Fingerprint Recognition Technique(PDF)

FINGERPRINT RECOGNITION
Project ID: 1044
A Final Project Report Submitted to
Biju Patnaik University of Technology, Rourkela
In partial fulfilment of the requirement for the B.Tech Degree
Submitted By
SAILENDRA SAGAR PATRA
SANDEEP KUMAR PANDA
May - 2013
Under the guidance of
Mrs. T. Mita Kumari
APEX INSTITUTE OF TECHNOLOGY & MANAGEMENT
Pahala, Bhubaneswar, Odisha – 752101, India

A Final Project Report Submitted to
Biju Patnaik University of Technology, Rourkela
In partial fulfilment of the requirement for the B.Tech Degree
Submitted By
SAILENDRA SAGAR PATRA Regn. No.-0901314147
SANDEEP KUMAR PANDA Regn. No.-0901314126
May - 2013
Under the guidance of
Mrs. T. Mita Kumari

CERTIFICATE
This is to certify that the project work entitled ‘Fingerprint Recognition’
is a bonafide work done by Sailendra Sagar Patra bearing Registration No.
0901314147 of ECE branch and Sandeep Kumar Panda bearing Registration
No. 0901314126 of ECE branch.
This report is submitted in partial fulfilment of the requirements for the
award of the B.Tech degree under Biju Patnaik University of Technology,
Rourkela, during the year 2012-13.
(Mrs T.Mita Kumari) (Dr. Satya Ranjan Pattanaik)
Project Guide B.Tech Project Coordinator
(Prof. R.C. Das)
PRINCIPAL
Institute Seal

ii
ABSTRACT
The study and implementation of a fingerprint recognition system based on Minutiae based
matching quite frequently used in various fingerprint algorithms and techniques. Human
fingerprints are rich in details called minutiae, which can be used as identification marks for
fingerprint verification. The goal of this project is to develop a complete system for
fingerprint verification through extracting and matching minutiae. To achieve good minutiae
extraction in fingerprints with varying quality, pre-processing in form of image enhancement,
image Binarization and image segmentation is first applied on fingerprints before they are
evaluated. Histogram equalization and Fourier Transform have been used for image
enhancement. Then the fingerprint image is binarized using the local adaptive threshold
method .Minutia Extraction is done by thinning and minutia marking technique. A simple
algorithm technique is used for minutia matching. By using match score method we
differentiate the two fingerprints are same or not.

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ACKNOWLEDGEMENT
It is our proud privilege to epitomize our deepest sense of gratitude and indebtedness
to our guide, Mrs. T.Mita Kumari, for her valuable instructions, guidance and support
throughout our project work. Her inspiring assistance and affectionate care enabled us to
complete our work smoothly and successfully.
We again owe our special thanks to Dr. Satya Ranjan Pattanaik, B.Tech Project
Coordinator for giving us an opportunity to do this project.
We would also like to thank Prof. R.C. Das, Principal, AITM, and Bhubaneswar for
his persistent drive for better quality in everything that happens at AITM. This report is a
dedicated contribution towards that greater goal.
Sailendra Sagar Patra
(Regn. No.-0901314147)
Sandeep Kumar Panda
(Regn. No.-0901314126)

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TABLE OF CONTENTS
ABSTRACT.........................................................................................................................ii
ACKNOWLEDGEMENT .................................................................................................iii
TABLE OF CONTENTS................................................................................................... iv
LIST OF FIGURES ........................................................................................................... vi
LIST OF ABBREVIATIONS ........................................................................................... vii
1. Introduction..................................................................................................................... 1
1.1 Introduction................................................................................................................. 1
1.2 What is a fingerprint? .................................................................................................. 1
1.3 What is Fingerprint Recognition? ................................................................................ 2
2. Algorithm Level Design and Enhancement Technique ................................................. 4
2.1 Algorithm Level Design .............................................................................................. 4
FLOW CHART................................................................................................................. 6
2.2 Fingerprint Image Enhancement Technique................................................................. 7
2.2.1 Histogram Equalization ........................................................................................ 7
2.2.2 Fingerprint Enhancement by Fourier transform..................................................... 8
2.2.3 Fingerprint Image Binarization ............................................................................. 9
2.3 Fingerprint Image Segmentation................................................................................ 10
2.3.1 Block direction estimation .................................................................................. 10
2.3.2 ROI extraction by Morphological operations....................................................... 11
2.4.1 Minutia Extraction.............................................................................................. 12
2.4.2 Minutia Marking................................................................................................. 13
2.4.3 False Minutiae Removal ..................................................................................... 14
2.4.4 Unify Terminations and Bifurcation.................................................................... 16
2.5 Minutia Matching...................................................................................................... 17
2.5.1 Alignment Stage ................................................................................................. 17
2.5.2 Match Stage........................................................................................................ 18
3. Results and Discussions................................................................................................. 20
3.1 Results for Minutiae Extraction algorithm ................................................................. 20
3.2 Comparison Results For Minutiae matching .............................................................. 25
3.2.1 Two Different Fingerprints ................................................................................. 25
3.2.2 Two Fingerprint of a same person with a Little Difference................................. 26

v
4. Conclusion ..................................................................................................................... 27
References.......................................................................................................................... 28

vi
LIST OF FIGURES
Figure 1.1: Fingerprint Image................................................................................................ 1
Figure 1.2: Two Minutia Features ......................................................................................... 2
Figure 1.3: Verification vs. Identification.............................................................................. 3
Figure 2.1: Simplified Fingerprint Recognition ..................................................................... 4
Figure 2.2: Minutia Extractor ................................................................................................ 4
Figure 2.3: Minutia Matcher ................................................................................................. 5
Figure 2.4: Steps Involved In Fingerprint Recognition .......................................................... 6
Figure 2.5: (a) Histogram of an image, (b) Histogram equalization of an image..................... 7
Figure 2.6: (a) Original image, (b) Enhanced Image after Equalization.................................. 8
Figure 2.7: (a) Original Image, (b) Image Enhancement by FFT............................................ 9
Figure 2.8: (a) Enhanced image, (b) Image after Binarization.............................................. 10
Figure 2.9: (a) Binarization image, (b) Direction Map ......................................................... 11
Figure 2.10: (a) Original Image, (b) Close Operation, (c) Open Operation, (d) ROI+Bound 12
Figure 2.11: (a) Bifurcation, (b) Termination, (c) Triple Counting Branch........................... 13
Figure 2.12: (a) Thinned Image, (b) Figure after Minutiae Extraction.................................. 14
Figure 2.13: False Minutia Points........................................................................................ 15
Figure 2.14: A Bifurcation to Three Terminations............................................................... 16
Figure 3.1: (a) Original Image, (b) Image after Histogram Equalization .............................. 20
Figure 3.2: (a) Histogram image, (b) Image Enhancement using FFT.................................. 21
Figure 3.3: (a) FFT Image, (b) Image after Binarization ...................................................... 21
Figure 3.4: (a) Binarization image, (b) Direction map of Binarization Image...................... 22
Figure 3.5: (a) Binarization Image, (b) ROI Image .............................................................. 22
Figure 3.6: (a) Open Operation, (b) Close Operation ........................................................... 23
Figure 3.7: (a) Adaptive Binarization, (b) ROI+BOUND image .......................................... 23
Figure 3.8: (a) ROI image, (b) Thinned Image.................................................................... 24
Figure 3.9: (a) Thinned Image, (b) Minutiae Marking after Thinning .................................. 24
Figure 3.10: (a) First Fingerprint, (b) Second Fingerprint, (c) Minutiae extraction of First
Fingerprint, (d) Minutiae Extraction of Second Fingerprint ................................................. 25
Fingerprint, (d) Minutiae extraction of Second Fingerprint.................................................. 26

vii
LIST OF ABBREVIATIONS
1.AFRS (Automatic Fingerprint Recognition System)………………………………………2
2.FFT (First Fourier Transform)……………………………………………………………...8
3.ROI (Region Of Interest)…………………………………………………………………..10
4.CN (Crossing Number)…………………………………………………………………….13
5. FVC2000 (Fingerprint Verification competition 2000)…………………………………..20
6.IEEE(Institute Of Electrical Electronics Engineering)…………………………………….28

1
1. Introduction
1.1 Introduction
Fingerprint recognition or fingerprint authentication refers to the automated method of
verifying a match between two human fingerprints [1]. Fingerprints are one of many forms
of biometrics used to identify an individual and verify their identity. Because of their
uniqueness and consistency over time, fingerprints have been used for over a century, more
recently becoming automated (i.e. a biometric) due to advancement in computing
capabilities.
1.2 What is a fingerprint?
Skin on human fingertips contains ridges and valleys which together forms distinctive
patterns. These patterns are fully developed under pregnancy and are permanent
throughout whole lifetime. Prints of those patterns are called fingerprints. Injuries like
cuts, burns and bruises can temporarily damage quality of fingerprints but when fully
healed, patterns will be restored. Through various studies it has been observed that no two
persons have the same fingerprints, hence they are unique for every individual.
Figure 1.1: Fingerprint Image
Chapter-1

2
However, shown by intensive research on fingerprint recognition, fingerprints are not
distinguished by their ridges and furrows, but by features called Minutia, which are some
abnormal points on the ridges (Figure 1.2)
Among the variety of minutia types reported in literatures, two are mostly significant and
in heavy usage:
 Ridge ending- the abrupt end of a ridge
 Ridge bifurcation- a single ridge that divides into two ridges
Figure 1.2: Two Minutia Features
1.3 What is Fingerprint Recognition?
Fingerprint recognition (sometimes referred to as dactyloscopy) is the process of
comparing questioned and known fingerprint against another fingerprint to determine if the
impressions are from the same finger or palm. It includes two sub-domains: one is fingerprint
verification and the other is fingerprint identification (Figure 1.3). In addition, different from
the manual approach for fingerprint recognition by experts, the fingerprint recognition here is
referred as AFRS (Automatic Fingerprint Recognition System) [2,3], which is program-
based.

3
However, in all fingerprint recognition problems, either verification(one to one
matching) or identification(one to many matching), the underlining principles of well defined
representation of a fingerprint and matching remains the same.
Figure 1.3: Verification vs. Identification

4
2. Algorithm Level Design and Enhancement
Technique
2.1 Algorithm Level Design
A fingerprint recognition system constitutes of fingerprint acquiring device, minutia
extractor and minutia matcher
Figure 2.1: Simplified Fingerprint Recognition
For fingerprint acquisition, optical or semi-conduct sensors are widely used [3]. They
have high efficiency and acceptable accuracy except for some cases that the user’s finger is
too dirty or dry.
The minutia extractor and matcher modules have been explained in detail in the next
part for algorithm design and other subsequent sections. To implement a minutia extractor, a
three-stage approach is widely used by Researchers. They are pre processing, minutia
extraction and post processing stage.
Figure 2.2: Minutia Extractor
Chapter- 2

5
For the fingerprint image pre processing stage, Histogram Equalization and Fourier
Transform have been used to do image enhancement. And then the fingerprint image is
binarized using the locally adaptive threshold method. The image segmentation task is
fulfilled by a three-step approach: block direction estimation, segmentation by direction
intensity and Region of Interest extraction by Morphological operations. For minutia
extraction stage, iterative parallel thinning algorithm is used. The minutia marking is a
relatively simple task. For the post processing stage, a more rigorous algorithm is developed
to remove false minutia. Also a novel representation for bifurcations is proposed to unify
terminations and bifurcations.
Figure 2.3: Minutia Matcher
The minutia matcher chooses any two minutiae as a reference minutia pair and then
matches their associated ridges first. If the ridges match well, the two fingerprint images are
aligned and matching is conducted for all remaining minutia.

6
FLOW CHART
Figure 2.4: Steps Involved In Fingerprint Recognition
LOAD IMAGE
HISTOGRAM EQUALIZATION
ENHANCEMENT USING FFT
BINARIZATION
RIDGE DIRECTION
THINING
MINUTIA MARKING
TEMPLATE
ALLIGN AND MATCH TEMPLATE
ROI
IMAGE
AQUAISATION
PRE PROCESSING
STAGE
MINUTIAE
EXTRACTION
MINUTIAE MATCH

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2.2 Fingerprint Image Enhancement Technique
The first step in the minutiae extraction stage is Fingerprint Image enhancement. This is
mainly done to improve the image quality and to make it clearer for further operations. Often
fingerprint images from various sources lack sufficient contrast and clarity. Hence image
enhancement is necessary and a major challenge in all fingerprint techniques to improve the
accuracy of matching. It increases the contrast between ridges and furrows and connects the
some of the false broken points of ridges due to insufficient amount of ink or poor quality of
sensor input.
In our project we have implemented three techniques: Histogram Equalization, Fast
Fourier Transformation and Image Binarization.
2.2.1 Histogram Equalization
Histogram equalization is a technique of improving the global contrast of an image by
adjusting the intensity distribution on a histogram [4]. This allows areas of lower local
contrast to gain a higher contrast without affecting the global contrast. Histogram
equalization accomplishes this by effectively spreading out the most frequent intensity
values.
The original histogram of a fingerprint image has the bimodal type(Figure 2.5(a),
the histogram after the histogram equalization occupies all the range from 0 to 255 and the
visualization effect is enhanced(Figure 2.5(b)).
(a) (b)
Figure 2.5: (a) Histogram of an image, (b) Histogram equalization of an image

8
(a) (b)
Figure 2.6: (a) Original image, (b) Enhanced Image after Equalization
2.2.2 Fingerprint Enhancement by Fourier transform
The image was divided into small processing blocks (32 by 32 pixels) and the Fourier
transform was performed according to:














N
vy
M
ux
jyxfvuF 2exp),(),( (2.1)
for u = 0, 1, 2, ..., 31 and v = 0, 1, 2, ..., 31.
In order to enhance a specific block by its dominant frequencies, the FFT of the block
was multiplied by its magnitude a set of times [4] .Where the magnitude of the original FFT
= abs (F(u ,v)) = |F(u ,v)|.
The enhanced block is obtained according to:
  k
vuFvuFFyxg ),(,),( 1
 
(2.2)
Where F-1
(F(u,v)) is done by:
 












 



 N
vy
M
ux
jvuF
MN
yxf
M
x
N
y
2exp,
1
),(
1
0
1
0
(2.3)
for x = 0, 1, 2, ..., 31 and y = 0, 1, 2, ..., 31.

9
The k in formula (2) is an experimentally determined constant, which was k=0.45 to
calculate.
While having a higher "k" improves the appearance of the ridges, filling up small
holes in ridges, having too high a "k" can result in false joining of ridges. Thus a termination
might become a bifurcation.
(a) (b)
Figure 2.7: (a) Original Image, (b) Image Enhancement by FFT
The enhanced image after FFT has the improvements to connect some falsely broken
points on ridges and to remove some spurious connections between ridges. The shown image
at the left side of Figure2.7(b) is also processed with histogram equalization after the FFT
transform.
2.2.3 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 colour while furrows are white. A locally adaptive
Binarization method is performed to binarized the fingerprint image. Such a named method
comes from the mechanism of transforming a pixel value to 1 if the value is larger than the
mean intensity value of the current block (16x16) to which the pixel belongs [Figure 2.8(a)
and Figure 2.8(b)].

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(a) (b)
Figure 2.8: (a) Enhanced image, (b) Image after Binarization
2.3 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 those spurious minutia
that are generated when the ridges are out of the sensor [5]. To extract the ROI, a two-step
method is used. The first step is block direction estimation and direction variety check, while
the second is intrigued from some Morphological methods.
2.3.1 Block direction estimation
The direction for each block of the fingerprint image with WxW in size(W is 16
pixels by default)is estimated. The algorithm is:
I. The gradient values along x-direction (gx) and y-direction (gy) for each pixel of
the block is calculated. Two Sobel filters are used to fulfill the task.
II. For each block, following formula is used to get the Least Square approximation
of the block direction.
 
  
 


 22
2
tan
yx
yx
gg
gg
 (2.4)
for all the pixels in each block.

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The formula is easy to understand by regarding gradient values along x-direction and
y direction as cosine value and sine value. So the tangent value of the block direction is
estimated nearly the same as the way illustrated by the following formula.
 

 22
sincos
cossin2
2tan


 (2.5)
After the estimation of each block direction, those blocks without significant
information on ridges and furrows are discarded based on the following formulas:
        2222
/2 yxyxyx ggWWggggE (2.6)
For each block, if its certainty level E is below a threshold, then the block is regarded
as a background block. The direction map is shown in the following diagram (assuming there
is only one fingerprint in For each block, if its certainty level E is below a threshold, then the
block is regarded each image.)
(a) (b)
Figure 2.9: (a) Binarization image, (b) Direction Map
2.3.2 ROI extraction by Morphological operations
Two Morphological operations called ‘OPEN’ and ‘CLOSE’ are adopted[5,6].
The ‘OPEN’ operation can expand images and remove peaks introduced by background

12
noise [Figure 2.10(c)]. The ‘CLOSE’ operation can shrink images and eliminate small
cavities [Figure 2.10(b)].
(a) (b)
Fig 2.10(d) show the interest fingerprint image area and its bound. The bound is the
subtraction of the closed area from the opened area. Then the algorithm throws away those
leftmost, rightmost, uppermost and bottommost blocks out of the bound so as to get the
tightly bounded region just containing the bound and inner area.
(c) (d)
Figure 2.10: (a) Original Image, (b) Close Operation, (c) Open Operation, (d) ROI+Bound
2.4 POST PROCESSING STAGE
2.4.1 Minutia Extraction
Ridge Thinning is to eliminate the redundant pixels of ridges till the ridges are just
one pixel wide. An iterative, parallel thinning algorithm is used. In each scan of the full

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fingerprint image, the algorithm marks down redundant pixels in each small image
window(3x3). And finally removes all those marked pixels after several scans. The thinned
ridge map is then filtered by other three Morphological operations to remove some H breaks,
isolated points and spikes.
2.4.2 Minutia Marking
After the fingerprint ridge thinning, marking minutia points is relatively easy. The
concept of Crossing Number (CN) is widely used for extracting the minutiae [6].
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 [Fig 2.11(a)]. If the central pixel is 1 and
has only 1 one-value neighbour, then the central pixel is a ridge ending [Fig 2.11(b)] ,i.e., if
Cn(P) = =1 it’s a ridge end and if Cn(P) = = 3 it’s a ridge bifurcation point, for a pixel P.
(a) (b)
(c)
Figure 2.11: (a) Bifurcation, (b) Termination, (c) Triple Counting Branch
0 1 0
0 1 0
1 0 1
0 0 0
0 1 0
0 0 1
0 1 0
0 1 1
1 0 0

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Fig 2.11(c) illustrates a special case that a genuine branch is triple counted. Suppose
both the uppermost pixel with value 1 and the rightmost pixel with value 1 have another
neighbour outside the 3x3 window, so the two pixels will be marked as branches too. But
actually only one branch is located in the small region. So a check routine requiring that none
of the neighbors of a branch are branches is added.
Also the average inter-ridge width D is estimated at this stage. The average inter ridge
width refers to the average distance between two neighbouring ridges. The way to
approximate the D value is to 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.
Together with the minutia marking, all thinned ridges in the fingerprint image are
labelled with a unique ID for further operation. The labelling operation is realized by using
the Morphological operation. Minutiae extraction from thinned image in [Figure 2.12(b) and
Figure 2.12(a).]
(a) (b)
Figure 2.12: (a) Thinned Image, (b) Figure after Minutiae Extraction
2.4.3 False Minutiae Removal
At this stage false ridge breaks due to insufficient amount of ink & ridge cross
connections due to over inking are not totally eliminated. Also some of the earlier methods

15
introduce some spurious minutia points in the image. So to keep the recognition system
consistent these false minutiae need to be removed.
Here we first calculate the inter ridge distance D which is the average distance between two
neighbouring ridges. For this scan each row to calculate the inter ridge distance using the
formula:
Inter ridge distance =
sum of all pixel with value 1
row length
Finally an averaged value over all rows gives D.
All we label all thinned ridges in the fingerprint image with a unique ID for further operation
using a MATLAB morphological operation BWLABEL.
Now the following 7 types of false minutia points are removed using these steps
(Figure 2.13).
m1 m2 m3 m4
m5 m6 m7
Figure 2.13: False Minutia Points
1. If d(bifurcation, termination) < D & the 2 minutia are in the same ridge then remove
both of them (case m1)
2. If d(bifurcation, bifurcation) < D & the 2 minutia are in the same ridge them remove
both of them (case m2, m3)
3. If d(termination, termination) ≈ D & the their directions are coincident with a small
angle variation & no any other termination is located between the two terminations
then remove both of them (case m4, m5, m6)
4. If d(termination, termination) < D & the 2 minutia are in the same ridge then remove
both of them (case m7)

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2.4.4 Unify Terminations and Bifurcation
Since various data acquisition conditions such as impression pressure can easily
change one type of minutia into the other, most researchers adopt the unification
representation for both termination and bifurcation. So each minutia is completely
characterized by the following parameters at last: 1) x-coordinate, 2) y-coordinate, and 3)
orientation.
The orientation calculation for a bifurcation needs to be specially considered. All
three ridges deriving from the bifurcation point have their own direction. The bifurcation is
broken into three terminations. The three new terminations are the three neighbour pixels of
the bifurcation and each of the three ridges connected to the bifurcation before is now
associated with a termination respectively [6,7][Figure 2.14].
Figure 2.14: A Bifurcation to Three Terminations
Three neighbors become termination(Left)
Each termination has their own orientation(right)
And the orientation of each termination (tx,ty) is estimated by following method :
A ridge segment is tracked whose starting point is the termination and length is D. All
coordinates of points in the ridge segment are summed up. The above summation is then
divided with D to get sx. And sy can be obtained using the same way.
The direction is obtained from:
atan ((sy-ty)/(sx-tx)) (2.7)
0 0 1
1 1 0
0 0 1

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2.5 Minutia Matching
Given two set of minutia of two fingerprint images, the minutia match algorithm
determines whether the two minutia sets are from the same finger or not. An alignment-based
match algorithm is used. It includes two consecutive stages: one is alignment stage and the
second is match stage [7].
1. Alignment stage. Given two fingerprint images to be matched, any one
minutia from each image is chosen, and the similarity of the two ridges
associated with the two referenced minutia points is calculated. If the
similarity is larger than a threshold, each set of minutia is transformed to a
new coordination system whose origin is at the referenced point and whose x-
axis is coincident with the direction of the referenced point.
2. Match stage: After obtaining two set of transformed minutia points, the elastic
match algorithm is used to count the matched minutia pairs by assuming two
minutia having nearly the same position and direction are identical.
2.5.1 Alignment Stage
The ridge associated with each minutia is represented as a series of x-coordinates
(x1,x2…xn) of the points on the ridge. A point is sampled per ridge length L starting from the
minutia point, where the L is the average inter-ridge length. And n is set to 10 unless the total
ridge length is less than 10*L.
So the similarity of correlating the two ridges is derived from:
5.0
0
22
0











m
i
ii
m
i
ii
Xx
Xx
S (2.8)
Where (xi~xn) and (Xi~XN) are the set of minutia for each fingerprint image
respectively. And m is minimal one of the n and N value. If the similarity score is larger than
0.8, then the next step is executed else the next pair of ridges are continued to match. For
each fingerprint, all other minutia are translated and rotated with respect to the reference
minutia according to the following formula:

18
_
_
_
= ∗
−
−
−
( . )
Where  ,, yx the parameters of the reference minutia and TM are is
TM =
cosθ −sinθ 0
sinθ cosθ 0
0 0 1
( . )
The following diagram illustrates the effect of translation and rotation:
X’-axis
Y-axis
X
F
E
y
Y’-axis D D
X-axis
Figure 2.15: Effect of Translation and Rotation
The new coordinate system is originated at minutia F and the new x-axis is coincident
with the direction of minutia F. No scaling effect is taken into account by assuming two
fingerprints from the same finger have nearly the same size.
2.5.2 Match Stage
The matching algorithm for the aligned minutia patterns needs to be elastic since the
strict match requiring that all parameters  ,, yx are the same for two identical minutiae is
impossible due to the slight deformations and inexact quantization of minutia [8].
The elastic matching of minutia is achieved by placing a bounding box around each
template minutia. If the minutia to be matched is within the rectangle box and the direction
Ө

19
discrepancy between them is very small, then the two minutiae are regarded as a matched
minutia pair. Each minutia in the template image either has no matched minutia or has only
one corresponding minutia.
The final match ratio for two fingerprints is the number of total matched pair over the
number of minutia of the template fingerprint. The score is 100*ratio and ranges from 0 to
100. If the score is larger than a pre-specified threshold, the two fingerprints are from the
same finger.
However, the elastic match algorithm has large computation complexity and is
vulnerable to spurious minutia.
ℎ =
( ℎ )
max( 1 2)
( . )

20
Chapter- 3
3. Results and Discussions
A fingerprint image is taking as an input from FVC2000 (Fingerprint Verification
competition 2000) to test the experiment performance [8]. This algorithm is used for
differentiating fingerprints by performing minutiae extraction.
3.1 Results for Minutiae Extraction algorithm
STEP1: First we take original fingerprint image and performing image enhancement by using
Histogram equalization. Figure 3.1(a) shows the original fingerprint image and its
corresponding histogram Equalized image is shown in Figure 3.1(b)
(a) (b)
Figure 3.1: (a) Original Image, (b) Image after Histogram Equalization

21
STEP2: For better enhancement we use histogram Equalization image as an input to the FFT
algorithm. Histogram equalized image is shown in Figure 3.2(a) and its enhanced image after
FFT is shown in Figure 3.2(b).
(a) (b)
Figure 3.2: (a) Histogram image, (b) Image Enhancement using FFT
STEP3: The enhanced image is binarized using Binarization algorithm. Enhanced image is
shown in Figure 3.3(a) and corresponding Binarized image shown in Figure 3.3((b).
(a) (b)
Figure 3.3: (a) FFT Image, (b) Image after Binarization

22
STEP4: Binarized image is shown in Figure 3.4(a) and its corresponding directional map
shown in Figure 3.4(b).
(a) (b)
Figure 3.4: (a) Binarization image, (b) Direction map of Binarization Image
STEP5: For extracting the un necessary area we use ROI algorithm. Binarized image is
shown in 3.5(a), and Its corresponding ROI image shown in 3.5(b).The size of binarized
image 256*256, but the size of ROI image is 224*224.
(a) (b)
Figure 3.5: (a) Binarization Image, (b) ROI Image

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STEP6: Figure 3.6(a) shows the open area of the ROI Image and Figure 3.6(b) shows the
close area of the ROI Image.
(a) (b)
Figure 3.6: (a) Open Operation, (b) Close Operation
STEP7: After Binarization the image is an input to the region of interest algorithm. it shows
the actual area of the fingerprint image by bounding the region with some useful colours in
Figure 3.7(b).
(a) (b)
Figure 3.7: (a) Adaptive Binarization, (b) ROI+BOUND image

24
STEP8: From ROI+BOUND image we found the ROI image. We use ROI image is an input
to the thinned algorithm. The Binarized image is shown in Figure 3.8(a) and its
corresponding image is shown in Figure 3.8(b). From the thinned figure We conclude that
Bifurcation and termination points are shown clearly.
(a) (b)
Figure 3.8: (a) ROI image, (b) Thinned Image
STEP9: We apply thinned image an input to the minutiae extraction algorithm for finding
minutiae point. Figure 3.9(a) shows thinned Image and Figure 3.9(b) shows minutiae marked
thinned image. Here red dots show the termination (ridge ending) points and yellow dots show
the bifurcation points.
(a) (b)
Figure 3.9: (a) Thinned Image, (b) Minutiae Marking after Thinning

25
3.2 Comparison Results For Minutiae matching
For simulation result, we have taken the threshold value of 0.65.
3.2.1 Two Different Fingerprints
Figure3.10 (a), Figure 3.10(b) shows two different fingerprint and Figure 3.10(c),
Figure 3.10(d) shows corresponding minutiae marked image. The match score value between
the two images is 0.37. As this value is less than the threshold value, we can conclude that
these two fingerprints are of two different persons.
(a) (b)
(c) (d)
Fingerprint, (d) Minutiae Extraction of Second Fingerprint

26
3.2.2 Two Fingerprint of a same person with a Little Difference
Figure3.11 (a), Figure 3.11(b) shows two different fingerprint, here Figure 3.11(b)
shows the little modify of Figure 3.11(a). Figure 3.10(c), Figure 3.10(d) shows corresponding
minutiae marked image. The match score value between the two images is 0.68. As this value
is Greater than the threshold value, we can conclude that these two fingerprints are of a same
person.
(a) (b)
(c) (d)
Figure 3.11: (a) First Fingerprint, (b) Second Fingerprint, (c) Minutiae extraction of First Fingerprint,
(d) Minutiae extraction of Second Fingerprint

27
Chapter- 4
4. Conclusion
The above implementation was an effort to understand how Fingerprint Recognition
is used as a form of biometric to recognize identities of human beings. It includes all the
stages from enhancement to minutiae extraction of fingerprints. There are various standard
techniques are used in the intermediate stages of processing.
The relatively low percentage of verification rate as compared to other forms of
biometrics indicates that the algorithm used is not very robust and is vulnerable to effects like
scaling and elastic deformations. Various new techniques and algorithm have been found out
which give better results.
Also a major challenge in Fingerprint recognition lies in the pre processing of the bad
quality of fingerprint images which also add to the low verification rate.
The reliability of any automatic fingerprint system strongly relies on the precision
obtained in the minutia extraction process. A number of factors are detrimental to the correct
location of minutia. Among them, poor image quality is the most serious one. In this project,
we have combined many methods to build a minutia extractor and a minutia matcher. The
following concepts have been used- segmentation using Morphological operations, minutia
marking by specially considering the triple branch counting, minutia unification by
decomposing a branch into three terminations.
There is a scope of further improvement in terms of efficiency and accuracy which can be
achieved by improving the hardware to capture the image or by improving the image
enhancement techniques. So that the input image to the thinning stage could be made better
this could improve the future stages and the final outcome.

28
References
[1] Fingerprint database - FVC2002 (Fingerprint Verification Competition 2002)
[2] Rafael C .Gonzalez, Richard E Woods digital image processing”2nd
edition, 2002.
[3] K. Jain, F. Patrick, A. Arun , “Handbook of Biometrics”, Springer Science Business
Media, LLC, 1st edition, pp. 1-42, 2008.
[4] D. Maio, and D. Maltoni, “Direct gray-scale minutia detection in fingerprints”, IEEE
Transactions Pattern Analysis and Machine Intelligence, vol. 19(1), pp. 27-40, 1997.
[5] D. Maltoni, D. Maio, and A. Jain, S. Prabhakar, “4.3: Minutiae-based Methods’
(extract) from Handbook of Fingerprint Recognition”, Springer, New York, pp. 141-
144, 2003.
[6] E. Hastings, “A Survey of Thinning Methodologies”, Pattern analysis and Machine
Intelligence, IEEE Transactions, vol. 4, Issue 9, pp. 869-885, 1992.
[7] K. Nallaperumall, A. L. Fred, and S. Padmapriya, “A Novel Technique for Fingerprint
Feature Extraction Using Fixed Size Templates”, IEEE 2005 Conference, pp. 371-
374, 2005.
[8] P. Komarinski, P. T. Higgins, and K. M. Higgins, K. Fox Lisa, “Automated Fingerprint
Identification Systems (AFIS)”, Elsevier Academic Press, pp. 1-118, 2005.

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