17264412 Fingerprint and Password Security System Thesis


Transcript of 17264412 Fingerprint and Password Security System Thesis

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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 other security 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).

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.

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1.3.1 Accuracy and Security

Biometrics based security systems are far most secure and accurate than

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 don’t 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 can’t possess multiple IDs and can’t change his ID through out his

life time. Each individual is identified through a unique Biometric identity throughout the


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 security system doesn’t allow such a crime. Here each

individual has a single unique ID and it can’t be shared with any other individual.

1.4 Biometrics categories

Biometrics can be categorized in various categories as follow.

1.4.1 Physical biometrics

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This biometrics involves measurement of physical characteristics of individuals.

The most prominent of these include



Hand geometry

Iris scans


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.


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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, 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


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.

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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 geometry systems 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 system’s performance.


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 can be 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.

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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





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

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Gait-based recognition involves identifying a person’s 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


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

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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

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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

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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.



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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

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?

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A fingerprint is the feature pattern of one’s finger as shown in figure below. It is

believed with strong evidence that these feature patterns are unique for each individual.

So each individual has its own fingerprint with permanent uniqueness. That’s 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

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.

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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.

Ridge Types

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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.

Minutiae Types (Terminations and Bifurcations)


Two main approaches are used for fingerprint matching which are described


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.

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2.3.2 Image 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.

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A fingerprint recognition system consists of image acquisition, minutiae extractor

and minutiae matcher. In our project there is also a password security added so a

password acquisition and password matcher is also needed.

For fingerprint acquisition, optical or semi-conduct sensors are widely used. But

for my project I have used the available fingerprints on the net for testing. So no

acquisition stage is implemented.

The minutia extractor and minutia matcher modules are explained in detail in the

next part for algorithm design and other subsequent sections.

3.2 Algorithmic Level Design

To implement our project a four stage approach has been used. These four

stages are given in the figure below.


Image enhancement Image Binarization Image segmentation

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Figure. Project

System Process flow Chart

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

Minutiae Extraction Thinning Minutiae marking

Post-processing False minutiae


Minutiae Matching

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4.1 Fingerprint Image Enhancement

The performance a fingerprint recognition system basically depends upon the

quality of the input image. Since the images acquired with different kinds of sensors are



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not of the perfect quality and so they can’t be used directly for the matching. Therefore

to ensure the accurate working of the system the image is first enhanced using different

algorithms and image processing techniques.

I have used histogram equalization in my project for the fingerprint image

enhancement. It is explained below.

4.1.1 Histogram Equalization

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.

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0 50 100 150 200 250












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

Histogram of image after enhancement.

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Fingerprint image Before Histogram Equalization

Fingerprint image After Histogram Equalization

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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 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 the grey 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.

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Figure. Fingerprint image after Binarization

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.

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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.

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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 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

1 0 1 0 1 0 0 1 0

1 0 0 0 1 0 0 0 0


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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


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.

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.

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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


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.

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Password Security


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


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


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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.

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 box appears

asking the user to enter password.

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Password GUI 1

b). when the user enters the password and presses OK on the dialogue box

another dialogue box appears showing whether the password is matched or not.

Password GUI 2

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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.

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Matlab Command Window

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

Main GUI Window

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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.

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


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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.

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GUI View Images

5. By clicking on the View Transform button in the View Panel we can view

the transformed image after different image preprocessing and post processing

algorithms applied as show below.

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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.

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Histogram of Original Images

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

images as shown below.

Histogram Of Images After Equalization

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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 the message box displays “Fingerprint

don’t match” as shown below.

Fingerprint Matching Result

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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.

Password Matching Result

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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 security system. 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.