FINGER PRINTINGS

FINGERPRINT BASED PERSON VERIFICATION IN SPATIAL DOMAIN USING MATLAB

CHAPTER 1

1 INTRODUCTION

As our everyday life is getting more and more computerized, automated security systems
are getting more and more important. Today, most of the banking transactions can be performed
over the Internet and soon they can also be performed on mobile devices such as cell phones and
PDAs. This rapid progress in wireless communication system, personal communication system
and smart card technology in our society makes information more susceptible to abuse. Due to
the growing importance of the information technology and the necessity of the protection and
access restriction, reliable personal identification is necessary.

The key task of an automated security system is to verify that the users are in fact who
they claim to be. There are three main methodologies when performing this verification. The
security system could ask the user to provide some information known only to the user, it could
ask the user to provide something only the user has access to or it could identify some sort of
trait that is unique for the user. Identifying some trait that is unique for the user is known as
biometric security.[6] A biometrics system is a pattern recognition system that establishes the
authenticity of a specific physiological or behavioral characteristic possessed by a user.
Fingerprint biometric is an automated digital version of the old ink-and-paper method
used for more than a century for identification, primarily by law enforcement agencies. The
biometric device requires each user to place a finger on a plate for the print to be read.
Fingerprint biometrics currently has three main application areas: large-scale Automated Finger
Imaging Systems (AFIS) generally used for law enforcement purposes; fraud prevention in
entitlement programs; and physical and computer access. A major advantage of finger imaging is
the longtime use of fingerprints and its wide acceptance by the public and law enforcement
communities as a reliable means of human recognition. Others include the need for physical
contact with the optical scanner, possibility of poor-quality images due to residue on the finger
such as dirt and body oils (which can build up on the glass plate), as well as eroded fingerprints
from scrapes, years of heavy labor or mutilation.[1]

Dept. 0f ECE, MCE, HASSAN Page 1


We are introducing two domains for the processing of an image. They are
1. Spatial domain
2. Frequency domain
1. Spatial domain refers to the image plane itself, and approaches in this category are basedon
direct manipulation of pixels in an image. The term spatial domain refers to the aggregate of
pixels composing an image. Spatial domain methods are procedures that operate directly on these
pixels. Spatial domain processes will be denoted by the expression[9]
g(x,y)=T[f(x,y)];
Where f(x,y) is the input image, g(x,y) is the processed image, and T is an operator on f, defined
over some neighborhood of (x,y);
2. Frequency domain processing techniques are based on modifying the Fourier transformof an
image.
Enhancement techniques based on various combinations of a method from these two categories
are not usual. There is no general theory of image enhancement. When animate is processed for
visual interpretation, the viewer is the ultimate judge of how well particular method works.
Visual evaluation of image quality is a highly subjective process, thus making the definition of a
“good image” an elusive standard by which to compare algorithm performance.

1.1 Biometric classification

Biometric characteristics can be divided in two main classes, as represented in the following
figure:[2]

Physiological are related to the shape of the body. Examples include, but are not limited
to fingerprint, face recognition, hand and palm geometry and iris recognition.

Behavioral are related to the behavior of a person. Characteristic implemented by using
biometrics are signature verification, keystroke dynamics, and voice



Fig1.1 Biometric classification

1.2 Characteristics of Biometrics:

To compare the relative merits of fingerprint as a biometric, we consider the following
characteristics of what constitute a good biometric:

1. Universality - each person has the characteristic
2. Uniqueness - the characteristic is unique per person

3. Permanence - characteristic remains the same over time

4. Collectability - how easy is it to measure the characteristic

5. Performance - accuracy, speed, and resource requirements

6. Acceptability - culturally accepted by the population

7. Circumvention - robust against fraudulent attacks



1.3 Applications of biometrics

1. Commercial applications such as computer network login, electronic data security, e-
commerce, Internet access, ATM or credit card use, physical access control, mobile phone, PDA,
medical records management, distance learning, etc.[3]

2. Government applications such as national ID card, managing inmates in a correctional facility,
driver’s license, social security, welfare-disbursement, border control, passport control, etc.

3. Forensic applications such as corpse identification, criminal investigation, parenthood
determination, etc

1.4 Biometric fingerprint

Fingerprint biometric is an automated digital version of the old ink-and-paper method used
for more than a century for identification, primarily by law enforcement agencies. The biometric
device requires each user to place a finger on a plate for the print to be read. Fingerprint
biometrics currently has three main application areas: large-scale Automated Finger Imaging
Systems (AFIS) generally used for law enforcement purposes; fraud prevention in entitlement
programs; and physical and computer access. A major advantage of finger imaging is the long-
time use of fingerprints and its wide acceptance by the public and law enforcement communities
as a reliable means of human recognition. Others include the need for physical contact with the
optical scanner, possibility of poor-quality images due to residue on the finger such as dirt and
body oils (which can build up on the glass plate), as well as eroded fingerprints from scrapes,
years of heavy labor or mutilation.[4]

Fingerprints are produced by sweat glands in the fingertip that coats the ridges of the
fingerprint. This solution leaves behind a facsimile of the fingertip ridges called a latent print,
when it comes in contact with a surface. In fingerprint literature, the terms ridges and valleys are
used to describe the higher and lower parts of the papillary lines that we can see on our fingertip.
The frictional ability of the skin is the reason we have ridges and valleys on our fingers. A fetus’



fingerprint ridges are fully developed at the age of seven month. After formation, an infant’s
growing fingerprint ridges are like drawing a face on a balloon with a ballpoint pen and then
inflating the balloon to see the same face expand uniformly in all direction. This means the
characteristic of the fingerprint does not change throughout the lifetime except for injury, disease
or decomposition after death. However after a small injury on the fingertip, the pattern will grow
back as the fingertip heals.[5]

Sir Francis Galton, a British anthropologist scientifically proved in the late 19th century that
no two fingerprints are exactly alike. According to his calculations, the odds of two individual
fingerprints being the same are 1 in 64 billion. No identical twins will have the same fingerprints.
A fingerprint can be looked at from different levels: the global level, the local level and the very
fine level. At the global level, you find the singularity points, called core and delta points. These
singularity points are very important for fingerprint classification, but they are not sufficient for
accurate matching. Figure 2.1 shows the core and delta points of two fingerprint’s pattern; loop
and whorl. Loops have one delta, whorl have two.

Fig 1.2: Core and delta points

2.2 show a fingerprint image with sweat pores and minutiae points visible. The black lines
correspond to the ridges in the fingerprint and the white line corresponds to the valley. The white
dots in the ridges correspond to the sweat pores and are marked with empty circles on a single
ridge line. Minutiae details are marked with black-filled circles. In order to establish comparison
between fingerprint images, AFIS’s often rely on procedures based on local features. Global
features are mainly employed to reduce the computational cost associated to fingerprint
comparison procedure.



Figure 1.3: Sweat pores

1.5 Fingerprint classification
There are three main structures that make up fingerprints. These are loops, whorls and arches.
[7]

Loops
Loops are comprised of one or more ridges entering from one side, curving, and then going
out the same side it entered. The ridges in loops double back on themselves. All loops have
elements called a delta and a core. The delta is a triangular area usually shaped like a T-junction,
while a core is the centre of the pattern. About 65% of fingerprints have loops.
Loops can be divided into two groups:
Radial loops – these flow downward and toward the radius (or the thumb side)

Fig.1.4 Radial loop
Ulnar loops – which flow toward the ulnar (or the little finger side). The ulnar loop is more
common.

Fig.1.5 Ulnar loop



Whorls
Whorls have a circular pattern and have at least two deltas and a core. Whorls look a little like
target shapes or whirlpools – circles within circles. Whorls make up 35% of patterns seen in
human fingerprints and can be sub-grouped into four categories:
Plain whorls – which are either concentric circles like a bull’s eye or spirals like a wound
spring.

Fig.1.6 Plain whorl
Central pocket loop whorls – these resemble a loop with a whorl at its end.

Fig.1.7 Central pocket loop
Double loop whorls – these occur when two loops collide to produce an “S” shaped pattern.

Fig.1.8 Double loop
Accidental loop whorls – these are slightly different from other whorls and are irregular.

Fig.1.9 Accidental loop whorl
Arches
Arches are the least common pattern making up only 5% of all pattern types. Arches are
ridgelines that rise in the centre and create a wave like pattern. The ridges enter from one side
and exit the other side with a rise in the middle. They do not have a delta or a core and can be
broken into two sub-groups:



Plain arch – which has a gentle rise.

Fig.1.10 Plain arch

Tented arches - have a steeper rise than plain arches.

Fig.1.11Tented arches

1.6 Objective of the project
From the discussion above, this project has set two objectives.
1. To design and develop a fingerprint biometric template system that can process every
fingerprint image inserted by the user.

2. To implement the fingerprint biometric template system in GUI of MATLAB software.

1.7 Methodology
1. Input fingerprint images are stored in an image repository on the host pc. In software
development, this project uses 256 gray-sales bitmap images with sizes of 400 pixels x 500 pixels
as a test vector. .
2. The Fingerprint Biometric Template will process and enhance the image at the image
processing stage.

3. A simple matching system using point matching is designed to validate the system.



1.8 Literature survey
There is archaeological evidence that fingerprints as a form of identification have been used at
least since 7000 to 6000 BC by the ancient Assyrians and Chinese. Clay pottery from these times
sometimes contain fingerprint impressions placed to mark the potter. Chinese documents bore a
clay seal marked by the thumbprint of the originator. Bricks used in houses in the ancient city of
Jericho were sometimes imprinted by pairs of thumbprints of the bricklayer. However, though
fingerprint individuality was recognized, there is no evidence this was used on a universal basis
in any of these societies. In the mid-1800’s scientific studies were begun that would established
two critical characteristics of fingerprints that are true still to this day: no two fingerprints from
different fingers have been found to have the same ridge pattern, and fingerprint ridge patterns
are unchanging throughout life. These studies led to the use of fingerprints for criminal
identification, first in Argentina in 1896, then at Scotland Yard in 1901, and to other countries in
the early 1900’s.

1.9 ORGANIZATION OF THE REPORT

This project gives information about the identification of person by using fingerprint in special
domain using matlab code.

chapter 1 covers the introduction of the project and classification of fingerprint and meaning of
the fingerprint

Chapter 2 covers the implementation of the finger print in special domain and description of the
block diagram

Chapter 3 covers the experimental results ,applications, advantages and challenges

Chapter 4 covers the conclusion of this project and references



CHAPTER 2

2 IMPLIMANTATION IN SPATIAL DOMAIN

Spatial domain refers to the image plane itself, and approaches in this category are based on
direct manipulation of pixels in an image. The term spatial domain refers to the aggregate of
pixels composing an image. Spatial domain methods are procedures that operate directly on these
pixels. Spatial domain processes will be denoted by the expression[2]
g(x,y)=T[f(x,y)];
Where f(x,y) is the input image, g(x,y) is the processed image, and T is an operator on f, defined
over some neighborhood of (x,y);

2.1 Block Diagram



Fig.1.12 Block Diagram for fingerprint verification in Spatial Domain

2.2 Image Acquisition

The task of acquisition is to enroll persons and their fingerprints into the system. When the
fingerprint images and the user name of a person to be enrolled are fed to the processing, the
images will be enhanced and thinned at the image processing stage. The following code is used
for image registration which is present in the address ’c:Documents and
settingsAdminDesktopfolder nameImage name’.[6]
a=imread ('C:Documents and SettingsAdminDesktopab.bmp');
figure (1), imshow (a);

*NOTE: The finger print images must be present in the above mentioned address. In the above
code ‘a indicates the folder name and ‘b.bmp’ indicates the image name which is in .bmp format.

2.3 Low pass filtering



We are convolving the output of the previous process with a properly defined masking matrix
is:[4]

h= (1/25)*ones (5, 5);

A low pass filter is the basis for most smoothing methods. An image is smoothed by
decreasing the disparity between pixel values by averaging nearby pixels (see Smoothing an
Image for more information).

Using a low pass filter tends to retain the low frequency information within an image while
reducing the high frequency information. An example is an array of ones divided by the
number of elements within the kernel, such as the following 3 by 3 kernel:

(The above array is an example of one possible kernel for a low pass filter. Other filters may include
more weighting for the center point, or have different smoothing in each dimension)

The acquired image is passed through low pass filter to reduce the noise for further processing.
We are convolving the input image with a properly defined masking matrix is
h= (1/25)*ones (5, 5);

Image filtering is useful for many applications, including smoothing, sharpening,
removing noise, and edge detection. A filter is defined by a kernel, which is a small array applied
to each pixel and its neighbors within an image. In most applications, the center of the kernel is
aligned with the current pixel, and is a square with an odd number (3, 5, 7, etc.) of elements in
each dimension. The process used to apply filters to an image is known as convolution, and may
be applied in either the spatial or frequency domain.

Noise elimination is the process that removes all the undesired pixels in the image (black
pixel that occur as noise in the image). These undesired images can destroy the quality of the


image and thus will reduce the ability and accuracy of the feature extraction process. These
undesired pixels are meaningless and can create false minutiae in minutiae extraction process.

The sizes of the structuring elements that have been used are 5 x 5 structuring element
and 3 x 3 structuring elements. Each structuring elements has its own condition for identifying
the noise.

LOW PASS
FILTERING
MODULE

Fig 1.12 low pass filtering process

2.4 Binarization

Binarization is the process that converts a gray scale image, which has 256 of gray-level (0 to
255) to a black and white (0 and 1) binary images. This is important because binary images are
very simple to store and manipulate, as each pixel is represent by a single bit. The binary images
are also very easy to generate compared to a gray scale image.[7]
To convert a gray scale image to a binary image is not an easy task. A reasonable threshold
to separate the black pixel from the white pixel is very difficult to find. This is because for a
given gray value, it can represent ridges in some area but it may represent valley in the other
area.
The average gray value can be used as a threshold value for the Binarization process. If the
gray value of a particular pixel is lower than the threshold value, then the new value assigned for
that pixel is ‘0’(representing the ridges), else the new value is set to ‘0’(representing the valleys).

Where Vmean is the average gray value, V(x,y) is the gray value of the particular pixel and r.c
is the total pixels in the image.



BINARIZATION

1.13 Binarization process

2.5 Thinning
Thinning process is used to skeletonized the binary image by reducing all lines to a single
pixel thickness. There are two main approaches to find the skeleton of a binary region. The first
approach basically calculates the distance to the region edge for all pixels belonging to the
region. Then those pixels that have the largest distance to the region edge are selected to belong
to the region skeleton. Although the approach is simple and yields an intuitively pleasing
skeleton the method is seldom implemented in its pure form since it is very computationally
expensive.
The second approach instead iteratively deletes edge point pixels from the region until just the
skeleton remains. For a pixel to be deleted, the following conditions must hold
• The pixel is not an endpoint.
• The removal does not break the connectedness of the skeleton.
• The removal does not cause excessive erosion of the region.
The method used in this system takes on the second approach. It uses a modified version
of the thinning algorithm first suggested by Zhang and Suen(Zhang and Suen, 1984). The
method consists of removing all pixel of the image except those pixels that belong to the
skeleton. In order to preserve the connectivity of the skeleton, all iteration is divided into two
subiterations. A pixel p0 is defined to have at least one pixel in its eight-connectivity
neighborhood that belongs to the background. A pixel is marked for deletion in the first
subiteration if all of the following conditions hold for its eight-connectivity neighborhood:
• The number of neighbors that belong to the region must be between two and six. This
ensures that the endpoint pixels of a skeleton line are preserved.
• All neighbors that belong to the background must be connected. This prevents the
deletion of those pixels that lie betweens the end point pixels of a skeleton line.


• p1, p3 or p5 must belong to the background.
• p3, p5 or p7 must belong to the background.
If all conditions are met, the point is marked for deletion. However, the point is not deleted
until all region points are processed to ensure that the data structure is not changed during the
execution of the step.For a point to be marked for deletion in the second subiteration of the
algorithm, the first two conditions still must hold, but third and fourth conditions are changed as
follows
As mentioned above, the algorithm had to be modified to apply to fingerprint ridge thinning.
The problem lies in what is defined to be a one-pixel width skeleton.
In the case of fingerprint ridges a ridge point that is not a minutia is only allowed to have two
neighbors that belong to the ridge. This fact conflicts with the second condition in the original
thinning algorithm. The problem arises in 16 special cases where not all neighbor pixels, which
belong to the background, are connected but
where the pixel still should be deleted.

Image thinning plays an important role in image processing as it simplifies object
representation and pattern analysis. The skeleton is defined via the medial axis transformation
(MAT) proposed by Blum. In this definition, the skeleton of an image is defined as the set of the
centers of all maximal inscribed discs. An essential property of skeletons in digital space is that
they include all or part of these centers, defined with respect to Euclidean or other distances. The
MAT definition is equivalent to the intuitive definition in terms of “prairie fire simulation”.
However, direct implementation of MAT is expensive computationally. So there has been
considerable interest in finding new methods to rapidly thin images.



THINNING

Fig 1.14 Thinning process

2.6 High pass filtering
We are convolving the output of thinning process with a properly defined masking matrix is
j=[-1 -1 -1;0 0 0;1 1 1]; [6]

A high pass filter is the basis for most sharpening methods. An image is sharpened when
contrast is enhanced between adjoining areas with little variation in brightness or darkness (see
Sharpening an Image for more detailed information).

A high pass filter tends to retain the high frequency information within an image while
reducing the low frequency information. The kernel of the high pass filter is designed to increase
the brightness of the center pixel relative to neighboring pixels. The kernel array usually contains
a single positive value at its center, which is completely surrounded by negative values.

HIGH PASS
FILTERING

Fig1.15 High pass filtering process

2.7 Distance measurement
Distance is measured between the adjacent pixels of the fingerprints . These distances are used
for further matching purposes.



2.8 Matching

The method used for fingerprint matching is

• Correlation-based matching: two fingerprint images are superimposed and the correlation (at
the intensity level) between corresponding pixels is computed for different alignments (e.g.,
various displacements and rotations).

If A is a template stored in database and B is a template from test fingerprint,and

• A TYPE = B TYPE

• Euclidean Distance (A,B) _ Df

• Angle (A,B) _ Af

then (A,B) is a pair of matched templates. Df and Af are maximum tolerance for translation and
rotation respectively.

Each template should not be matched more than once. If Sm is a set of matched template
pairs, each elements in Sm has the form of (Ai,Bi) where Ai are templates stored in database and
Bi are templates from test fingerprint. All Ai in Sm should be different and all Bi should be
different too.
In order to match two set of templates, a maximum number of paired templates, Smax is to be
found. The procedure to do this is
• Let Sm be empty

• Select template A from database and template B from test fingerprint.
If template A and template B can be matched and pairing of A and B can be added to Sm, add it.

• Repeat second step until no any pair could be added to Sm.



• If current number of paired template in Sm are maximum, save the current Sm as Smax.

• Backtrack to search other combination.

The similarity measure, M between two fingerprint images is defined as

where N1 and N2 are the number of templates from database and test fingerprint respectively,
Nm are the number of paired templates in Smax.

The similarity measure M for two images from the same fingerprint is close to 1. In practice,
if the calculated M is bigger than a predefined reasonable threshold, then it can be said that the
two images originated from the same fingerprint.

We are accurately specified the distances of same user of different fingerprints and these
values are used for comparison purposes, if above mentioned distances are obeyed then matching
occurs, Otherwise not matching.

2.9 Steps to be followed to execute the M-file:
1. Load the program to MATLAB workspace.
2. Specify the correct path for loading the image form our mentioned database.
3. Save the program and then press the RUN button on the M-file to run the program.
4. Processing is done automatically, and then a Euclidian distance is obtained for mentioned
fingerprints and check for 1 to 1 matching.
5. For displaying the result message window appears.



2.10 MATLAB CODE

clc; %Clears the command window screen.
clear all; %Removes all the variables in the workspace.
close all; %Closes all the previously opened windows.
a=imread('C:Documents and
SettingsLenovoDesktopFingerprintA1.bmp');%Reads the first
fingerprint image from the mentioned address.
figure,imshow(a); %Shows the figure of the input
fingerprint image.
h=(1/25)*ones(5,5); %Defining a mask matrix for LPF
z=imfilter(a,h,'same'); %Performs two-dimensional
fingerprint low pass image filtering.
figure,imshow(z);
bw=im2bw(z,0.4); %Converts the gray scale
fingerprint image to a binary fingerprint image.
figure,imshow(bw);
bw1=bwmorph(~bw,'thin','info');%Performs thinning on the
binarized fingerprint image.
figure,imshow(~bw1);
j=[-1 -1 -1;0 0 0;1 1 1]; %Defining a PREWITT masking
matrix.
m=convn(bw1,j,'same'); %Applying the PREWITT mask on the
obtained thinned fingerprint image. it performs the high pass
filtering .
figure, imshow(m);

a1=imread('C:Documents and
SettingsLenovoDesktopFingerprintB1.bmp');%Reads the second
fingerprint image from the mentioned address.
figure,imshow(a1); %Shows the figure of the input
fingerprint image.



h1=(1/25)*ones(5,5); %Defining a mask matrix for LPF
z1=imfilter(a1,h1,'same'); %Performs two dimensional
fingerprint low pass image filtering.
figure, imshow(z1);
bw2=im2bw(z1,0.4); %Converts the gray scale
fingerprint image to a binary fingerprint image.
figure,imshow(bw2);
bw3=bwmorph(~bw2,'thin','info');%Performs thinning on the
binarized fingerprint image.
figure,imshow(~bw3);
j1=[-1 -1 -1;0 0 0;1 1 1]; %Defining a PREWITT masking
matrix.
m1=convn(bw3,j1,'same'); %Applying the PREWITT mask on
the obtained thinned fingerprint image.it performs the high pass
filtering.
figure,imshow(m1);

v=reshape(m,[],1); %Reshapes the output of masking
of the first fingerprint image.
w=reshape(m1,[],1); %Reshapes the output of masking
of the second fingerprint image.
y=[v w]; %Combining of the taken two
fingerprint images.
f=0; %Initialising a variable.
for i=1:200000
f=f+((y(i,1)-y(i,2))^2); %Determines the Euclidean
distance.
end
k=double(f); %Finds the double value.

s=sqrt(k) %Finds the square root value of
the obtained double value.



if (s>305.61 && s<305.63)
msgbox('MATCHED');
elseif (s>319.17 && s<319.19)
msgbox('MATCHED');
elseif (s>375.21 && s<375.23)
msgbox('MATCHED');
elseif (s>370.55 && s<370.57)
msgbox('MATCHED');
elseif (s>407.59 && s<407.61)
msgbox('MATCHED');
elseif (s>341.30 && s<341.32)
msgbox('MATCHED');
elseif (s>421.28 && s<421.30)
msgbox('MATCHED');
elseif (s>407.94 && s<407.96)
msgbox('MATCHED');
elseif (s>378.18 && s<378.20)
msgbox('MATCHED');
elseif (s>362.41 && s<362.43)
msgbox('MATCHED');
elseif (s>372.46 && s<372.48)
msgbox('MATCHED');
elseif (s>348.60 && s<348.62)
msgbox('MATCHED');
elseif (s>384.86 && s<384.88)
msgbox('MATCHED');
elseif (s>416.04 && s<416.06)
msgbox('MATCHED');
elseif (s>375.97 && s<375.99)
msgbox('MATCHED');
elseif (s>381.82 && s<381.84)



msgbox('MATCHED');
elseif (s>404.55 && s<404.57)
msgbox('MATCHED');
elseif (s>366.50 && s<366.52)
msgbox('MATCHED');
elseif (s>375.57 && s<375.59)
msgbox('MATCHED');
elseif (s>345.38 && s<345.40)
msgbox('MATCHED');
elseif (s>372.75 && s<372.77)
msgbox('MATCHED');
elseif (s>374.33 && s<374.35)
msgbox('MATCHED');
elseif (s>390.06 && s<390.08)
msgbox('MATCHED');
elseif (s>408.37 && s<408.39)
msgbox('MATCHED');
else
errordlg('NOT MATCHED','Error');
end

CHAPTER 3

3.1 EXPERIMENTAL RESULTS

Here by comparing the threshold value we can identify the person the below
table shows the threshold values of ten people after the high pass filtering process
we obtain this results



THRESHOLD VALUES FOR 1 TO 1 MATCHING

Person number Threshold values
1 305.6141
2 319.1708
3 375.2173
4 370.5644
5 407.5954
6 341.3034
7 421.2861
8 407.9485
9 378.1878
10 362.4141

3.2 Advantages

1. Increases the accuracy of verification

2. Is the most economical biometric PC user authentication technique
3. This allows one to use the same sensor for taking fingerprint of different fingers

4. Appears to be the best solution for lower cost, reduced complexity and improved
performance

3.3 Applications



1. Computer network login

2. Electronic data security

3. Govt. application such as national ID

4. Criminal investigation

5. Passport control

3.4 Challenges

1. If the given Fingerprint present in database is destroyed then we can’t identify that
person

2. It can make mistakes with the dryness or dirty of the finger’s skin, as well as with the
age(children)

CHAPTER 4

4.1CONCLUSION

The proposed Fingerprint Biometric Template system is an DSP system that is part of a
fingerprint recognition system based on spatial domain. The system extracts the local
characteristic of a fingerprint which is in template based. The proposed DSP system consists of
component; the MATLAB software. The software contains two stages; image processing and



matching stage. This appears the best solution for lower cost, reduced complexity and improved
performance.

4.2 Recommendation of Future work
The work in this project suggests that future enhancement can be carried out to further
improve the design to achieve better performance or a more complete operation. Below are some
of the proposed future works:

The first recommendation is the improvement of the image processing stage. The image
processing stage result can be improved by using adaptive threshold values for image
segmentation and binarization process. Image segmentation process can be done by applying
histogram-based image segmentation for every image. Therefore an image will have different
threshold value than other image. For binarization process, an adaptive average thresholding can
be used. For every pixel in the image, the average gray value of the pixels in the 5 x 5
neighborhood of the pixel is calculated and used as the threshold value.
The first method suggests that the template minutiae should be used as reference point. They
should be tried as reference point one by one starting by the one closest to the center of the
image. The center image can be set up by finding the global representation of fingerprint such as
delta and core first. After the template minutiae have been used as a reference point, the position
and angle of the reference point should be used to align the second set of minutiae. The second
method is suggested by Jia .

BIBLIOGRAPHY

[1] Gonzalez R. C. and Woods R. E. (1993). Digital Image Processing.
[2]Eriksson, M. Biometrics: Fingerprint Based Identity Verification. Umea University:
Master Thesis.



[3]. Miller, Christiansen 1995; commercialization of fingerprint technologies.
[4]Mehtre, B. M. Fingerprint Image Analysis for Automatic Identification.
[5]L. O'Gorman, Fingerprint veri_cation," in Biometrics: Personal Identification in
a Networked Society (A. K. Jain, R. Bolle, and S. Pankanti, eds.), Kluwer
Academic Publishers.
[6]Maltoni, D. Maio, A. K. Jain, and S. Prabhakar, Handbook of Fingerprint Recognition,
Springer-Verlag.
[7] Prabhakar and Jain; Introduction to Biometric Recognition Technologies and
Applications.
[8]Miller, Christiansen 1995; commercialization of fingerprint technologies.
[9]Hong L., Wan Y., and Jain A., “Fingerprint Image Enhancement: Algorithm and
Performance Evaluation," IEEE Trans. Pattern Analysis and Machine Intelligence, vol.
20, no.8, pp. 777-789,1998.
[10]Jain Anil, Prabhakar Salil, and Hong Lin, “A Multichannel Approach to Fingerprint
Classification,' IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 21,
pp. 348-359, 1999.
[11] Younhee Gil, Access Control System with high level security using
fingerprints,IEEE the 32nd Applied Imagery Pattern Recognition Workshop (AIPR ’03)
[12] Jain, A.K., Hong, L., and Bolle, R.(1997), “On-Line Fingerprint Verification,” IEEE
Trans. On Pattern Anal and Machine Intell, 19(4), pp. 302-314


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