16-09-2016, 12:25 PM
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ABSTRACT
From security point of view, fingerprints and
biological data in general constitute sensitive
information that has to be protected. Towards this
direction, the method discussed in this paper isolates a
very small fraction of the user‟s biological data, and
only this fraction is stored for future reference. This
can also improve the overall efficiency and bandwidth
effectiveness of the system. The novel application of
computational geometry algorithms in the fingerprint
segmentation stage showed that the extracted feature
(characteristic polygon) may be used as a secure and
accurate method for fingerprint-based verification over
the Internet. On the other hand the proposed method
promisingly allows very small false acceptance and
false rejection rates, as it is based on specific
segmentation.
Biometrics technology allows determination and
verification of ones identity through physical
characteristics. To put it simply, it turns the human
body in to his or her password. In this paper two
algorithms have been proposed by taking biometric
techniques to authenticate an ATM account holder,
enabling a secure ATM by image processing.
INTRODUCTION
Biometry, as the science of studying mathematical or
statistical properties in physiological and behavioral
human characteristics, is widely used in forensic and
no forensic applications in security field such as
remote computer access, access control to physical
sites, transaction authorization etc. In this paper the
problem of fingerprint verification via the Internet is
investigated.
Specifically, the method that is used for the above
purpose is based on a traditional finger scanning
technique, involving the analysis of small unique
marks of the finger image known as minutiae.
Minutiae points are the ridge endings or bifurcations
branches of the finger image. A typical live-scan fingerprint will contain 30-40 minutiae. Other systems
analyze tiny sweat pores on the finger that, in the same
way as minutiae, are uniquely positioned. Finger
scanning is not immune to environmental disturbance.
As the image is captured when the finger is touching
the scanner device it is possible that dirt, condition of
the skin, pressure and alignment or rotation of the
finger all affect the quality of the fingerprint.
Furthermore, such methods may be subject to attacks
by hackers when biometric features are transferred via
Internet.
In this paper a method is developed, which addresses
the problem of the rotation and alignment of the finger
position. The proposed method is based on
computational geometry algorithms. The advantages of
this method are based on a novel processing method
using specific extracted features, which may be
characterized as unique to each person. These features
depend exclusively on the pixels brightness degree for
the fingerprint image, in contrast to traditional
methods where features are extracted using techniques
such as edge, minutiae points and ridges detection.
Specifically, these features express a specific
geometric area (convex layer) in which the dominant
brightness value of the fingerprint ranges. What makes
biometrics useful for many applications is that they
can be stored in a database.
2. METHOD
In brief, the proposed method is described in the
following steps:
1. Pre-processing stage: The input image is made
suitable for further processing by image enhancement
techniques using Matlab.
2. Processing stag:. The data, which comes from step
1, is submitted to specific segmentation (data sets)
using computational geometry algorithms
implemented via Matlab. Thus, onion layers (convex polygons) are created from these data sets, see figure 1.
3. Meta-processing stage (during registration only):
The smallest layer (convex polygon) of the constructed
onion layers is isolated from the fingerprint in vector
form, see figure 2. For the rest of this paper, this will
be referred to as the referenced polygon. This is
supposed to be stored in a reference database, for
subsequent verification.
4. Verification stage: This stage consists of the
following steps:
i. An unknown fingerprint is submitted to the
proposed processing method (Steps 1 and 2),
and a new set of onion layers is constructed.
ii. The referenced polygon that has been
extracted during registration stage is
intersected with the onion layers and the
system decides whether the tested vector
identifies the onion layers correctly or not.
ABSTRACT
From security point of view, fingerprints and
biological data in general constitute sensitive
information that has to be protected. Towards this
direction, the method discussed in this paper isolates a
very small fraction of the user‟s biological data, and
only this fraction is stored for future reference. This
can also improve the overall efficiency and bandwidth
effectiveness of the system. The novel application of
computational geometry algorithms in the fingerprint
segmentation stage showed that the extracted feature
(characteristic polygon) may be used as a secure and
accurate method for fingerprint-based verification over
the Internet. On the other hand the proposed method
promisingly allows very small false acceptance and
false rejection rates, as it is based on specific
segmentation.
Biometrics technology allows determination and
verification of ones identity through physical
characteristics. To put it simply, it turns the human
body in to his or her password. In this paper two
algorithms have been proposed by taking biometric
techniques to authenticate an ATM account holder,
enabling a secure ATM by image processing.
INTRODUCTION
Biometry, as the science of studying mathematical or
statistical properties in physiological and behavioral
human characteristics, is widely used in forensic and
no forensic applications in security field such as
remote computer access, access control to physical
sites, transaction authorization etc. In this paper the
problem of fingerprint verification via the Internet is
investigated.
Specifically, the method that is used for the above
purpose is based on a traditional finger scanning
technique, involving the analysis of small unique
marks of the finger image known as minutiae.
Minutiae points are the ridge endings or bifurcations
branches of the finger image. A typical live-scan fingerprint will contain 30-40 minutiae. Other systems
analyze tiny sweat pores on the finger that, in the same
way as minutiae, are uniquely positioned. Finger
scanning is not immune to environmental disturbance.
As the image is captured when the finger is touching
the scanner device it is possible that dirt, condition of
the skin, pressure and alignment or rotation of the
finger all affect the quality of the fingerprint.
Furthermore, such methods may be subject to attacks
by hackers when biometric features are transferred via
Internet.
In this paper a method is developed, which addresses
the problem of the rotation and alignment of the finger
position. The proposed method is based on
computational geometry algorithms. The advantages of
this method are based on a novel processing method
using specific extracted features, which may be
characterized as unique to each person. These features
depend exclusively on the pixels brightness degree for
the fingerprint image, in contrast to traditional
methods where features are extracted using techniques
such as edge, minutiae points and ridges detection.
Specifically, these features express a specific
geometric area (convex layer) in which the dominant
brightness value of the fingerprint ranges. What makes
biometrics useful for many applications is that they
can be stored in a database.
2. METHOD
In brief, the proposed method is described in the
following steps:
1. Pre-processing stage: The input image is made
suitable for further processing by image enhancement
techniques using Matlab.
2. Processing stag:. The data, which comes from step
1, is submitted to specific segmentation (data sets)
using computational geometry algorithms
implemented via Matlab. Thus, onion layers (convex polygons) are created from these data sets, see figure 1.
3. Meta-processing stage (during registration only):
The smallest layer (convex polygon) of the constructed
onion layers is isolated from the fingerprint in vector
form, see figure 2. For the rest of this paper, this will
be referred to as the referenced polygon. This is
supposed to be stored in a reference database, for
subsequent verification.
4. Verification stage: This stage consists of the
following steps:
i. An unknown fingerprint is submitted to the
proposed processing method (Steps 1 and 2),
and a new set of onion layers is constructed.
ii. The referenced polygon that has been
extracted during registration stage is
intersected with the onion layers and the
system decides whether the tested vector
identifies the onion layers correctly or not.