Abstract — Fingerprint is one of the biometric features mostly used for identification and verification. Latent fingerprints are conventionally recovered coming in to existence of crime scenes and are analyzed with active databases of well-known fingerprints for finding criminals. A bulk of matching algorithms with distant uniqueness has been developed in modern years and the algorithms are depending up on minutiae features. The detection of accepted systems tries to find which fingerprint in a database matches the fingerprint needs the matching of its minutiae against the input fingerprint. Since the detection complexity are more minutiae of other fingerprints. Therefore, fingerprint matching system is a higher than verification and detection systems. This paper discussed about the various novel techniques like Minutia Cylinder Code (MCC) algorithm, Minutia score matching and Graphic Processing Unit (GPU). The feature extraction anywhere in the extracted features is sovereign of shift and rotation of the fingerprint. Meanwhile, the matching operation is performed much more easily and higher accuracy.

1. INTERNATIONAL JOURNAL FOR TRENDS IN ENGINEERING & TECHNOLOGY
VOLUME 3 ISSUE 1 –JANUARY 2015 - ISSN: 2349 - 9303
A Survey Based on Fingerprint Matching
System
K. Lavanya1
1
Department of Computer Science & Engineering,
Bannari Amman Institute of Technology,
Anna University,
lavanyakrish.k93@gmail.com
M.Krishnamoorthi2
2
Department of Computer Science & Engineering,
Bannari Amman Institute of Technology,
Anna University,
krishnamoorthim@bitsathy.ac.in
Abstract — Fingerprint is one of the biometric features mostly used for identification and verification. Latent fingerprints
are conventionally recovered coming in to existence of crime scenes and are analyzed with active databases of well-known
fingerprints for finding criminals. A bulk of matching algorithms with distant uniqueness has been developed in modern
years and the algorithms are depending up on minutiae features. The detection of accepted systems tries to find which
fingerprint in a database matches the fingerprint needs the matching of its minutiae against the input fingerprint. Since the
detection complexity are more minutiae of other fingerprints. Therefore, fingerprint matching system is a higher than
verification and detection systems. This paper discussed about the various novel techniques like Minutia Cylinder Code
(MCC) algorithm, Minutia score matching and Graphic Processing Unit (GPU). The feature extraction anywhere in the
extracted features is sovereign of shift and rotation of the fingerprint. Meanwhile, the matching operation is performed
much more easily and higher accuracy.
Index Terms— Graphic processing unit, latent fingerprints, minutiae, minutia cylinder code and minutia score matching.
1 INTRODUCTION
fingerprint is the replication of a fingertip
epidermis, produced when a finger is pressured
against a plain surface. The most apparent structural
characteristic of a fingerprint is a pattern of ridges and
valleys; in a fingerprint pattern, ridges are dark, though
valleys are bright. Ridges and valleys are frequently run
in parallel and they will bifurcate as well as terminate.
When analyzed at the global level, the fingerprint
pattern explains about one or more regions where the
ridge lines which are characterized by high curvature,
frequent and termination. These regions are called
singularities or singular regions which shall be divided
into three types: loop, delta, and whorl.
Fig 1: Ridges & Valley in Fingerprint
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Fig 2: Singular regions in Fingerprint
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2. INTERNATIONAL JOURNAL FOR TRENDS IN ENGINEERING & TECHNOLOGY
VOLUME 3 ISSUE 1 –JANUARY 2015 - ISSN: 2349 - 9303
At the local level, other important features,
called minutiae which can be identified in the
fingerprint patterns. Minutia refers to dissimilar ways
that the ridges can be disconnected. For example, a
ridge can unexpectedly come in to an end called
termination, or can divide into two ridges. It is a specific
point in the fingerprint image. When the fingerprint is
obtained from the person, the numbers of minutiae are
recorded. Graphical Processing Unit (GPU) is “a single
chip processor and used to manage the performance of
video and 3D functions”. Some of the GPU typical
features are 2-D or 3-D graphics, rendering polygons
and texture mapping. The very first GPU was developed
by NVidia in 1999 and it is called as GeForce 256. The
GeForce 256 model can process up to 10 million
polygons per second and have more than 22 million
transistors. This model was a single-chip processor with
integrated transform and BitBLT support. Most GPUs
use their transistors for 3-D computer graphics. Anyhow
some have increased memory for checking vertices,
such as geographic information system (GIS)
applications. Some of the other modern technology
supports programmable shaders executes textures,
mathematical vertices and explicit color formats. Some
applications like computer-aided design (CAD) can
process over 200 billion operations per second and
transfer up to 17 million polygons per second. More
scientists and engineers use GPUs for more in-depth
calculated studies utilizing vector and matrix features.
1.1 FINGERPRINT BIOMETRIC SYSTEM
Nearly 70% of consumers’ worldwide support using
biometrics systems, such as fingerprints or voice
recognition, administered by a trusted organization
(bank, healthcare provider or government organization)
as a way to verify an individual's identity. Biometrics
consists of automated methods of recognizing a person
based on unique physical characteristic. Several types of
biometric system, but in some of different application,
hold at least one similarity: the biometric must be suited
upon a distinguishable human attribute such as an
individual’s fingerprint pattern.
Nowadays fingerprint devices are by far the most
popular form of biometric security used, with a wide
variety of systems on the market intended for general
and bulk market usage. Long gone are the massive
fingerprint scanners; now a fingerprint scanning device
can be small.
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A fingerprint is made up of a pattern of ridges and
furrows as well as characteristics that occur at Minutiae
points Fingerprint scanning essentially provides an
identification of a person based on the acquisition and
recognition of those unique patterns and ridges in a
fingerprint.
The actual fingerprint identification process
will change slightly between products and systems. The
basis of identification, nevertheless, is nearly the same.
Some of the standard systems are comprised of a sensor
for scanning a fingerprint and a processor which stores
the fingerprint database and software which compares
and matches the fingerprint to the predefined database.
Inside the database, a fingerprint is usually matched to a
reference number, or ID number, and then it is matched
to a person's name or account. In case of security the
fingerprint match is generally used to allow or disallow
access, but nowadays this can also be used for
something as simple as a time clock or payroll access.
In large government organizations and
corporations, biometrics plays a important role in
employee identification and security. Additionally some
data centers have moved on the bandwagon and have
implemented biometric scanners to enhances remote
access and management by adding another layer of
network security for system administrators.
Unfortunately the cost of invoking fingerprint and other
biometric security scanning in data centers is more
expensive, and many centers still entrust on ID badges
while waiting until biometric technology becomes a
little more pocket-book friendly.
Today companies have realized that fingerprint
scanning is an efficient means of security. While the
cost of executing biometric scanners in larger
organizations and data centers is still costly and finding
several fingerprint scanning devices which would fit
into the budget of many small offices and home users.
1.2 Fingerprint Segmentation
Before extracting the feature of a fingerprint, it is
important to separate the fingerprint regions (presence
of ridges) from the background[1]. This limits the
region to be processed and therefore reduces the
processing time and false feature extraction. A correct
segmentation may be, in some cases, very difficult,
especially in poor quality fingerprint image or noisy
images, such as presence of latents.
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3. INTERNATIONAL JOURNAL FOR TRENDS IN ENGINEERING & TECHNOLOGY
VOLUME 3 ISSUE 1 –JANUARY 2015 - ISSN: 2349 - 9303
The same information used for quality extraction, such
as contrast, ridge orientation and ridge frequency can be
used for the segmentation or inclusive the quantified
region quality may be used directly by considering as
background the regions with quality below some
threshold. Normally, the segmentation are also
computed by block in the same way as the quality
extraction.
1.3 Fingerprint Enhancement
The quality of the fingerprint image is determined by
many factors which sometime may be difficult to
control; therefore a fingerprint system must be able to
handle also the image of medium and low quality
(recoverable). In some cases it is possible to improve
significantly the image quality by applying some image
enhancement technique. The main purpose of such
procedure is to enhance the image by improving the
clarity of ridge structure or increasing the consistence of
the ridge orientation. In noisy regions, it is difficult to
define a common orientation of the ridges. The process
of enhancing the image before the feature extraction is
also called pre-processing. Here we show two technique
of pre-processing fingerprint images.
The first consist in a simple normalization and the
second uses Fourier transformation (FFT). There a lot of
other more complex techniques and there still place for
future researches on this topic. The enhancement may
be useful for the following cases like connect broken
ridges, eliminate noises between the ridges and
improving the ridge contrast.
1.4 Minutia Extraction
There has been proposed many methods for the minutia
extraction, the traditional method consist of the
following steps.
a) Binarization: This process consist in
converting the gray scale image in binary
image, i.e, the intensity of the image has only
two value: black, representing the ridges, and
white, representing the valleys and the
background. A simple method to binarize is to
use a global threshold value, however, it is not
well suited for noisy images, a more robust
method consist of using some rectangular
mask, rotate according the orientation of the
ridges.
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b) Thinning: The objective of thinning is to find the
ridges of one pixel width. The process consists in
performing successive erosions until a set of connected
lines of unit-width is reached. These lines are also called
skeletons. An important property of thinning is the
preservation of the connectivity and topology which
however can lead to generation of small bifurcation
artifacts and consequently to detection of false minutiae.
Therefore some procedure aiming the elimination of
these artifacts must be performed after the thinning.
c) Minutiae Detection: From the binary thinned image,
the minutia are detected by using 3x3 pattern masks.
Samples of masks used for identifying the ridge ending
and bifurcations point are shown in the figure below.
Although the process seems to be simple, it is necessary
to consider the elimination of false detected minutiae[2].
After a successful extraction of minutiae, they are
stored in a template, which may contain the minutia
position (x,y), minutia direction (angle), minutia type
(bifurcation or termination), and in some case the
minutia quality may be considered. During the
enrollment the extracted template are stored in the
database and will be used in the matching process as
reference template or database template. During the
verification or identification, the extracted minutia are
also stored in a template and are used as query template
during the matching.
2 PROCESSES USED IN FINGERPRINT
BIOMETRIC
The fundamental fingerprint biometric processes are:
a) Enrollment scans: The user’s fingerprints are
scanned and associated with that user’s identity in
the system. It is supervised process to allow for
preventing false identity creation and propagation.
In an enterprise scenario, enrollment would be done
at the time a person becomes employed and only
needs to be done once.
b) Template creation and storage: A biometric
template is created from biometric features derived
from the scanned fingerprint. The enrollment
template becomes the fingerprint biometric record
for the user. In some solutions, the fingerprint scan
itself may also be stored. See below figure
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4. INTERNATIONAL JOURNAL FOR TRENDS IN ENGINEERING & TECHNOLOGY
VOLUME 3 ISSUE 1 –JANUARY 2015 - ISSN: 2349 - 9303
Fig 3: Biometric Template Creation
c) Live scan: Each time a person requests access to the
system, a live scan of the fingerprint is made and a
live template is derived from that scan. The scanner
may also perform a liveness measurement, which
can distinguish between an artificial copy of the
fingerprint and a “live” finger and thus validate the
authenticity of the fingerprint.
d) Automated matching: The live template is
compared to a specific enrollment template and a
matching score is generated.
3 ALGORITHMS USED IN FINGERPRINT
MATCHING SYSTEM
3.1 Minutia Cylinder-Code
Minutia Cylinder-Code (MCC) algorithm accomplished
with local structure for every minutia. This structure
encodes spatial and directional connections between the
minutia and its neighborhood and can be represented
without difficult as a cylinder whose base and height are
compared to the spatial and fractional facts.
The weaknesses were forwarded by
recommending local minutiae matching method. Local
minutiae are distinguished by attributes which are
invariant with respect to the global transformations like
translation and rotation. Matching fingerprints placed on
local minutiae arrangements relaxes global spatial,
which are highly different; and accordingly reduces the
amount of information available for fussy fingerprints.
Local minutiae system can be classified into
nearest neighbor-based and fixed radius-based. The
neighbors of the central minutiae, K which is spatially
closest minutiae. These points to fixed-length
descriptors which will be matched very conveniently.
The descriptor length variables are depends on the local
minutiae density and leads to a more complex local
matching. It is more sophisticated against missing and
false minutiae. Two drawbacks are the absolute
encoding of radial angles, and the missing directional
difference between the central minutia and the
neighboring ones.
IJTET©2015
The technique proposed by Feng [6] does not
endure from the listed disadvantages and can be
considered as a new fixed-radius local matching
algorithm. The MCC [2] introduces a novel minutiae-
only local representation aimed at combining the both
advantages of neighbor-based which matches minutiae
from two fingerprints with similar neighbor and fixed-
radius structures uses the information of all the minutiae
at once.
3.2 Minutia Score Matching
RAVI. J and K. B. RAJA [5] presented a paper using
Fingerprint Recognition using Minutia Score Matching
method (FRMSM). The pre-processing the original
fingerprint involves image binarization, ridge ending,
and noise removal. The Minutia Score Matching method
is used for fingerprint identification to correspond the
minutia points. The proposed method FRMSM gives
better False Matching Ratio (FMR) values. The need is
to identify a person for security. The block diagram of
FRMSM determines the match of tested fingerprint with
the template database using Minutia Matching Score
algorithm.
Fig 4: Block diagram of FRMSM
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a) Fingerprint Image: The input fingerprint
image is the gray scale image of the person’s
fingerprint; where the minutiae points are the
locations and the ridge becomes discontinuous.
b) Binarization: The use of Binarization in this
process is to convert a gray scale image into a
binary image i.e. 0 or 1 by fixing the threshold
value.
c) Block Filter: The binarized image is thinned
using Block Filter to minimize the thickness of
all ridge lines. It will not change the location
and orientation of minutiae points.
d) Minutiae Extraction: The minutiae location
and the angles are derived after minutiae
extraction.
e) Minutiae Matching: To compare with the
input fingerprint data with the expected data,
so then minutiae matching technique is used.
f) Matching Score: It achieves the better False
Matching Ratio
3.4 GRAPHIC PROCESSING UNIT
It is a processor optimized for 2D/ 3D graphics and
video. It provides real- time visual interaction with the
computed objects. In 2007, Compute Unified Device
Architecture (CUDA) was invented. It is a parallel
computing platform and programming model created by
NVIDIA and implemented by the graphics processing
units (GPUs)
4 CONCLUSION
In this paper, we have discussed about the various
approaches and techniques involved in the Fingerprint
matching system. This paper presents the thorough
information on the subject of fingerprint biometrics and
expressly alert on techniques that overcome the
disadvantages of fingerprint biometric system.
Fingerprint biometric system is used most of the fields
except chemical industries since the finger print of
individual’s people working in Chemical industries are
habitually affected. It wrap up that Finger print
biometrics is solitary of the efficient, protected, cost
efficient, ease to utilize technologies for consumer
authentication and according to our investigation almost
all drawbacks are prevail over in fingerprint biometric
system.
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[3] M. Schatz, C. Trapnell, A. Delcher, and A.
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