biometric fingerprint authentication by minutiae ... · fig 5 flowchart for enrollment algrithm...
TRANSCRIPT
Biometric Fingerprint Authentication by Minutiae Extraction
Using USB Token System
Prof. Archana C.Lomte1(Author1), Dr.S.B.Nikam
2
1JSPM’s Bhivrabai Sawant Institute of Technology & Research(W), Department of Computer Engg
2Government Polytechnique,Department of Computer Engg.
ABSTRACT: Biometrics is automated methods of
identifying a person or verifying the identity of a
person based on a physiological or behavioral
characteristic. Many body parts, personal
characteristics have been suggested and used for
biometric security system. Which include fingerprint,
hands, face, eyes, voice etc. so all physiological
characteristics are the permanent identification of
every person by birth and it never change throughout
the life. There are number of Biometric techniques
available to fulfill the different kinds demand in the
market. Every method consists number of advantages
compared to the others. But as if now there is no such
method able to completely satisfy the current security
system. so because of this research is continuously
going on to find out the newer methods that will
provide a higher security.In this paper, the different
methods of biometric authentication is presented.
Keywords: Minutiae Extraction, USB Token,
Biometric Technique.
1 INTRODUCTION: In today’s highly
computerized advancing digital world the security is
becoming most important topic for authentication.
Existing authentication measures rely on information
based on approaches like passwords, PIN numbers or
token based approaches like passport, swipe cards.
These methods are not very secure. These methods
can be easily accessed through number of ways by
stealing or by shearing because of this it is quite
impossible to differentiate between authorized user
and the person having access to the tokens or
passwords.
1.1 WHAT IS BIOMETRICS?
It is the technology of analyzing biological data. It
measures and analyzes biological characteristics such
as fingerprint,iris,hands,face,ear etc for
authentication purpose.
Biometric characteristics of human can be divided in
to three types.1) Physiological
2)Behavioral3)Chemical/Biological
Fig.1 Classification of Biometric Characteristics
1.2 NEED OF BIOMETRICS:
There are two major ways of Biometrics:
Identification and Verification.
Identification is determining that who a person is.
In this identification taking the measured
characteristics and trying to match in a database
containing records of that particular person and that
characteristic.
Verification is determining if he is the same person.
In this it involves taking the measured characteristics
Prof. Archana C Lomte et al, Int.J.Computer Technology & Applications,Vol 4 (2),187-191
IJCTA | Mar-Apr 2013 Available [email protected]
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ISSN:2229-6093
and compare with the previously stored
characteristics for that person.
2 VARIOUS EXISTING METHOD USED FOR
FINGERPRINT MATCHING
1) Here a method for extraction of minutiae from
fingerprint images using midpoint ridge contour
representation. In this midpoint ridge contour method
first step is segmentation. Segmentation is to
separate foreground from background of fingerprint
image. Image of 64x64 region is extracted from
fingerprint. So the grayscale intensities in that
regions are normalized to a constant mean and
variance to remove the effect of sensor noise and
grayscale variations due to finger pressure
differences. After the completion of normalization
difference between two of ridges are enhanced by
filtering 64x64 normalized windows by appropriate
Gabor filter. That processed fingerprint image is
scanned from top to bottom and left to right and
transition from whit(background) to black
(foreground) are detected[1].
2) In second method Robert Hastings developed a
enhancing the ridge pattern by using orientation
transmission process by a variation of different
values measured in different direction and
transmission to smooth the image in the direction to
parallel to the ridge flow. So in this process the image
intensity varies easily as one pass through the ridges
or valleys by removing most of the small
irregularities and breaks but with the uniqness of the
individual ridges and valleys[2].
3)In this method V.V Kumari and N.Suryanarayanan
proposed for execution measure of local operators in
fingerprint by observing the edges of fingerprint
images using five local operators namely
Sobel,Roberts,Prewitt,Canny and LoG.So after
processing the edge detected image is further
segmented to extract individual segment from the
image[3].
4) The Directional Fingerprint Processing method
was developed by Ballan M. So the directional
fingerprint processing using fingerprint smoothing,
categorization and identification based on the
singular points (delta and core points) received
from the directional histogram of a fingerprint.
The process includes directional image formation,
directional image block representation, singular point
detection and decision. So this method gives
matching decision vectors with minimum errors,
because of this method is simple and fast[4].
5) Filter based representation technique for
fingerprint identification which is developed by
Prabhakar S. and Jain A.K. . In this technique both
local and global characteristics in a fingerprint are
take in to consideration to make identification.So
each fingerprint image is filtered in a number of
directions. The matching stage computes the
Euclidian distance between the template finger code
and the input finger code. At last we are getting good
matching with high accuracy[5].
6) Prposed fingerprint identification technique
developed by G. Sambasiva Rao uses grey level
watershed method which find out the ridges present
on a fingerprint image by directly scanned
fingerprints.
7) Eric Kukula developed a method to explore the
result of five different strength levels on fingerprint
matching performance. In this process the results
expose a significant differences in minutiae counts
and image quality score based on the strength level
and each sensor technology.
8) Luping Ji and Zhang Yi propsed a method for
judging four directions orientation field by
considering four steps 1) preprocessing fingerprint,2)
determining the primary ridge of fingerprint block iii)
estimating block direction
9) M.R. Girgisa developed a method to explain a
fingerprint matching based on lines extraction and
graph matching principles which consists of a genetic
M. R. Girgisa et al., proposed a method to describe a
fingerprint matching based on lines extraction and
graph matching principles by adopting a hybrid
scheme which consists of a genetic algorithm phase
and a local search phase. Experimental results
demonstrate the robustness of algorithm.
Prof. Archana C Lomte et al, Int.J.Computer Technology & Applications,Vol 4 (2),187-191
IJCTA | Mar-Apr 2013 Available [email protected]
188
ISSN:2229-6093
2.1 PROPOSED SYSTEM
In the proposed system fingerprint based
authentication for USB Token systems. This
fingerprint authentication system can divide into two
phases of enrollment and verification. The
verification algorithm consists of three parts: Image
Pre-Processing, Minutiae Extraction and Minutiae
Matching. First two steps Pre-processing and
Extraction cannot be executed on the resource-
constrained environments such as USB token.
Fig.2 Fingerprint Based Match On Token
The Minutiae Matching step(alignment and matching
stages) in this we have to compute the similarity
between the enrolled minutiae and the input minutiae
which is executed on the Match-on-Token, whereas
other steps Image Pre-Processing and Minutiae
Extraction are executed on the host PC.
Fig 3 Architecture of the USB Token
In the Table 1 the system specification of the USB
token is developed. The USB token employs
206MHz CPU, 16MBytes Flash memory, and
1MBytes RAM. The size of the USB token is
7cm2cm1cm. Fig. 2. shows the architecture of the
USB token. The processing core of the Intel SA-1110
processor includes the USB end-point interface to
communicate between the host PC and the token.
Also, the USB token employs the serial port and
JATG interface to use in debugging.
3 FINGERPRINT ENROLLMENTS FOR THE
USB TOKEN:
The minutiae-based fingerprint
authentication systems based on the comparison
between two minutiae sets, a reliable minutiae
extraction algorithm is critical to the performance of
the system. As we know, minutiae are detected from
the raw image through the preprocessing and
extraction stage. However, the extraction stage has
some false minutiae detected, and true minutiae
missed as well. Thus, performance of a fingerprint
authentication system is controlled by three kinds of
errors. In particular, if they occur during an
enrollment phase and are stored as enrolled template,
they will degrade the overall performance
significantly, i.e., the falsely detected minutiae will
affect the matching phase continuously. Therefore,
the wrongly detected minutiae need to be discarded
and the missed ones need to be compensated prior to
be stored as enrolled minutiae.
Prof. Archana C Lomte et al, Int.J.Computer Technology & Applications,Vol 4 (2),187-191
IJCTA | Mar-Apr 2013 Available [email protected]
189
ISSN:2229-6093
Fig.4 Fingerprint-based Authentication system
using minutiae Impression
In the above diagram the fingerprint-based
authentication system using plural fingerprint images
during enrollment. Enrolled minutiae are generated
from several real fingerprint images, which can
prevent false minutiae from being stored as enrolled
minutiae and true minutiae missed.
4 FINGERINT VERIFICATION FOR THE USB
TOKEN:
In the fingerprint matching stage there are
two phases one is minutiae alignment and point
matching. As we know the stored template and input
minutiae cannot be compared directly because noise
or deformations. The first phase i.e. minutiae
alignment phase computes the parameters in order to
align the 2 fingerprints. Then the point matching
phase counts the overlapping minutiae pairs in the
aligned fingerprints. Actually, the first phase minutia
alignment phase requires a lot of memory space and
time for execution than the second phase.
In following algorithm accumulator array is
used in order to compute the shift and rotations
parameters [7]. When the 2 fingerprints are from the
same person the I/P to the alignment phase consists
of two sets of minutiae points P and Q extracted
from fingerprint images [6]. We think that the second
fingerprint image can be obtained by applying a
similarity transformation such as rotation and
translation to the first image. The second point set Q
is then rotated and translated version of the set P,
where points may be shifted by a random noise,
some points may be added and some points deleted.
The main task of fingerprint alignment is to recover
this unknown transformation. As we do not know
whether the 2 fingerprints are the same or not, we try
to find the best transformation.We discretize the set
of all possible transformations, and the matching
score is computed for each transformation. The
transformation having the maximal matching score is
believed to be the correct one.
Let’s consider a transformation,
Fig 5 Flowchart for Enrollment Algrithm using
multiple Impression
5 CONCLUSION:
As we saw USB token is a model of very secure
device, and biometric is the promising technology for
verification.These two can be combined for many
applications to enhance both the security and the
convenience.
However, typical biometric verification algorithms
that have been executed on standard PCs may not be
executed in real-time on the resource-constrained
environments such as USB token. In this paper, we
have presented a fingerprint enrollment algorithm
which can improve the accuracy and a memory-
Prof. Archana C Lomte et al, Int.J.Computer Technology & Applications,Vol 4 (2),187-191
IJCTA | Mar-Apr 2013 Available [email protected]
190
ISSN:2229-6093
efficient fingerprint verification algorithm which can
be executed in real-time on the USB token. To
improve the accuracy, we employ multiple
impressions to check false minutiae detected and true
minutiae missed. Then, to reduce the memory
requirement, we employ a small-sized accumulator
array. To compute the alignment parameters more
accurately, we perform more computations at from a
coarse-grain to a fine-grain resolution on the
accumulator array. Currently, we are porting
memory-efficient speaker and face verification
algorithms to the USB token for multi-modal
biometric authentication.
REFERENCES
1. Bhupesh Gour, T. K. Bandopadhyaya and
Sudhir Sharma, “Fingerprint Feature
Extraction using Midpoint Ridge Contour
Method and Neural Network”, International
Journal of Computer Science and Network
Security, vol. 8, no, 7, pp. 99-109, (2008).
2. Robert Hastings, “Ridge Enhancement in
Fingerprint Images Using Oriented
Diffusion”, IEEE
Computer Society on Digital Image
Computing Techniques and Applications,
pp. 245-252, (2007)
3. Vijaya Kumari and N. Suriyanarayanan,
“Performance Measure of Local Operators
in Fingerprint
Detection”, Academic Open Internet
Journal, vol. 23, pp. 1-7, (2008).
4. Ballan.M, “Directional Fingerprint
Processing”, International Conference on
Signal Processing, vol.2,pp. 1064-1067,
(1998).
5. Prabhakar, S, Jain, A.K, Jianguo Wang,
Pankanti S, Bolle, “Minutia Verification and
Classification for Fingerprint Matching”,
International Conference on Pattern
Recognition vol. 1, pp. 25-29, (2002).
6. N.Ratha, K.karu, and A.Jain; A Real Time
matching System Fingerprint
Databases,IEEE Transactions on Pattern
Analysis and Machine Intelligence,Vol.
18,No.8,August(1996)
7. S. Pan, et. al.,: A Memory-Efficient Fingerprint
Verification Algorithm using A Multi-
Resolution Accumulator Array, ETRI Journal,
Vol. 25, No. 3, June (2003)
Prof. Archana C Lomte et al, Int.J.Computer Technology & Applications,Vol 4 (2),187-191
IJCTA | Mar-Apr 2013 Available [email protected]
191
ISSN:2229-6093