biometric fingerprint authentication by minutiae ... · fig 5 flowchart for enrollment algrithm...

Biometric Fingerprint Authentication by Minutiae Extraction

Using USB Token System

Prof. Archana C.Lomte1(Author1), Dr.S.B.Nikam

2

1JSPM’s Bhivrabai Sawant Institute of Technology & Research(W), Department of Computer Engg

2Government Polytechnique,Department of Computer Engg.

ABSTRACT: Biometrics is automated methods of

identifying a person or verifying the identity of a

person based on a physiological or behavioral

characteristic. Many body parts, personal

characteristics have been suggested and used for

biometric security system. Which include fingerprint,

hands, face, eyes, voice etc. so all physiological

characteristics are the permanent identification of

every person by birth and it never change throughout

the life. There are number of Biometric techniques

available to fulfill the different kinds demand in the

market. Every method consists number of advantages

compared to the others. But as if now there is no such

method able to completely satisfy the current security

system. so because of this research is continuously

going on to find out the newer methods that will

provide a higher security.In this paper, the different

methods of biometric authentication is presented.

Keywords: Minutiae Extraction, USB Token,

Biometric Technique.

1 INTRODUCTION: In today’s highly

computerized advancing digital world the security is

becoming most important topic for authentication.

Existing authentication measures rely on information

based on approaches like passwords, PIN numbers or

token based approaches like passport, swipe cards.

These methods are not very secure. These methods

can be easily accessed through number of ways by

stealing or by shearing because of this it is quite

impossible to differentiate between authorized user

and the person having access to the tokens or

passwords.

1.1 WHAT IS BIOMETRICS?

It is the technology of analyzing biological data. It

measures and analyzes biological characteristics such

as fingerprint,iris,hands,face,ear etc for

authentication purpose.

Biometric characteristics of human can be divided in

to three types.1) Physiological

2)Behavioral3)Chemical/Biological

Fig.1 Classification of Biometric Characteristics

1.2 NEED OF BIOMETRICS:

There are two major ways of Biometrics:

Identification and Verification.

Identification is determining that who a person is.

In this identification taking the measured

characteristics and trying to match in a database

containing records of that particular person and that

characteristic.

Verification is determining if he is the same person.

In this it involves taking the measured characteristics

Prof. Archana C Lomte et al, Int.J.Computer Technology & Applications,Vol 4 (2),187-191

IJCTA | Mar-Apr 2013 Available [email protected]

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ISSN:2229-6093

and compare with the previously stored

characteristics for that person.

2 VARIOUS EXISTING METHOD USED FOR

FINGERPRINT MATCHING

1) Here a method for extraction of minutiae from

fingerprint images using midpoint ridge contour

representation. In this midpoint ridge contour method

first step is segmentation. Segmentation is to

separate foreground from background of fingerprint

image. Image of 64x64 region is extracted from

fingerprint. So the grayscale intensities in that

regions are normalized to a constant mean and

variance to remove the effect of sensor noise and

grayscale variations due to finger pressure

differences. After the completion of normalization

difference between two of ridges are enhanced by

filtering 64x64 normalized windows by appropriate

Gabor filter. That processed fingerprint image is

scanned from top to bottom and left to right and

transition from whit(background) to black

(foreground) are detected[1].

2) In second method Robert Hastings developed a

enhancing the ridge pattern by using orientation

transmission process by a variation of different

values measured in different direction and

transmission to smooth the image in the direction to

parallel to the ridge flow. So in this process the image

intensity varies easily as one pass through the ridges

or valleys by removing most of the small

irregularities and breaks but with the uniqness of the

individual ridges and valleys[2].

3)In this method V.V Kumari and N.Suryanarayanan

proposed for execution measure of local operators in

fingerprint by observing the edges of fingerprint

images using five local operators namely

Sobel,Roberts,Prewitt,Canny and LoG.So after

processing the edge detected image is further

segmented to extract individual segment from the

image[3].

4) The Directional Fingerprint Processing method

was developed by Ballan M. So the directional

fingerprint processing using fingerprint smoothing,

categorization and identification based on the

singular points (delta and core points) received

from the directional histogram of a fingerprint.

The process includes directional image formation,

directional image block representation, singular point

detection and decision. So this method gives

matching decision vectors with minimum errors,

because of this method is simple and fast[4].

5) Filter based representation technique for

fingerprint identification which is developed by

Prabhakar S. and Jain A.K. . In this technique both

local and global characteristics in a fingerprint are

take in to consideration to make identification.So

each fingerprint image is filtered in a number of

directions. The matching stage computes the

Euclidian distance between the template finger code

and the input finger code. At last we are getting good

matching with high accuracy[5].

6) Prposed fingerprint identification technique

developed by G. Sambasiva Rao uses grey level

watershed method which find out the ridges present

on a fingerprint image by directly scanned

fingerprints.

7) Eric Kukula developed a method to explore the

result of five different strength levels on fingerprint

matching performance. In this process the results

expose a significant differences in minutiae counts

and image quality score based on the strength level

and each sensor technology.

8) Luping Ji and Zhang Yi propsed a method for

judging four directions orientation field by

considering four steps 1) preprocessing fingerprint,2)

determining the primary ridge of fingerprint block iii)

estimating block direction

9) M.R. Girgisa developed a method to explain a

fingerprint matching based on lines extraction and

graph matching principles which consists of a genetic

M. R. Girgisa et al., proposed a method to describe a

fingerprint matching based on lines extraction and

graph matching principles by adopting a hybrid

scheme which consists of a genetic algorithm phase

and a local search phase. Experimental results

demonstrate the robustness of algorithm.



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2.1 PROPOSED SYSTEM

In the proposed system fingerprint based

authentication for USB Token systems. This

fingerprint authentication system can divide into two

phases of enrollment and verification. The

verification algorithm consists of three parts: Image

Pre-Processing, Minutiae Extraction and Minutiae

Matching. First two steps Pre-processing and

Extraction cannot be executed on the resource-

constrained environments such as USB token.

Fig.2 Fingerprint Based Match On Token

The Minutiae Matching step(alignment and matching

stages) in this we have to compute the similarity

between the enrolled minutiae and the input minutiae

which is executed on the Match-on-Token, whereas

other steps Image Pre-Processing and Minutiae

Extraction are executed on the host PC.

Fig 3 Architecture of the USB Token

In the Table 1 the system specification of the USB

token is developed. The USB token employs

206MHz CPU, 16MBytes Flash memory, and

1MBytes RAM. The size of the USB token is

7cm2cm1cm. Fig. 2. shows the architecture of the

USB token. The processing core of the Intel SA-1110

processor includes the USB end-point interface to

communicate between the host PC and the token.

Also, the USB token employs the serial port and

JATG interface to use in debugging.

3 FINGERPRINT ENROLLMENTS FOR THE

USB TOKEN:

The minutiae-based fingerprint

authentication systems based on the comparison

between two minutiae sets, a reliable minutiae

extraction algorithm is critical to the performance of

the system. As we know, minutiae are detected from

the raw image through the preprocessing and

extraction stage. However, the extraction stage has

some false minutiae detected, and true minutiae

missed as well. Thus, performance of a fingerprint

authentication system is controlled by three kinds of

errors. In particular, if they occur during an

enrollment phase and are stored as enrolled template,

they will degrade the overall performance

significantly, i.e., the falsely detected minutiae will

affect the matching phase continuously. Therefore,

the wrongly detected minutiae need to be discarded

and the missed ones need to be compensated prior to

be stored as enrolled minutiae.



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Fig.4 Fingerprint-based Authentication system

using minutiae Impression

In the above diagram the fingerprint-based

authentication system using plural fingerprint images

during enrollment. Enrolled minutiae are generated

from several real fingerprint images, which can

prevent false minutiae from being stored as enrolled

minutiae and true minutiae missed.

4 FINGERINT VERIFICATION FOR THE USB

TOKEN:

In the fingerprint matching stage there are

two phases one is minutiae alignment and point

matching. As we know the stored template and input

minutiae cannot be compared directly because noise

or deformations. The first phase i.e. minutiae

alignment phase computes the parameters in order to

align the 2 fingerprints. Then the point matching

phase counts the overlapping minutiae pairs in the

aligned fingerprints. Actually, the first phase minutia

alignment phase requires a lot of memory space and

time for execution than the second phase.

In following algorithm accumulator array is

used in order to compute the shift and rotations

parameters [7]. When the 2 fingerprints are from the

same person the I/P to the alignment phase consists

of two sets of minutiae points P and Q extracted

from fingerprint images [6]. We think that the second

fingerprint image can be obtained by applying a

similarity transformation such as rotation and

translation to the first image. The second point set Q

is then rotated and translated version of the set P,

where points may be shifted by a random noise,

some points may be added and some points deleted.

The main task of fingerprint alignment is to recover

this unknown transformation. As we do not know

whether the 2 fingerprints are the same or not, we try

to find the best transformation.We discretize the set

of all possible transformations, and the matching

score is computed for each transformation. The

transformation having the maximal matching score is

believed to be the correct one.

Let’s consider a transformation,

Fig 5 Flowchart for Enrollment Algrithm using

multiple Impression

5 CONCLUSION:

As we saw USB token is a model of very secure

device, and biometric is the promising technology for

verification.These two can be combined for many

applications to enhance both the security and the

convenience.

However, typical biometric verification algorithms

that have been executed on standard PCs may not be

executed in real-time on the resource-constrained

environments such as USB token. In this paper, we

have presented a fingerprint enrollment algorithm

which can improve the accuracy and a memory-



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efficient fingerprint verification algorithm which can

be executed in real-time on the USB token. To

improve the accuracy, we employ multiple

impressions to check false minutiae detected and true

minutiae missed. Then, to reduce the memory

requirement, we employ a small-sized accumulator

array. To compute the alignment parameters more

accurately, we perform more computations at from a

coarse-grain to a fine-grain resolution on the

accumulator array. Currently, we are porting

memory-efficient speaker and face verification

algorithms to the USB token for multi-modal

biometric authentication.

REFERENCES

1. Bhupesh Gour, T. K. Bandopadhyaya and

Sudhir Sharma, “Fingerprint Feature

Extraction using Midpoint Ridge Contour

Method and Neural Network”, International

Journal of Computer Science and Network

Security, vol. 8, no, 7, pp. 99-109, (2008).

2. Robert Hastings, “Ridge Enhancement in

Fingerprint Images Using Oriented

Diffusion”, IEEE

Computer Society on Digital Image

Computing Techniques and Applications,

pp. 245-252, (2007)

3. Vijaya Kumari and N. Suriyanarayanan,

“Performance Measure of Local Operators

in Fingerprint

Detection”, Academic Open Internet

Journal, vol. 23, pp. 1-7, (2008).

4. Ballan.M, “Directional Fingerprint

Processing”, International Conference on

Signal Processing, vol.2,pp. 1064-1067,

(1998).

5. Prabhakar, S, Jain, A.K, Jianguo Wang,

Pankanti S, Bolle, “Minutia Verification and

Classification for Fingerprint Matching”,

International Conference on Pattern

Recognition vol. 1, pp. 25-29, (2002).

6. N.Ratha, K.karu, and A.Jain; A Real Time

matching System Fingerprint

Databases,IEEE Transactions on Pattern

Analysis and Machine Intelligence,Vol.

18,No.8,August(1996)

7. S. Pan, et. al.,: A Memory-Efficient Fingerprint

Verification Algorithm using A Multi-

Resolution Accumulator Array, ETRI Journal,

Vol. 25, No. 3, June (2003)



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biometric fingerprint authentication by minutiae ... · fig 5 flowchart for enrollment algrithm...

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