03-05-2013, 03:50 PM
Fingerprint Recognition Using Minutia Matching
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Abstract
Fingerprints are the most widely used biometric
feature for identification and verification in the field of
biometrics. This paper presents the types of fingerprint,
implementation of a minutiae based approach to fingerprint
identification and verification. In this research paper we have
discussed a minutiae based matching technique. This approach
has been intensively studied; this technique is the backbone of
the current available fingerprint recognition products.
INTRODUCTION
Fingerprint recognition is one of the reliable, most
important and useful biometric technique used for person
identification and verification. Fingerprints are one of the
maximum used biometric technologies for considered legal
proofs of evidence in all over the world [1]. A fingerprint is an
impression of the friction ridges found on the inner surface of
a finger. A fingerprint is comprised of ridges and valleys, the
ridges are the dark area of the fingerprint and valleys are the
white area that exists between the ridges.
FINGERPRINT PATTERN TYPES
Fingerprint patterns are divided into three main groups
consisting of Arches, Loops and Whorls. Approximately
5% of all fingerprints are Arches, 30% are Whorls and 65%
are Loops.
Loop Patterns:
In a Loop pattern, the ridges will flow in one side, re-curve,
(loop around) touch or pass through an imaginary line drawn
from the delta to the core, and exit the pattern on the same
side from which it entered. The loop pattern consists of one or
more re-curving ridges and one delta.
There are two types of loop patterns:
1. Ulnar loop
2. Radial loop.
Difference between ulnar and radial loop are, if the ridges
flow in from the little finger side, this would be an ulnar loop
and if the ridges flow in from the thumb side this would be a
radial loop.
FINGERPRINT RECOGNITION
Fingerprint recognition (also known as Dactyloscopy) is the
process of comparing known fingerprint against another or
template fingerprint to determine if the impressions are from
the same finger or not. It includes two sub-domains: one is
fingerprint verification and the other is fingerprint
identification [3].
Verification specify an individual fingerprint by comparing
only one fingerprint template stored in the database, while
identification specify comparing all the fingerprints stored in
the database. Verification is one to one matching and
identification is one to N (number of fingerprint templates
available in database) matching. Verification is a fast process
as compared to identification.
FINGERPRINT MATCHING TECHNIQUES
There are many Fingerprint Matching Techniques. Most
widely used matching techniques are these:
• Correlation-based matching: In correlation based
matching the two fingerprint images are matched through
corresponding pixels which is computed for different
alignments and rotations. The main disadvantage of
correlation based matching is its computational complexity.
• Minutiae-based matching: This is the most popular and
widely used technique, for fingerprint comparison. In
minutiae-based techniques first of all we find minutiae points
on which we have to do mapping. However, there are some
difficulties when using this approach. It is difficult to identify
the minutiae points accurately when the fingerprint is of low
quality.
• Pattern-based (or image-based) matching: Pattern based
technique compare the basic fingerprint patterns (arch, whorl,
and loop) between a previously stored template and a
candidate fingerprint. This requires that the images be aligned
in the same orientation. In a pattern-based algorithm, the
template contains the type, size, and orientation of patterns
within the aligned fingerprint image. The candidate fingerprint
image is graphically compared with the template to determine
the degree to which they match [3].
CONCLUSION
In this paper we have presented types of fingerprint
patterns and matching techniques. Fingerprint recognition
using minutiae matching algorithm has been used for
matching the minutiae points.