

furrows are white.

First a threshold value from the image is selected using matlab

function graythresh. A threshold intensity value between 0 and 1 is

obtained using the above function. This value is then used to convert thegrey image to a black and white image. The value of pixel which is less then

the above threshold value calculated is taken as 0 representing the ridge in

black color. A value of the pixel in the image which is greater than the

threshold value calculated is then converted to 1 representing the white

color valley or furrow. The following image shows the image before and after

Binarization

Fingerprint

image before

Binarization.

Figure.Fingerprint

image after

Binarization

21

8/14/2019 Ammad FYP

22/40

Fingerprint image segmentation

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

each fingerprint image. The image area without effective ridges and furrows

is first discarded since it only holds background information. Then the bound

of the remaining effective area is sketched out since the minutia in the

bound region is confusing with that spurious minutia that is generated when

the ridges are out of the sensor.

4.3 ROI by morphological operations

Two Morphological operations called OPEN and CLOSE are adopted.

The OPEN operation can expand images and remove peaks introduced by

background noise. The CLOSE operation can shrink images and eliminate

small cavities.

4.4 Fingerprint Ridge Thinning

Ridge Thinning is to eliminate the redundant pixels of ridges till the

ridges are just one pixel wide. For this purpose I have used matlab built in

function bwmorph. This function repeats operation on the ridges until they

are one pixel wide and are suitable for minutiae extraction phase.

22

8/14/2019 Ammad FYP

23/40

5.1 Minutia Marking

After the fingerprint ridge thinning, marking minutia points is relatively

easy. In general, for each 3x3 window, if the central pixel is 1 and has exactly

23

MINUTIAE MATCHING

8/14/2019 Ammad FYP

24/40

3 one-value neighbors, then the central pixel is a ridge branch as shown in

figure. If the central pixel is 1 and has only 1 one-value neighbor, then the

central pixel is a ridge ending shown in figure below.

Bifurcation Termination

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

average inter-ridge width refers to the average distance between two

neighboring ridges. The way to approximate the D value is simple. Scan a

row of the thinned ridge image and sum up all pixels in the row whose value

is one. Then divide the row length with the above summation to get an inter-

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

several other rows and column scans are also conducted, finally all the inter-

ridge widths are averaged to get the D.

5.2 Post processing

Post processing mainly involves removal of the false minutiae from the

fingerprint image.

As described earlier, the crossing number algorithm is used again to

locate the terminations and bifurcations within the final thinned image. In

this process, the locations where the ridges end at the outer boundaries of

the image are classified as terminations. In the true sense, however, these

locations are not unique termination minutiae. Instead, they only appear as

terminations because the dimensions of the image force each ridge to come

to an end. Knowing this, these locations should not be recorded as minutiae

within the fingerprint. One way to eliminate such locations involves creating

an ellipse to only select minutiae points inside the fingerprint image.

24

101

010010

100

010000

8/14/2019 Ammad FYP

25/40

The center of the ellipse is established by locating the minimum and

maximum rows and columns that contain a ridge pixel, then calculating the

row and column that lie halfway between these extremes.

5.3 Minutiae Matching

The matching process involves comparing one set of minutiae data to

another set. In most cases, this process compares an input data set to a

previously stored data set with a known identity, referred to as a template.

The template is created during the enrollment process, when a user presents

a finger for the system to collect the data from. This information is then

stored as the defining characteristics for that particular user.

In our project since the whole process is done in matlab so there is no

need for database creation. We need to compare only two images. If both the

images are from the same fingerprint they are matched otherwise they are

unmatched. The following are the steps involved in the matching process.

1. First of all the two fingerprints which are to be matched are

load into the matching function.

2. Minutiae points i.e ridge ending and ridge bifurcation

extracted from both fingerprint images are laoded into the

function.

3. A built in matlab function isequal is used to compare the

two minutiae points i.e ridge ending and ridge bifurcation of

both the images with each other.4. If both the ridge endings and ridge bifurcations of the two

fingerprint images matches with each other, only then the

fingerprint images are matched otherwise they are

unmatched.

25

8/14/2019 Ammad FYP

26/40

26

PASSWORD SECURITY

8/14/2019 Ammad FYP

27/40

Password Security

Password?

A password is a secret word or string of characters that is used to

prove identity or gain access to a resource. The password must be kept

secret from those not allowed access.

Fingerprint is the state of art security measure as compared to

password and other conventional security methods. But in our project we

have summed up both the fingerprint and password security in order to

design a more secure and efficient security system. Since our project design

and simulation is in matlab so we have used matlab coding to implement this

task. The steps used in method are as follow.

1. First of all an array of passwords is saved in m file of matlab.

2. When a user wants to enter the system he must enter the password

to get access.

3. When the password is entered it is matched with the already stored

passwords in the m file.

4. If the entered password matches with any of the passwords in the

array stored in the m file then the password matches dialogue box appears.

5. If the password does not matches with any of the passwords stored

in the array then an unmatched messages is displayed and access is not

granted to the user.

27

http://en.wikipedia.org/wiki/Wordhttp://en.wikipedia.org/wiki/Character_(computing)http://en.wikipedia.org/wiki/Secrecyhttp://en.wikipedia.org/wiki/Character_(computing)http://en.wikipedia.org/wiki/Secrecyhttp://en.wikipedia.org/wiki/Word

8/14/2019 Ammad FYP

28/40

In our project the Password security works and is presented in the

following way as shown in different figures.

6.2 Password GUI

a). when the user enters the Enter Password button a dialogue boxappears asking the user to enter password.

Password GUI 1

28

8/14/2019 Ammad FYP

29/40

b). when the user enters the password and presses OKon the dialogue

box another dialogue box appears showing whether the password is matched

or not.

Password GUI 2

29

8/14/2019 Ammad FYP

30/40

30

SYSTEM GUI AND ITS

USER MANUAL

8/14/2019 Ammad FYP

31/40

7.1 GUI User Manual

We have presented our project in matlab GUI. Following are different

steps showing its operation.

1. Open the matlab 6.0 or above.

2. In the matlab command window type fig1 and press enter.

Matlab Command Window

31

8/14/2019 Ammad FYP

32/40

The following window will appear when we press enter in the above

figure.

Main GUI Window

There are three main panels in the main window as shown in the above

figure. There is a control panel, view panel and exit panel. Control panel have

two buttons of which match images is of main importance. The view panel

shows different buttons used for performing different operations on the two

images.

32

8/14/2019 Ammad FYP

33/40

3. From the file menu click open and load two images as show in the

figure below.

GUI Loading Images

4. After loading the two images we can view the images by clicking on

the view images button in the view panel as show below.

33

8/14/2019 Ammad FYP

34/40

GUI View Images

5. By clicking on the View Transform buttonin the View Panel we can

view the transformed image after different image preprocessing and

post processing algorithms applied as show below.

34

8/14/2019 Ammad FYP

35/40

Transformed Images

6. When we click the histograms button in the view panel, the

histograms of the two images are shown as in the figure below.

35

8/14/2019 Ammad FYP

36/40

Histogram of Original Images

36

8/14/2019 Ammad FYP

37/40

7. Click on the Hist Equalization to view the equalized histogram of

the two images as shown below.

Histogram Of Images After Equalization

8. when we click the match images, it compares the two images and

displays the result. If the two fingerprint images match then a

message box is shown showing Fingerprint matched, otherwise

37

8/14/2019 Ammad FYP

38/40

the message box displays Fingerprint dont match as shown

below.

Fingerprint Matching Result

9. In the Control Panel click on Enter Password to enter password for

authentication. When we click on this button user is asked to enter

password in the matlab command window. If the password is

matched a message box appears showing that the password is

matched otherwise the message box shows that the password did

not match.

38

8/14/2019 Ammad FYP

39/40

Password Matching Result

39

CONCLUSION

8/14/2019 Ammad FYP

40/40

Conclusion

In our project we have combined biometric i.e. fingerprint and

conventional security i.e. password security system. The conventional

password security has several advantages as well as disadvantages. Those

disadvantages have been overcome by using it with fingerprint securitysystem. So overall system has both the advantages of the state of art

biometric and conventional security system which makes it more powerful

than either of the two security measures. The efficiency of the fingerprint

security system mainly depends upon the accuracy and the response time.

Keeping these two things in mind we have used robust algorithms giving us

fast response time and accuracy.

Future work

Following are the some of the recommendations and future work that

can be done in order to enhance the system further.

1. There is a need in fingerprint security system to detect

whether the fingerprint is from a living user or not.

Experiments have shown that fingerprint security systems can

be fooled by using copy of the fingerprint from a user.

2. Several biometric may be combined i.e. multi biometric

security system may be used in order further increase the

security and efficiency.

3. It is obvious that biometric systems will govern the security

domain in future electronic world. So due increase in usage

the identification speed and accuracy will be the crucial

factors. Therefore the algorithms may be optimized in such a

way in order to meet these demands.