05-07-2012, 02:33 PM
Voting System with Fingerprint Enhancement Algorithm
[attachment=26858]
INTRODUCTION
Fingerprint identification is one of the most
important biometric technologies which has drawn a
substantial amount of attention recently. A fingerprint
is the pattern of ridges and valleys (also called
furrows in the fingerprint literature) on the surface
of a fingertip. Each individual has unique fingerprints.
The uniqueness of a fingerprint is exclusively
determined by the local ridge characteristics and their
relationships. A total of 150 different local ridge
characteristics (islands, short ridges, enclosure, etc.)
have been identified. These local ridge characteristics
are not evenly distributed. Most of them depend heavily
on the impression conditions and quality of
fingerprints and are rarely observed in fingerprints. The
two most prominent local ridge characteristics, called
minutiae, are
1. Ridge ending.
2. Ridge bifurcation.
Examples of minutiae (a) A minutiae can be
characterized by its position and its orientation. (b)
Minutiae overlaid on a fingerprint Image.
FINGERPRINT ENHANCEMENT
A fingerprint image enhancement algorithm
receives an input fingerprint image, applies a set of
intermediate steps on the input image, and finally
outputs the enhanced image. In order to introduce
the fingerprint image enhancement algorithm, a list
Published in International Journal of Advanced Engineering & Application, June 2011 Issue 111
of notations and some basic definitions are given
below.
Algorithm
The flowchart of the fingerprint enhancement
algorithm is shown in Fig. The main steps of the
algorithm include:
1. Segmentation: This is the process of separating
the foreground regions in the input image from the
background regions.
2. Normalization: An input fingerprint image is
normalized so that it has a prespecified mean and
variance.
3. Local orientation estimation: The orientation image
is estimated from the normalized input fingerprint
image.
Minutiae-based matching
(Analyzing the local structure) and
(Analyzing the global structures).
Currently the computer aided fingerprint
recognition is using the minutiae-based matching.
Minutiae points are local ridge characteristics and
appear as either a ridge ending or a ridge bifurcation.
The uniqueness of a fingerprint can be
determined by the pattern of the ridges and the
valleys a fingerprint‘s made of. A complete
fingerprint consists of about 100 minutiae points in
average. The measured fingerprint area consists in
average of about 30-60 minutiae points depending on the
finger and on the sensor area. These minutiae points are
represented by a cloud of dots in a coordination system.
They are stored together with the angle of the
tangent of a local minutiae point in a fingerprint-code
or directly in a reference template.
ADVANTAGES OF USING FINGERPRINT
Prevents unauthorized use or access
Adds a higher level of security to an identification
process
Eliminates the burden and bulk of carrying ID
cards or remembering Pins
Heightens overall confidence of business processes
dependent on personal identification.
CONCLUSION
We believe that this paper turned out to be a
success of its original mission. We set out to
develop a networked biometric authentication system
and this is exactly what we achieved. A user is able to
be registered and stored into a database, they can
then enter their id and scan their fingerprint which is
then authenticated and the result is sent back locally
to the microcontroller.
[attachment=26858]
INTRODUCTION
Fingerprint identification is one of the most
important biometric technologies which has drawn a
substantial amount of attention recently. A fingerprint
is the pattern of ridges and valleys (also called
furrows in the fingerprint literature) on the surface
of a fingertip. Each individual has unique fingerprints.
The uniqueness of a fingerprint is exclusively
determined by the local ridge characteristics and their
relationships. A total of 150 different local ridge
characteristics (islands, short ridges, enclosure, etc.)
have been identified. These local ridge characteristics
are not evenly distributed. Most of them depend heavily
on the impression conditions and quality of
fingerprints and are rarely observed in fingerprints. The
two most prominent local ridge characteristics, called
minutiae, are
1. Ridge ending.
2. Ridge bifurcation.
Examples of minutiae (a) A minutiae can be
characterized by its position and its orientation. (b)
Minutiae overlaid on a fingerprint Image.
FINGERPRINT ENHANCEMENT
A fingerprint image enhancement algorithm
receives an input fingerprint image, applies a set of
intermediate steps on the input image, and finally
outputs the enhanced image. In order to introduce
the fingerprint image enhancement algorithm, a list
Published in International Journal of Advanced Engineering & Application, June 2011 Issue 111
of notations and some basic definitions are given
below.
Algorithm
The flowchart of the fingerprint enhancement
algorithm is shown in Fig. The main steps of the
algorithm include:
1. Segmentation: This is the process of separating
the foreground regions in the input image from the
background regions.
2. Normalization: An input fingerprint image is
normalized so that it has a prespecified mean and
variance.
3. Local orientation estimation: The orientation image
is estimated from the normalized input fingerprint
image.
Minutiae-based matching
(Analyzing the local structure) and
(Analyzing the global structures).
Currently the computer aided fingerprint
recognition is using the minutiae-based matching.
Minutiae points are local ridge characteristics and
appear as either a ridge ending or a ridge bifurcation.
The uniqueness of a fingerprint can be
determined by the pattern of the ridges and the
valleys a fingerprint‘s made of. A complete
fingerprint consists of about 100 minutiae points in
average. The measured fingerprint area consists in
average of about 30-60 minutiae points depending on the
finger and on the sensor area. These minutiae points are
represented by a cloud of dots in a coordination system.
They are stored together with the angle of the
tangent of a local minutiae point in a fingerprint-code
or directly in a reference template.
ADVANTAGES OF USING FINGERPRINT
Prevents unauthorized use or access
Adds a higher level of security to an identification
process
Eliminates the burden and bulk of carrying ID
cards or remembering Pins
Heightens overall confidence of business processes
dependent on personal identification.
CONCLUSION
We believe that this paper turned out to be a
success of its original mission. We set out to
develop a networked biometric authentication system
and this is exactly what we achieved. A user is able to
be registered and stored into a database, they can
then enter their id and scan their fingerprint which is
then authenticated and the result is sent back locally
to the microcontroller.