15-11-2012, 06:25 PM
Fingerprint Image Enhancement: Algorithm and Performance Evaluation
ENHANCEMENT ALGORITHM.pdf (Size: 3.86 MB / Downloads: 70)
INTRODUCTION
FINGERPRINT identification is one of the most important
biometric technologies which has drawn a substantial
amount of attention recently [12], [15]. A fingerprint
is the pattern of ridges and valleys (also called furrows
in the fingerprint literature [14]) 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 [12],
[14]. A total of 150 different local ridge characteristics
(islands, short ridges, enclosure, etc.) have been identified
[14]. 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 and
2) ridge bifurcation.
A ridge ending is defined as the point where a ridge ends
abruptly. A ridge bifurcation is defined as the point where
a ridge forks or diverges into branch ridges. A good quality
fingerprint typically contains about 40–100 minutiae.
Examples of minutiae are shown in Fig. 1.
Automatic fingerprint matching depends on the comparison
of these local ridge characteristics and their relationships
to make a personal identification [12]. A critical
step in fingerprint matching is to automatically and reliably
extract minutiae from the input fingerprint images, which
is a difficult task.
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 our fingerprint image enhancement
algorithm, a list of notations and some basic definitions
are given below.
Orientation Image
The orientation image represents an intrinsic property of
the fingerprint images and defines invariant coordinates for
ridges and valleys in a local neighborhood. By viewing a
fingerprint image as an oriented texture, a number of
methods have been proposed to estimate the orientation
field of fingerprint images [11], [17], [10], [1]. We have developed
a least mean square orientation estimation algorithm.
Given a normalized image, *, the main steps of the
algorithm are as follows:
Ridge Frequency Image
In a local neighborhood where no minutiae and singular
points appear, the gray levels along ridges and valleys can
be modeled as a sinusoidal-shaped wave along a direction
normal to the local ridge orientation (see Fig. 8). Therefore,
local ridge frequency is another intrinsic property of a fingerprint
image. Let * be the normalized image and 2 be
the orientation image, then the steps involved in local ridge
frequency estimation are as follows: