GANNON UNIVERSITYELECTR.docx

GANNON UNIVERSITY
ELECTRICAL AND COMPUTER ENGINEERING
DEPARTMENT
FALL2015
GECE 572: DIGITAL SIGNAL
PROCESSING
FINGER PRINT RECOGNITION USING MINUTIAE BASED
FEATURE
FINAL PROJECT
Prepared by
THADASINA PRUTHVIN REDDY
[email protected]
SALMAN SIDDIQUI
[email protected]

Table of contents
1. Abstract
2. Introduction
3. Fingerprint matching
4. Pre-processing stage
5. Minutiae extraction stage
6. Post-processing stage
7. Merits & Demerits
8. Applications & future scope
9. Conclusions
10.References
1. Abstract

Nowadays, conventional identification methods such as driver's
license, passport, ATM cards and PIN codes do not meet the
demands of this wide scale connectivity. Automated biometrics
in general, and automated fingerprint authentication in
particular, provide efficient solutions to these modern
identification problems. Fingerprints have been used for many
centuries as a means of identifying people. The fingerprints of
individual are unique and are stay unchanged during the life
time. Fingerprint matching techniques can be placed into two
categories, minutiae-based and correlation based. Minutiae-
based techniques first find minutiae points and then map their
relative placement on the finger. However, there are some
difficulties when using this approach. It is difficult to extract
the minutiae points accurately when the fingerprint is of low
quality the correlation-based method is able to overcome some
of the difficulties of the minutiae-based approach. However, it
has some of its own shortcomings. Correlation-based techniques
require the precise location of a registration point and are
affected by image translation and rotation.
2. Introduction
Biometric recognition refers to the use of distinctive
physiological (e.g. fingerprint, palm print, iris, face) and
behavioral (e.g. gait, signature) characteristics, called biometric
identifiers for recognizing individuals.

Fingerprint recognition is one of the oldest and most reliable
biometric used for personal identification. Fingerprint
recognition has been used for over 100 years now and has come
a long way from tedious manual fingerprint matching. The
ancient procedure of matching fingerprints manually was
extremely cumbersome and time-consuming and required skilled
personnel.
Finger skin is made up of friction ridges and sweat pores all
along these ridges. Friction ridges are created during fetal life
and only the general shape is genetically defined. The
distinguishing nature of physical characteristics of a person is
due to both the inherent individual genetic diversity within the
human population as well as the random processes affecting the
development of the embryo. Friction ridges remain the same
throughout one’s adult life. They can reconstruct themselves
even in case of an injury as long as the injury is not too serious.
Fingerprints are one of the most mature biometric technologies
and are considered legitimate proofs of evidence in courts of
law all over the world. In recent times, more and more civilian
and commercial applications are either using or actively
considering using fingerprint-based identification because of
the availability of inexpensive and compact solid state scanners
as well as its superior and proven matching performance over
other biometric technologies.
Some important terms related to fingerprint identification
systems are explained below:
Fingerprint Acquisition: How to acquire fingerprint images
and how tore present them in a proper machine-readable format.

fingerprints are from the same finger.
in a database.
Fingerprint Classification: To assign a given fingerprint to
one of the pre specified categories according to its geometric
characteristics.
In case of both fingerprint identification and fingerprint
verification systems, our tasks will be broken up into 2 stages:
1. Off-line phase: Several fingerprint images of the fingerprint
of a person to be verified are first captured and processed by a
feature extraction module; the extracted features are stored as
templates in a database for later use.
2. On-line phase: The individual to be verified gives
his/her identity (in case of a verification system) and places
his/her finger on the inkless fingerprint scanner, minutia points
are extracted from the captured fingerprint image. These
minutiae are then fed to a matching module, which matches
them against his/her own templates in the database (in case of a
verification system) or against all the users in the database (in
case of an identification system).
2.1 What is a fingerprint?
Fingerprints are the most important part in biometric for human
identification. They are unique and permanent from birth to
death. So, fingerprints have been used for the forensic

application and personal identification.
A fingerprint is collection of many ridges and furrows
(Valleys). The continuous dark pattern flow in fingerprint is
called ridges and the light area between ridges is called furrows.
Fingerprint has some unique points on the ridge which is known
as minutiae point. Here we can consider two main types of
minutiae points which are termination point and bifurcation
point as shown in Fig.1.Termination: where a ridge ends and
Bifurcation: where ridges split into two parts.
Figure 1 Minutiae Points (Termination, Bifurcation)
2.2 Fingerprint Recognition
The fingerprint recognition problem can be grouped into two
sub-domains: one is fingerprint verification and the other is
fingerprint identification. In addition, different from the manual
approach for fingerprint recognition by experts, the fingerprint
recognition here is referred as AFRS (Automatic Fingerprint
Recognition System. Fingerprint verification is to verify the
authenticity of one person by his fingerprint. The user provides
his fingerprint together with his identity information like his ID
number. The fingerprint verification system retrieves the
fingerprint template according to the ID number and matches
the template with the real-time acquired fingerprint from the
user. Usually it is the underlying design principle of AFAS
(Automatic Fingerprint Authentication System).

Figure 2. General architecture of a fingerprint verification
system
Fingerprint identification is to specify one person's identity by
his fingerprint(s). Without knowledge of the person's identity,
the fingerprint identification system tries to match his
fingerprint(s) with those in the whole fingerprint database. It is
especially useful for criminal investigation cases. And it is the
design principle of AFIS (Automatic Fingerprint Identification
System).However, all fingerprint recognition problems, either
verification or identification, are ultimately based on a well-
defined representation of a fingerprint. As long as the
representation of fingerprints remains the uniqueness and keeps
simple, the fingerprint matching, either for the
1-to-I verification case or 1-to-m identification case, is
straightforward and easy.
2.3 Techniques for Fingerprint Recognition
1) Minutiae Extraction based Techniques: Mostly accepted
finger scan technology is based on Minutiae. Minutiae based
techniques produce the fingerprint by its local features,
like termination and bifurcation. When minutiae points match
between two fingerprints so fingerprint are match. This
approach has been genuinely studied, and it is the backbone of
the current available fingerprint recognition products.

2) Pattern Matching or Ridge Feature based Techniques: Feature
extraction are established on series of ridges as averse to
different points which design the basis of pattern matching
techniques over Minutiae Extraction. Minutiae points can be
change by wear and tear and the main drawback are that these
are acute to proper adjustment of finger and need large storage .
3) Correlation based Techniques: Correlation based technique is
used to match two fingerprints, the fingerprint are adjusted and
computed the correlation for each corresponding pixel. They
can match ridge shapes, breaks, etc. Main disadvantages of this
method are its computational complication and less tolerance to
non-linear distortion and contrast variation.
4) Image based Techniques: This technique attempt to do
matching which based on the global features of an all
fingerprint images. It is an advance and newly develops method
for fingerprint recognition
3. Fingerprint matching
The matching of fingerprint is achieved by some image
processing steps. These step can easily be understand by the
algorithm below:
Input: Two Gray-scale Fingerprint image.
Output: Verify the fingerprint image using minutiae matching.

Step 1: Enhancement of Input Image i.e. fingerprint image using
Histogram equalization. Step 2: Binarized the enhanced
fingerprint image.
Step 3: Selection of ROI (Region of Interest) in binarized
image.
Step 4: Thinning of the Region of Interest as the part of
fingerprint image. Step 5: Minutiae points are extracted from
image.
Step 6: Comparison and matching of one fingerprint to another
fingerprint.
Step 7: Match the minutiae points of two images are computed.
If Minutiae points are matched in both images so fingerprint
matching score are 1 and if it is not matched then
matching score are 0 they are mismatched.
Figure 3. Fingerprint Matching block diagram
The overall implementation of algorithm may also express by
using block diagram, as shown above. This block diagram is sub
divided as pre-processing stage, minutiae extraction stage and
post-processing stage
4. Pre-processing stage

4.1 Image Acquisition
The first stage of any vision system whether for identification
or verification is the image acquisition stage. Nowadays, the
automated fingerprint verification systems use live-scan digital
images of fingerprints acquired from a fingerprint sensor. These
sensors are based on optical, capacitance, ultrasonic, thermal
and other imaging technologies.
1. Optical Sensors: These are the oldest and most widely used
technology. In most devices, a charged coupled device (CCD)
converts the image of the fingerprint, with dark ridges and light
valleys, into a digital signal. They are fairly inexpensive and
can provide resolutions up to 500 dpi. Most sensors are based
on FTIR (Frustrated Total Internal Reflection) technique
to acquire the image. In this scheme, a source illuminates the
fingerprint through one side
Figure 4 :(a) General schematic for an FTIR based optical
sensor (b) Schematic of a capacitive sensor
of the prism as shown (Figure 4).Due to internal reflection
phenomenon, most of the light is reflected back to the other side
where it is recorded by a CCD camera. However, in regions
where the fingerprint surface comes in contact with the
prism, the light is diffused in all directions and therefore
does not reach the sensor resulting in dark regions. The quality

of the image depends on whether the fingerprint is dry or wet.
Another problem faced by optical sensors is the residual
patterns left by the previous fingers. Furthermore it has been
shown that fake fingers are able to fool most commercial
sensors. Optical sensors are also among the bulkiest sensor due
to the optics involved.
2. Capacitive Sensors: The silicon sensor acts as one plate of a
capacitor, and the finger as another other. The capacitance
between the sensing plate and the finger depends inversely as
the distance between them. Since the ridges are closer, they
correspond to increased
capacitance and the valleys correspond to smaller capacitance.
This variation is converted into an
8-bit gray scale digital image. Most of the electronic devices
featuring fingerprint authentication use this form of solid state
sensors due to its compactness. However, sensors that are
smaller than
0.5”x0.5” are not useful since it reduces the accuracy
recognition.
3. Ultra-sound Sensors: Ultrasound technology is perhaps the
most accurate of the fingerprint sensing technologies. It uses
ultrasound waves and measures the distance based on the
impedance of the finger, the plate, and air. These sensors are
capable of very high resolution. Sensors with
1000dpi or more are already available (www.ultra-scan.com).
However, these sensors tend to be very bulky and contain
moving parts making them suitable only for law enforcement
and access control applications.

4. Thermal Sensors: These sensors are made up of pyro-electric
materials whose properties change with temperature. These
are usually manufactured in the form of strips .As the
fingerprints is swiped across the sensor, there is differential
conduction of heat between the ridges and valleys(since skin
conducts heat better than the air in the valleys) that is measured
by the sensor. Full size thermal sensors are not practical since
skin reaches thermal equilibrium very quickly once placed on
the sensor leading to loss of signal. This would require us to
constantly keep the sensor at a higher or lower temperature
making it very energy inefficient. The sweeping action prevents
the finger from reaching thermal equilibrium leading to good
contrast images. However, since the sensor can acquire only
small strips at a time, a sophisticated image registration
and reconstruction scheme is required to construct the whole
image from the strips.
One of the most essential characteristics of a digital fingerprint
image is its resolution which indicates the number of dots or
pixels per inch (ppi). The minimum resolution that allows the
feature extraction algorithms to locate minutiae is 250 to 300
ppi.
4.2 Image Enhancement
Fingerprint image enhancement is to make the image clearer for
easy further operations.
The performance of minutiae extraction algorithms and other
fingerprint recognition techniques relies heavily on the quality

of the input fingerprint images. A fingerprint image is
firstly enhanced before the features contained in it could be
detected or extracted. A well enhanced image will provide a
clear separation between the valid and spurious features.
Since the fingerprint images acquired from sensors or other
media are not assured with perfect quality. However the
fingerprint images obtained are usually poor due to elements
that corrode the clarity of the ridge elements. This leads to
problems in minutiae extraction. Spurious features are those
minutiae points that are created due to noise or artifacts and
they are not actually part of the fingerprint.
In an ideal fingerprint image, ridges and valleys alternate and
flow in a locally constant direction. Thus, image enhancement
techniques are employed to reduce the noise and enhance the
definition of ridges against valleys. In order to ensure good
performance of the ridge and minutiae extraction algorithms in
poor quality fingerprint images, an enhancement algorithm to
improve the clarity of the ridge structure is necessary.
Enhancement methods, for increasing the contrast between
ridges and furrows and for connecting the false broken points of
ridges due to insufficient amount of ink are very useful to keep
a higher accuracy to fingerprint recognition.
Histogram Equalization
It is a method for enhance the fingerprint image. Fingerprint
image enhancement is to create clearer for easy other
operations. Histogram equalization is to extend the pixel value
of an image so as to increase the perceptional information. The
histogram of a original fingerprint image has the bimodal type
the histogram after the histogram equalization occupies all the
range from 0 to
255 and the visualization effect is enhanced.

In MATLAB histogram equalization is done by using MATLAB
function.
histeq (image_file_name);
Below, the figure shows the original image histogram and
histogram after equalization operation.
4.3 Binarization
A Fingerprint-Image-Binarization transforms an 8-bit gray
image to a 1-bit binarized image. Most minutiae extraction
algorithms operate on binary images where there are only two
levels of interest: 0-value holds for ridges and 1-value for
furrows. And after the binarization operation ridges are
highlighted with black color and furrows are highlighted with
white color.
An adaptive binarization method is achieved to binarize the
fingerprint image. In this method image is split into blocks of
16 x 16 pixels. A pixel value is set 1 if its value is greater than
the mean intensity value of the accepted block to which the
pixel belongs.

4.4 Image segmentation
This is a segmentation technique. The main motive of the
segmentation is to make the image simpler which can be
representing very easily and to make image meaningful that will
be easy to analyze. Generally ROI (Region of Interest) is very
useful for analyze a fingerprint image. It is a subset of an image
or a dataset analyze for a particular purpose. When the image
area has ineffective ridges and furrows so firstly it made wider
and larger in all directions.
There are two regions that describe any fingerprint image;
namely the foreground region and the background region. The
foreground regions are the regions containing the ridges and
valleys. The ridges are the raised and dark regions of a
fingerprint image while the valleys are the low and white
regions between the ridges. The foreground regions often
referred to as the Region of Interest (ROI). The background
regions are mostly the outside regions where the noises
introduced into the image during enrolment are mostly found
Region of Interest (ROI)
To extraction of the ROI is performed in two steps: First, block
direction estimation and direction variety check; second, used

some Morphological methods.
Two types of morphological methods are available i.e. OPEN
and CLOSE. The OPEN operation can enlarge the images and
eliminate background noise. And CLOSE operation can shrink
images and eliminate small cavities.
bwmorph (x, 'close'); bwmorph (y, 'open');
5. Minutiae extraction stage
After the enhancement of the fingerprint image, the image is
ready for minutiae extraction. For proper extraction, however, a
thinning algorithm is applied to the enhanced image. It produces
a skeletonized representation of the image.
5.1 Thinning
Thinning is a morphological operation that is used to remove
selected foreground pixels from binary images. It is used to
eliminate the redundant pixels of ridges till the ridges are just
one pixel wide. Thinning is normally only applied to binary
images, and produces another binary image as output. It is the
final step prior to minutiae extraction. All the pixels on the
boundaries of foreground regions that have at least one

background neighbor are taken. Any point that has more than
one foreground neighbor is deleted as long as doing so does not
locally disconnect the region containing that pixel.
This is done by using the MATLAB thinning function that is:-
Then the thinned image is filtered by using the following three
MATLAB functions. This are some H is breaks, isolated points
and spikes.
e, 'hbreak',k)
The conditions for better thinning result:
a) Each ridge should be thinned to its center pixel. b)
Noise and singular pixels should be removed.
c) No further removal of pixels should be possible after
accomplish of thinning process

5.2 Minutiae Marking
The method extracts the minutiae from the enhanced image.
This method extracts the ridge endings and bifurcations from
the skeleton image by examining the local neighbourhood of
each ridge pixel using a 3×3 window. The method used for
minutiae extraction is the crossing number (CN) method. This
method involves the use of the skeleton image where the ridge
flow pattern is eight-connected. The minutiae are extracted by
scanning the local neighbourhood of each ridge pixel in the
image using a 3×3 window. CN is defined as half the sum of the
differences between the pairs of adjacent pixel.
CN=0.5 i=1Σ8 (Pi- Pi+1)
The ridge pixel can be divided into bifurcation, ridge ending
and non-minutiae point based on it. A ridge ending point has
only one neighbor, a bifurcation point possesses more than
two neighbors, and a normal ridge pixel has two neighbors. A
CN value of zero refers to an isolated point, value of one to a
ridge ending, two to a continuing ridge point, three to a
bifurcation point and a CN of four means a crossing point.
Minutiae detection in a fingerprint skeleton is implemented by
scanning thinned fingerprint and counting the crossing
number. Thus the minutiae points can be extracted.
Cn{p} =1 Ridge Ending Cn{p} =3 Ridge Bifurcation

In the proposed method, the minutiae point’s locations and their
considered direction from the 8 directions (N, S, W, E, NE,
NW, SE, SW) are recorded then they used to construct the
database depending of the number of recorded minutiae point
and their direction.
Suppose P is the checked point and P1-P8 are neighbourhood
pixels
If CN = 3 then
If P1 and P3 and P7 = 1 then direction = W Else if P1 and P3
and P5 = 1 then direction = S Else if P1 and P7 and P5 = then
direction = N Else if P3 and P5 and P7 = 1 then direction = E
Else if P4 and P3 and P5 = 1 then direction = SE Else if P3 and
P2 and P1 = 1 then direction = SW Else if P3 and P5 and P6 = 1
then direction = NE
Else if P4 and P8 and P5 = 1 then direction = NW End if
If CN = 1 then
If P1 = 1 then direction = W If P1 = 1 then direction = W If P3
= 1 then direction = S If P7 = 1 then direction = N If P5 = 1
then direction = E
If P4 = 1 then direction = SE If P2 = 1 then direction = SW If
P6 = 1 then direction = NE If P8 = 1 then direction = NW
6. Post-processing stage

6.1 Minutiae Matching
When all minutiae points of two fingerprint images are
extracted in selected region of interest. Now, minutiae matching
are performed for verification. Basically, minutiae
Matching is a process which completed in two steps:
1) Find Total Minutia Points: This step is used to calculate the
total number of Ridge and Bifurcation points separately. And it
compares the calculated value with the original image values.
2) Find Location of Minutiae Points: It works on the basis of
Minutia Marking process. Simply, when minutia points marked
on the image it also store the location of the point. This stored
information it used to compare two different images at
verification process. If both the images belong to the same
person then the location of ridge/bifurcation will match.
Otherwise matching of fingerprint images unsuccessful.
6.2 Remove False Minutiae
In fingerprint recognition, the goal is too able to detect the
minutiae point and to reduce the false minutiae in the
fingerprint image. In order to remove false minutia, there are a
few process that need to be going through which are minutia
marking and false minutia removal.
The procedures in removing false minutia are:
1. If the distance between one bifurcation and one termination

is less than D and the two minutiae are in the same ridge (ml
case). Remove both of them. Where D is the average inter-ridge
width representing the average distance between two parallel
neighboring ridges.
2. If the distance between two bifurcations is less than
D and they are in the same ridge, remove the two bifurcations.
3. If two terminations are within a distance D and their
directions are coincident with a small angle variation. And they
suffice the condition that no any other
termination is located between the two terminations. Then the
two terminations are
7. Merits & Demerits
Advantages
cards.
passwords.
Disadvantages

Gelatin.
great threat to Security and
intellectual property.
Some of the employees may find it uncomfortable to Have
their fingerprint stored with the employer.
8. Applications & future scope
Applications
1. Financial services (e.g. ATM )
2. Immigration & border control (e.g. points of entry
declared for frequent travelers, passport and visa cases )
3. Social services (e.g. fraud preventation in entitlement
programmers)
4. Health care (e.g. security measure for privacy or medical
records)
5. Physical access control (e.g. at institutional, government &
residential establishment)
6. Time & attendance (e.g. replacement of time punch card)
7. Computer Security (e.g. personal computer access, network
access, Internet use, e- commerce, e-mail, encryption)

8. Telecommunications (e.g. mobile phones, call center
technology, phone cards, televised shopping)
9. Law enforcement (e.g. criminal investigation, national ID,
driving license, rehabilitation institutions/prison, home
confinement, small gun)
Further works which can be carried out include following.
1. To perform statistical experiment used in this project on a
larger sample size & a conduct a full analysis of observed
result.
2. An implementation of a smarter matching algorithm should
be able to improve the verification & identification process.
3. Issue need to be addressed in the systematic way in
developing a fool proof fingerprint based identification system
for a wide scale development e.g. encryption security of
fingerprint template detection of force fingers, privacy concern
etc.
4. Implementation of on-line fingerprint verification &
Identification system using biometric device.
9. Conclusion
This seminar has concentrated on fingerprint based biometric
identification & verification systems. The primary focus is
subsequent extraction of minutiae by direct gray scale image
extraction technique .There are two important operations in pre-
processing stage as Histogram Equalization, and Selection of
ROI. These two operations make this algorithm efficient. The
Histogram Equalization enhanced the quality of Input-image,

which actually help to produce accurate calculation. This
research concludes that the Fingerprint Verification is possible
even the quality of the fingerprint image got affected. The ROI
based approach reduces the processing time of algorithm by
working on segment not the complete image, which means it
makes fingerprint matching faster. The verification is done for
selected region that authenticate the pattern. The literature of
this technique is deeply studied and experimentally executed in
MATLAB.
10. References
1. Raymond Thai, ‘Fingerprint Image Enhancement and
Minutiae-Extraction,”
Thesis submitted to School of Computer Science and Software
Engineering, University of
Western Australia
2. AK Jain, A. Ross, and S. Prabhakar, Fingerprint Matching
Using Minutiae
and Texture Features , Proc. of International Conference on
Image Processing, 2001
3. Digital Image Processing by Rafael C. Gonzalez and
Richard E. Woods,Pearson Education,
2003
4. Digital Image Processing using MATLAB: Rafael C.
Gonzalez, Richard E. Woods 2nd
Edition, 2009

5. Fingerprint Image Enhancement and Minutiae Extraction by
Raymond Thai 2002
6. Online Fingerprint Verification by Sharat Chikkerur CUBS,
University of Buffalo

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