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FINGERPRINT RECOGNITION AND PASSWORD SECURITY SYSTEM
PROJECT SUPERVISOR
Dr. SYED WAQAR SHAH
PROJECT LEADER
AMMAD UDDIN
MEMBERS
AMEER ULLAH
RUMMAN KHAN
MUHAMMAD SHAKEEL
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INTRODUCTION
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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 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
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 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
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
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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
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.
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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
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
Gait
Handwriting
Speech
Signature
Now let discuss some the behavioral biometrics in a little detail.
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Gait
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
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
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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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Finger
Identifi
cation
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.
FINGERPRINT IENTIFICATION
SYSTEM
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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)
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.
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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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3
.1
Syst
em
Lev
el
Design
SYSTEM DESIGN
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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.
Preprocessing
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
removal
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
FINGERPRINT IMAGE
PROCESSINGS
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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
500
1000
1500
2000
2500
3000
0 50 100 150 200 250
0
200
400
600
800
1000
1200
1400
1600
1800
2000
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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5
.1
Min
utia
Ma
rkin
g
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
MINUTIAE MATCHING
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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
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.
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
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.
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PASSWORD SECURITY
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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.
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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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SYSTEM GUI AND ITS
USER MANUAL
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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
below.
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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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CONCLUSION
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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 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.
