International Journal of Engineering Trends and Technology (IJETT) – Volume 21 Number 7 – March 2015
Fingerprint Recognition Based on Texture
Feature
Miss. Sindhu S Kale, Prof. Sachin B. Honrao
ME Student, ETC Department, Dr. BAMU Aurangabad, Maharashtra, India
Abstract— Fingerprints have been used for biometric
identification and employed in forensic science to support
criminal investigations, and in biometric systems. In Automatic
Fingerprint Recognition Classification and Matching are two key
issues. This paper provides work on the enhancement of
fingerprint images, and the subsequent extraction of minutiae
and provides reliable techniques for fingerprint image
enhancement and minutiae extraction
Core Point: is the point with the most variant changes in the
direction of the line.
Delta Point: the point where the ridges spread into three
directions.
Crossover Point: It is a short ridge that runs between two
parallel ridges.
Island Point: It is a line type which stands alone [1].
Keywords— Fingerprints, extraction, Recognition Classification,
Matching.
II. LITERATURE REVIEW
Prabhakar et al. [5] describe a classification technique for
fingerprint. In these method pictures of fingerprint divided
into 5 categories. First one is whorl, right loop, left loop, arch,
and tented arch as shown in the figure 2. This method
completely unique illustration (Finger Code) and relies on a 2
stage classifier to create a classification. The two-stage
classifier uses a k-nearest neighbor classifier in its 1st stage
and a group of neural network classifiers in its second stage to
classify a feature vector into one amongst the 5 fingerprint
categories.
I. INTRODUCTION
Fingerprint verification is one of the most reliable personal
identification methods and it plays a very important role in
forensic applications like criminal investigations, terrorist
identification and National security issues. In an increasingly
digitized world the reliable personal authentication has
become an important human computer interface activity.
National security, e-commerce and access to computer
networks are now very common where establishing a person’s
identity has become vital. Existing security measures rely on
knowledge-based approaches like passwords or token-based
approaches such as swipe cards and passports to control
access to physical and virtual spaces, but these methods are
not very secure [7]. Fingerprint recognition is mostly used for
person Identification and verification. 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 [1]. The ridges create a point, which is
known by different names shown in the Figure1.
Fig.1: Different points from Fingerprint [1].
Ending Point: The point where a ridges break.
Bifurcation Point: The point where ridges divided into two.
ISSN: 2231-5381
Fig. 2: arch, right loop, whole, tented arch respectively [6].
Mukesh Kumar Thakur et al. [8] propose a wireless
fingerprint security system based on Zigbee technology to
overcome above dearth. In this paper the methodology based
on the taking fingerprint of a user, which can be taken with
the help of a fingerprint sensor module and matching it with
the database details corresponding to the user fingerprint and
displays it on the computer screen. From the results it is
clearly obtained that the proposed approach provides very
high accuracy. Thus the approach is very much secured. Thus
the system provides a substantive improvement over
recognition of a person based purely on biometric feature.
III. METHODOLOGY
This section describes the methods for constructing a series
of image enhancement techniques for fingerprint images. The
algorithm I have implemented is built on the techniques
developed by Hong et al. This algorithm consists of four main
stages:
normalization,
orientation estimation,
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International Journal of Engineering Trends and Technology (IJETT) – Volume 21 Number 7 – March 2015
ridge frequency estimation, and
Gabor filtering.
In addition to these four stages, this paper implemented three
additional stages that include:
segmentation,
binarisation, and
thinning.
The first step of the fingerprint enhancement algorithm is
image segmentation. Segmentation is the process of separating
the foreground regions in the image from the background
regions. In this stage, Firstly, the image is divided into blocks
and the grey-scale variance is calculated for each block in the
image. If the variance is less than the global threshold, then
the block is assigned to be a background region; otherwise, it
is assigned to be part of the foreground.
The grey-level variance for a block of size W X W is defined
as:
Where, V (k) is the variance for block k, I(i; j) is the grey-level
value at pixel (i; j), and M(k) is the mean grey-level value for
the block k.
In next stage i.e. Binarisation is the process that converts a
grey-level image into a binary image.
This improves the contrast between the ridges and valleys in a
fingerprint image, and consequently facilitates the extraction
of minutiae. The binarisation process involves examining the
grey-level value of each pixel in the enhanced image, and, if
the value is greater than the global threshold, then the pixel
value is set to a binary value one; otherwise, it is set to zero.
The outcome is a binary image containing two levels of
information, the foreground ridges and the background valley
the average of the grey-level values within an image window
at each pixel, and if the average is greater than the threshold,
then the pixel value is set to a binary value of one; otherwise,
it is set to zero. The grey-level image is converted to a binary
image, as there are only two levels of interest, the foreground
ridges and the background valleys.
The final image enhancement step typically performed prior to
minutiae extraction is thinning. Thinning is a morphological
operation that successively erodes away the foreground pixels
until they are one pixel wide. This algorithm is accessible in
MATLAB via the `thin' operation under the’ bwmorph’
function. Each sub-iteration begins by examining the
neighborhood of each pixel in the binary image, and based on
a particular set of pixel-deletion criteria, it checks whether the
pixel can be deleted or not. These sub-iterations continue until
no more pixels can be deleted.
IV. EXPERIMENTAL RESULT
Table1. Summarizes the results from experiments
conducted on a sample of 30 fingerprint images. The sample
size figures shown in the table represent the total number of
distances calculated between a minutiae point and its nearest
neighbour. Additionally, the histogram plots for each of the
data types from the table are shown in Figure 3. It can be seen
ISSN: 2231-5381
that the results exhibit large standard deviation values. This
suggests that the shortest distance values vary greatly for both
types of minutiae, and that the configuration of a group of
neighbouring minutiae is not evenly distributed throughout a
fingerprint image. In addition, further work can be done to fit
a probability distribution model to this set of observed data,
which can provide further insight into the statistical nature of
distances between neighbouring minutiae points.
TABLE I
THE RESULTS FROM EXPERIMENTS CONDUCTED ON A
SAMPLE OF 30 WITH EXISTING METHODS
Data Set
Number
Source
Data
of
Sample
Size
Mean
Minutiae
Density
(per
mm2)
Standard
Deviation
1
Dankmeijer
et al. [2]
1000
0.1900
0.0069
2
Stoney et al.
[3]
412
0.2230
0.0045
3
Kingston [4]
100
0.2460
0.0084
4
Current
Study
30
0.2040
0.0285
Fig.3: The estimated orientation for well-defined synthetic images.
V. CONCLUSION
The primary focuses of the work on the enhancement of
fingerprint images, and the subsequent extraction of minutiae
and provides reliable techniques for fingerprint image
enhancement and minutiae extraction. These techniques can
then be used to facilitate the further study of the statistics of
fingerprints. In addition, these techniques can be also
employed in other fingerprinting applications such as
fingerprint matching and classification.
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International Journal of Engineering Trends and Technology (IJETT) – Volume 21 Number 7 – March 2015
[5]
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[6]
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