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American Journal of Science and Technology

2015; 2(6): 262-269

Published online September 30, 2015 (http://www.aascit.org/journal/ajst)

ISSN: 2375-3846

Keywords Fingerprint,

Minutiae Extraction,

Minutiae Matching,

Binarization,

Smoothing,

Image Segmentation,

Matrix Equalization and

Bifurcation

Received: May 5, 2015

Revised: June 22, 2015

Accepted: June 23, 2015

Fingerprint Matching Through Minutiae Based Feature Extraction Method

Md. Shahadat Hossain1, Md. Rafiqul Islam

2

1Applied mathematics, Mathematics Discipline, Khulna University, Khulna, Bangladesh 2Mathematics Discipline, Khulna University, Khulna, Bangladesh

Email address [email protected] (Md. S. Hossain), [email protected] (Md. R. Islam)

Citation Md. Shahadat Hossain, Md. Rafiqul Islam. Fingerprint Matching Through Minutiae Based Feature

Extraction Method. American Journal of Science and Technology.

Vol. 2, No. 6, 2015, pp. 262-269.

Abstract Here minutiae based feature extraction method has been discussed which is used for

fingerprint matching. This method is mainly depending on the characteristics of minutiae

of the individuals. The minutiae are ridge endings or bifurcations on the fingerprints. Their

coordinates and direction are most distinctive features to represent the fingerprint. Most

fingerprint matching systems store only the minutiae template in the database for further

usage. The conventional methods to utilize minutiae information are treating it as a point

set and finding the matched points from different minutiae sets. This kind of

minutiae-based fingerprint recognition/matching systems consists of two steps: minutiae

extraction and minutiae matching. Image enhancement, histogram equalization, thinning,

binarization, smoothing, block direction estimation, image segmentation, ROI extraction

etc. are discussed in the minutiae extraction step. After the extraction of minutiae the false

minutiae are removed from the extraction to get the accurate result. In the minutiae

matching process, the minutiae features of a given fingerprint are compared with the

minutiae template and the matched minutiae will be found out. The final template used for

fingerprint matching is further utilized in the matching stage to enhance the system’s

performance.

1. Introduction

Fingerprint matching is a widely used biometric authentication system that is done on

the basis that every person in the world has its own fingerprint. Every fingerprint has its

universal and uniqueness characteristics and widely acceptability. We have mentioned that

each fingerprint is formed in the womb during the age 17th

week of the fetus and remain

unchanged throughout the whole life [4]. In our study we have tried to find out the

difference between the input fingerprints by concept of the uniqueness of the fingerprints.

For experiment we have taken the concept of matching any two fingerprints from the

online journal [3]. We have seen that their input fingerprint, histogram equalization for

enhancing the fingerprints, binarization, image segmentation (Block direction estimation

and ROI extraction), and false minutiae removal, minutia matcher (Alignment stage and

matching stage) are discussed. After that we have mentioned an exceptional concept that

every image give its own matrix which help to find out the difference between the input

fingerprints. In the next stage we have seen although each fingerprints have a bifurcation,

in the first fingerprint image there are two minutiae in the same direction where in the

second fingerprint image the rest two minutiae are in the different direction. In our study

we have taken the help of MATLAB programming and at one stage the AFIS (Automated

263 Md. Shahadat Hossain and Md. Rafiqul Islam: Fingerprint Matching Through Minutiae Based Feature Extraction Method

Fingerprint Identification System) software for the

implementation of the matching of two different fingerprint

images.

1.1. Minutiae Features

The major minutiae features of fingerprint ridges are ridge

ending, bifurcation, and short ridge (or dot). The ridge ending

is the point at which a ridge terminates. Bifurcations are points

at which a single ridge splits into two ridges. Short ridges (or

dots) are ridges which are significantly shorter than the

average ridge length on the fingerprint. Minutiae and patterns

are very important in the analysis of fingerprints since no two

fingers have been shown to be identical.[6]

Figure 1. Bifurcation and ridge ending.

Figure 2. Short ridge (dot).

1.2. Minutiae Extraction

Typically each detected minutiae is described by four

parameters:

where:

– are coordinates of the minutiae point,

– is minutiae direction typically obtained from local

ridge orientation,

– is type of the minutiae point (ridge ending or ridge

bifurcation),

The position of the minutiae point is at the tip of the ridge or

the valley and the direction is computed to the X axis (Fig 1).

2. Methodology

2.1. Fingerprint Matching Algorithm

The algorithm for matching two fingerprint images are

mentioned below

Figure 3. The fingerprint matching procedure.

2.2. Input Images & Histogram Equalization

Histogram equalization is widely used for contrast

enhancement in a variety of applications due to its simple

function [1]. Histeq performs histogram equalization in

MATLAB.

In histogram equalization the input pixel intensity x is

transformed to new intensity value . The transformed

function T is the product of a cumulative histogram and a scale

factor [11]. The scale factor is needed to fit the new intensity

value within the range of the intensity values, for example

Figure 4. Image-1 of a fingerprint and its histogram and equalization.

im

( ), , , ..................(i)i i i i im x y tθ=

,i ix y

iθ

it

byx T

0 255∼

American Journal of Science and Technology 2015; 2(6): 262-269 264

Figure 5. Image 2 of another fingerprint and its histogram and equalization.

2.3. Image Enhancement

Sometimes, the images have obtained does not have good

quality and so the quality of image can be upgraded by

enhancing the image, and thus the contrast between ridges and

valleys can be increased.

Figure 6. The enhanced image-1 after histogram equalization.

Figure 7. The enhanced image-2 after histogram equalization.

2.4. Edge Detection

Figure 8. Edge detected of image-1.

Figure 9. Edge detected of image-2.

The purpose of edge detection in AFIS is to significantly

reduce the amount of data found in a fingerprint image and

leave only the most important information. Edge detection

works by finding points on an image where the gray scale

value changes greatly between pixels [2].

2.5. Binarization of the Input Fingerprints

Some features of binarization is mentioned below

� Image binarization converts an image of up to 256 gray

levels to a black and white image. Frequently,

binarization is used as a pre-processor before OCR. In

fact, most OCR packages on the market work only on

bi-level (black & white) images.

� The simplest way to use image binarization is to choose

a threshold value, and classify all pixels with values

above this threshold as white, and all other pixels as

black. The problem then is how to select the correct

threshold. In many cases, finding one threshold

compatible to the entire image is very difficult, and in

many cases even impossible. Therefore, adaptive image

binarization is needed where an optimal threshold is

chosen for each image area.


Figure 10. The Binarized images of the fingerprint image-2.

Figure 11 .The Binarized images of the fingerprint image-1.

2.6. Image Segmentation

In general, only a Region of Interest (ROI) is useful to be

recognized for each fingerprint image. The image area without

effective ridges and furrows is first discarded since it only

holds background information. Then the bound of the

remaining effective area is sketched out since the minutiae in

the bound region are confusing with that spurious minutia that

is generated when the ridges are out of the sensor.

Image segmentation is classified into two major part

� Block direction estimation and

� ROI extraction

2.6.1. Block Direction Estimation

To estimate the block direction for each block of the

fingerprint image an algorithm is needed which is mentioned

below

1. At first it needs to calculate the gradient values along

both x-direction and y-direction for each

pixel of the block.

2. For each block, there needs to use following formula to

get the Least Square approximation of the block

direction.

for all the pixels in each block.

The formula mentioned above is easy to understand by

regarding gradient values along x-direction and y-direction as

cosine value and sine value. Therefore the tangent value of the

block direction can be estimated nearly the same as the way

illustrated by the following formula.

After completing the estimation of each block direction,

those blocks without having significant information on ridges

and furrows are discarded based on the following formulas:

For each block, if its certainty level E is below a threshold,

then the block is regarded as a background block.

Figure 12. Block direction of the fingerprint-1.

Figure 13. Block direction of the fingerprint-2.

2.6.2. ROI Extraction (Morphological Method)

Close (shrink images and eliminate small cavities)

Open (expands images and remove peaks introduced by

background noise)

( )xg ( )yg

( )( )2 2

2tan 2

x y

x y

g g

g gβ =

−∑∑

∑∑

2 2

2sin costan 2

cos sin

θ θθθ θ

=−

( ) ( )( )

2 2

2 2

2 x y x y

x y

g g g gE

WW g g

+ −=

+∑∑ ∑∑

∑∑


Figure 14. Image of the fingerprint 1.


2.7. Thinning of the Input Fingerprint

Thinning is defined as a procedure to transform a digital

binary pattern to a connected skeleton of unit width. After

thinning the minutiae of the original fingerprint become more

visible in the thinned fingerprint image. And this thinned

fingerprint image is used to match with the other fingerprint

images so that the variation of features among the

fingerprints can be detected easily.

Figure 16. The thinned image-1.

Figure 17. The thinned image- 2.

2.8. Termination and Bifurcation

Since various data acquisition conditions such as

impression pressure can easily change one type of minutia into

the other, most researchers adopt the unification

representation for both termination and bifurcation. So each

minutia is completely characterized by the following

parameters at last: 1) x-coordinate, 2) y-coordinate, and 3)

orientation. The orientation calculation for a bifurcation needs

to be specially considered. All three ridges deriving from the

bifurcation point have their own direction represents the

bifurcation orientation using a technique proposed in [7]

simply chooses the minimum angle among the three

anticlockwise orientations starting from the x-axis. Both

methods cast the other two directions away, so some

information loses. The termination and bifurcation of both

fingerprints are mentioned below.

Figure 18. Termination of 1.


Figure 19. Termination of 2.

Figure 20. Bifurcation of image-1.

Figure 21. Bifurcation of image -2.

Since various data acquisition conditions such as

impression pressure can easily change one type of minutia into

the other, most researchers adopt the unification

representation for both termination and bifurcation.

2.9. Removal of the False Minutiae

The false minutiae can affect the result of fingerprint

matching. These types of minutia are to be removed.

Figure 22. Image-1 after removal of false minutiae.

Figure 23. Image-2 after removal of false minutiae.

2.10. Minutiae in the ROI of the Fingerprints

In this stage marked minutiae are highlighted with different

colors so that it can be found out the difference between any

two fingerprints.

Figure 24. Image of fingerprint 1.


Figure 25. Image of fingerprint 2.

2.11. Unique Minutiae Sorter

The matching of the fingerprint includes some procedures

which are mentioned through the following figures



2.12. Minutiae Matching

Feature-based(Minutiae-based) Matching: Typical

fingerprint recognition methods employ feature-based

matching, where minutiae (i.e., ridge ending and ridge

bifurcation) are extracted from the registered fingerprint

image and the input fingerprint image, and the number of

corresponding minutiae pairings between the two images is

used to recognize a valid fingerprint image. Alternatively, Jain

et al. [8] used a string matching technique while Isenor and

Zaky [9] propose a graph-based fingerprint matching

algorithm. In are [10] describes a fingerprint verification

algorithm based on a bipartite graph construction between

model and query fingerprint feature clusters. The minutiae

matching problem has been generally addressed as a point

pattern matching problem which has been extensively studied

yielding families of approaches known as relaxation methods,

algebraic and operational research solutions, tree-pruning

approaches, energy- minimization methods, Hough transform,

etc.

3. Experimental Results and Discussions

From the tree diagram mentioned above it is notable that the

tree diagram of the two given fingerprints are totally different

from each other. So the given two fingerprints are also

different from each other.

Figure 28. Direction of minutia of image 1.

Figure 29. Direction of minutia of image 2.

From the above figure-28 and figure-29 it is found that in


each fingerprint there is one bifurcation. But in the first image

the other two minutiae are in the same direction while in the

second one image the other two minutiae are in the different

direction. So it can be reached to the decision that the two

fingerprints are totally different.

4. Conclusions

Our vision has combined a method to build a minutia

extractor and a minutia matcher. The combination of multiple

methods comes from a wide investigation into different

research papers. Also some significant changes like

segmentation using Morphological operations, minutia

marking with special considering the triple branch counting,

minutia unification by decomposing a branch into three

terminations and matching in the unified x-y coordinate

system after a two-step transformation are used in our project,

which are not reported in other literatures we referred to. The

processes named thinning, binarization, smoothing , block

direction estimation, absolute contrast, image segmentation,

ROI extraction etc. are done by using the update software

“SourceAFIS-1.7.0” where AFIS means Automated

Fingerprint Identification System .Also a program coding

with MATLAB going through all the stages of the fingerprint

matching is built. It is helpful to understand the procedures of

fingerprint matching. And demonstrate the key issues of

fingerprint matching. At last by the Matlab code for

equalizing two matrices of the two input fingerprints it is

found that the result is zero which means that the two

fingerprints taken as input are absolutely different from each

other.

Acknowledgement

I would like to give thank Professor Dr. Md. Rafiqul Islam

Sir for his time to time, very much needed, valuable guidance.

I am also grateful to him who encouraged me to make this

effort a success.

References

[1] Bassiou, N. and Kotropoulos, C., "Color image histogram equalization by absolute discounting back-off," Computer Vision and Image Understanding, 107(1-2):108-122,

[2] Boldischar, M. and Moua, C. P., “Edge Detection and Feature Extraction in Automated Fingerprint Identification Systems”

[3] Applications of fingerprint matching are available at http://www.answers.com/Q/What_are_the_practical_applications_of_fingerprinting

[4] Lasting impression of fingerprint is available at http://www.livescience.com/ 30- lasting-impression-fingerprints-created.html

[5] Image binarization is available at https://www.research.ibm.com/haifa/projects/image/glt/binar.html

[6] Thornton, John (May 9, 2000). Latent Fingerprints, Setting Standards In The Comparison and Identification. 84th Annual Training Conference of the California State Division of IAI. Retrieved 30 August 2010.

[7] Applications of fingerprint matching are available at http://www.answers.com/Q/What_are_the_practical_applications_of_fingerprinting

[8] A. K. Jain, L. Hong, R. M. Bolle, "On-line fingerprint verification", IEEE Trans, on Pattern Analysis and Machine Intelligence, Vol. 19, No 4, pp.302-313, 1997.

[9] D. K. Isenor, S. G. Zaky, "Fingerprint identification using graph matching", Pattern Recognition, Vol. 19, No 2, pp. 113-122, 1986.

[10] K.-C. Fan, C.-W. Liu, Y.-K. Wang, "A fuzzy bipartite weighted graph matching approach to fingerprint verification", In Proc. of the IEEE International Conf. on Systems, Man and Cybernetics, pp. 729-733, Oct 1998.

[11] Formula of histogram equalization is available at http://www.songho.ca/dsp/histogram/histogram.html

Biography

Md. Shahadat Hossain: I am Md. Shahadat Hossain. I was born in 1st January, 1992 in the village named Khuriakhali of Sharonkhola Upazilla under Bagerhat district. I have completed my bachelor degree in mathematics from Khulna university, Bangladesh in the year 2014. Now I am a student of M.Sc. of the same institution in applied mathematics. In my free time I am engaged in research related to mathematics.

Md. Rafiqul Islam: I am Dr. Md. Rafiqul Islam. I was born in 1966 in the district of Satkhira, Bangladesh. I have taken my bachelor degree in mathematics from University of Rajshahi. I have taken my M.Sc. degree in applied mathematics from University of Saudi Arabia. I have completed my Ph.D. degree in applied mathematics from University of Rajshahi. At present I am a professor of mathematics discipline, Khulna University, Bangladesh.

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