Proceedings of the 2011 International Conference on Wavelet "\nalysis and Pattern Recognition, Guilin, 10-13 July, 2011

A Novel Matching Technique for Fingerprint Recognition by Graphical

Structures.

Zhifan Gaol, Xinge Youl, Long Zhou2, Wu Zeng2,3

1 Department of Electronics and Infonnation Engnineering, Huazhong University of Science and Technology, Wuhan, China

2Department of Electric Infonn ation Engineering , Wuhan Polytechnic University, Wuhan, China

3State Key Laboratory of InfonnationEngineering in Surveying, :rvrapping and Remote Sensing, Wuhan University, Wuhan, China

E-l\AIAL: gaozhifan@gmai1.com,youxg@mai1.hust.edu.cn, profzh@126.com, zengwude@yahoo.com.cn

Abstract Fingerprint matching is an important issue in auto­

matic fingerprint identification systems. There are dif­ficulties about fingerprint matching based on neighbor­hood. One is the size of the neighborhood can not be determined readily, the other is the feature in the neigh­borhood can be affected by the noise. To deal with these problem, we developed a novel algorithm for fingerprint matching based on local structures to efficiently extract neighboring minutiae features. Neighboring features present the information of peripheral minutiae which directly connect with the central minutiae on topology. We use one feature vector to present neighboring fea­tures from different samples. The samples considered as the same class can make the proposed algorithm robust to rotation and translation of fingerprint images. The experiments are conducted on FVC2002, and the results illustrate the effectiveness of the proposed algorithm.

Keywords: Fingerprint matching; graphical structure; biomet­

rics; pattern recognition

1 Introduction

With the advent of electronic connection in our society, automatic personal identification has been becoming an im­portant issue in many field. However, traditional idenfica­tions widely used, like ID card and password, cannot meet the need of more accurate and safe personal identification. Fingerprint recognition is a popular technique to overcome the disadvantages of traditional identifications, and provide a more accurate and private solution, With the development of Automatic fingerprint identification system(AFIS), fin­gerprint identification is widely used in identification units

978-1-4577 -0282-2/11/$26.00 ©2011 IEEE

and for evidence of the crime all over the world.

In the work of fingerprint matching, the characteristic features is the core. :rvrany studies have been conducted around this topic: how to capture features, how to ex­tract features, how to select features, and how to use fea­tures. There are several useful features categorized into three levels[6]. Level 1 features, such as pattern class and ridge pattern, provide the macro details of the fingerprint impressions. Minutiae belongs to Level 2 feature. Level 3 features are micro patterns, including ridge width, edge con­tour, pores and other permanent details. The level 1 feature has no uniqueness, however it can provide the general clas­sification of the fingerprint in statistical analysis. Utilizing the level 2 feature is the main method to discriminate fin­gerprints. It provides sufficient infonnation[ll] to enlarge intra-class distance and reduce inter-class distance until fin­gerprints from different people could be recognized. So far, Level 3 feature has been used as a supplementary method to provide extra discriminatory information, partly due to the instruments of capturing fine level 3 feature are expensive.

:rvrany approaches about fingerprint matching have been shown in literature, and the most popular fingerprint match­ing algorithms are based on minutiae. It is necessary to capture accurate information about minutiae. So as to nonnal-quality fingerprint images, the performance of these approaches relying on minutiae is better than most other method[lO]. Though minutiae are divided into many cat­egories, it usually adopts two types of minutiae: ridge bifurcation and ridge ending, shown in Fig.l. Tico and Kuosmannen[14] proposed a method to create a minutiae descriptor, which sampled a set of local orientation val­ues unifonnly around the minutiae point. Cappelli[3] in­troduces another minutiae point called Minutia Cylinder­Code(MCC) to combine nearest neighbor-based methods and fixed radius-based methods to utilize their advantages

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Proceedings of the 2011 International Conference on Wavelet "\nalysis and Pattern Recognition, Guilin, 10-13 July, 2011

Figure 1. A par t of a figure impression with ridge bifurcation and ridge ending. The circle presents the ridge bifurcation, and the rect­

angle presents the ridge ending.

and eliminate their drawbacks. Chang[4] combined Hough

transform and 2-D cluster appproach to correctly identify

the missing patterns and pseudo-patterns. The researches

of Starink[12], Le[16], Tan[13] are around the topic of en­

ergy minimization. Bishnu[2] studied the 2-D partial point

set pattern matching, and introduces some simple assump­

tions for approximate match. Weber[l7] used a template­

based algorithm to recognize fingerprint simply, that was

simpler than Hough transform. Udupa[IS] improved We­

ber's method, to consider the problem of fingerprint match­

ing as comparing between two corresponding feature sets.

Moreover, point pattern matching in fingerprint is

also considered as global minutiae matching and local minutiae matching. Global minutiae matching has two

disadvantages[3]: computationally demanding and lack of

robustness. The state-of-the-art idea is about how to make

the performance of local matching reach the global level.

Based on neighborhood, local minutiae matching is very

efficient, and tolerant against spurious and missing feature

points. However, they have their own shortcomings. First,

if the radius of neighborhood is fixed, as a threshold, the value of radius is not easy to determined, because location

of minutiae is inaccurate and fingerprint images have non­

linear distortion, and the minutiae near the neighborhood

border may be lost. Second, if the number of the points in

neighborhood is fixed, the methods are sensitive to missing

and spurious minutiae.

In this paper, we introduce a novel minutiae-based local

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descriptor, to combine the preceding advantages and over­

come these drawbacks. First, the descriptor is defined and

its characteristics are presented. Then the descriptor is ap­

plied on the topology of fingerprint minutiae and extract

features, so each central point can be represented as a fea­ture vector, and these feature vectors can be converted into

feature values. Finally, we use feature vector sets from dif­

ferent fingerprints to finish verification.

The rest of this paper is organized as follows: Section 2

defines the proposed descriptor and presents its characteris­

tics. Section 3 describes the process of matching algoritllm.

The experiments are presented in section 4. Section S shows the conclusion and future work.

Figure 2. (a)an original fingerprint image. (b)The minutiae set. (c)The topology net.

2 The Proposed Methods

Let T and I be two fingerprint minutiae vectors, corre­

sponding to the template and the input fingerprint, respec­

tively. Unlike other matching techniques, the representa­

tion of minutiae vectors are considered as feature vectors

whose elements are the fingerprint minutiae[lO]. The ele­

ments of the vectors can be described by some fingerprint

features, like type, orientation, location of minutiae and so

on. Most popular minutiae matching approaches consider

each minutiae as a triplet m = {.T, y, B}, where the x, y

are x-coordinate and y-coordinate respectively and B is the

minutiae angle , however, in the following, we only consider

each minutiae as a couple m = {x, y }, and x, y means the

Proceedings of the 2011 International Conference on Wavelet "\nalysis and Pattern Recognition, Guilin, 10-13 July, 2011

Figure 3. There has some marked point on this topology net, which is from A to J. Let A be the central point. The neighboring points

are B, C, D, E, F, G, H, I, while J does not be­

longs to the neighborhood of A, because the

connection between A and J is not direct, re­

lying on F or other points.

coordinates in Cartesian coordinates:

T = {mi,m�, ... ,mj}

(1)

(2)

wheremi = {Xi,Yi},mj = {Xj,Yj},i 1, 2, ... ,NI,

j = 1, 2, ... ,NT, N I and NT are the number of points in

I and T respectively.

Let N be the neighborhood of minutiae mi, and the mik is the point in the neighborhood which has the direct con­

nectivity to the central point mi, that is, the connection be­

tween mi and mik does not rely on any other point.

(3)

where k = 1, 2, ... , N P, N P is the number of neighboring

points, the function Con presents the map from the central

point mi to its neighboring point. Fig.3 shows the direct

connectivity in topology net.

Let d be the Euclidean distance between the neighbor­

hood point and the central point, and S be the slope from

the neighborhood point to the central point.

S = Yi - Yik (5)

Xi - Xik Then we transform the slope S to the corresponding an­

gle 8. the range of 8 is [0,360°) by counterclockwise from

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the first quadrant to the fourth quadrant.

8 = {: + 1800 S + 3600

Xi - Xik :::: 0, Yi - Yik :::: °

Xi - Xik < °

Xi - Xik :::: 0, Yi - Yik < °

(6)

Now the neighboring features are presented as the differ­

ent distances and the different angles to the central point. In

order to process these information, the different neighbor­

ing points are mapped to be on a feature circle C, and the

center of this circle is the central point corresponding the

neighborhood.

C = MAPC(N) (7)

Let r be the radius of C, and the number of the point N P on the circumference of C is 8r, shown in Fig.4. One neigh­

boring point is mapped to one point on the circumference of

C. There mapping function M APC from the neighboring

point to circumference point is divided into two parts,

Cweight = startingweight X d (8)

(9)

The formula(8), is a part of M APC about the weight of

each neighboring point, where startingweight is the start­

ing weight of each point in the neighborhood, and Cweight is the weight after mapping. The formula(9) is about the

position Cposition of neighboring point on the circle C. Next, the feature circle C is extended to a feature vector

V shown in Fig.5, where the point in the circle C, corre­

sponding to the small angle, is mapped to the low position

of the feature vector V, while the point corresponding to

the large angle, which is mapped to the high position of

feature vector V. In feature vector V , the non-zero ele­

ments present the neighboring point. The element position Vposition and value Vweight present the angle 8 and the dis­

tance d of the neighboring point, respectively.

Vposition = Cposition

Vweight = Cweight

(10)

(11)

The feature vector V is sensitive to the rotation of the

fingerprint image. In the several capture of fingerprint from

the same person, the fingerprint images have obvious differ­

ences in vision, one of which is the rotation. This difference

leads the absolute locations of neighboring minutiae to be

variable, resulting in the feature circle and the feature vec­

tor, while the pattern of one fingerprint will be changed.

However, the relative positions between the neighboring

points and the central point are invariant. So the feature vector V is changed to overcome the rotation in following,

and the new feature vector is V min.

Proceedings of the 2011 International Conference on Wavelet "\nalysis and Pattern Recognition, Guilin, 10-13 July, 2011

7 6 5 4 3

4 3 2 8 2 5 0 1 9 0 1 6 7 8 10 16

11 12 13 14 15

� I--

1 t--

8r I-

Figure 4. N P is the number of points which have the distance r from the central point. The central point is denoted by 0, and other

digits presents the point related to N P. When radius R is 1, N P is 8; When radius R is 2, N P is 16. When radius R is r, N P is 8r, ex analogia.

Now the operation of circular shift, left shift or right shift, is used to deal with the feature vector V. Let SV be the feature vector set translated from V:

SV = {VilVi = CircularShijt(V)} (1 2)

where i = 0 ,1 , 2 ... Sr-1, CircularShijt is the function of circular shift, and Viis the feature vector after shifting i ele­ments on V and VO is V itself, which is shown in Fig.6. SV presents the various patterns caused by rotation. In order to make the fingerprint matching algorithm robust to rotation, instead of patterns in SV , it uses one pattern V min. First we add the weights to the elements of the feature vector, the

c V

small angle 8r 8r-l high position

2 8r-2

.-8r

8rl 3 8r2 2 low position

1 large angle

Figure 5. The transform process from a fea­ture circle C to a feature vector V.

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weight vector W is

(1 3)

where j = 0,1,2 ... , Sr - 1, N+ is the positive integer col­lection and a is any positive integer. Then the weighted value WV of every feature vector in SV is calculated. Last the pattern corresponding to the smallest weighted value in WV is presented by V min. For every vertex in the topol­ogy net, we can get a feature vector V min. The num ber of V min is depend on the num ber of vertex.

WV Vweight WT '\:"' (i-I)Vi L- a weight

where i = 1,2, ... , Sr.

(1 4)

Let S min be the vector set of all the Vmin in one finger­print image. in which, the V";'in presents the Vmin corre­sponding to the ith minutiae, and minutiaenum presents the number of minutiae.

The template fingerprint minutiae vector T and the input template fingerprint minutiae vector I have a S min each, denoted by STmin and SImin, and the corresponding Vmin is presented as VT min and V Imin.

[VII VI2 V rminutiaenU mj SImin = min' min' .. " .1. min (1 6)

STmin = [VT;'in, VT';'in, ... , VT,:l:utiaenu mj (1 7)

where V I:nin is composed of V I�eight and V I;osition ,while VT';'in is composed of VT;ei ht and VTiosition

The matching is divided into locaf matching and global matching. Considering V I:nin and VT";'in, we can calcu­late the local matching score. Let the starting score be zero. VTj. is be traversed. If a non-zero value element is found, mtn . the position of the element VTgosition is taken down. Then the distance sector D S is presented as { (Bb, Ub)

DS = (Bb,Sr)

(0, Ub)

Bb> O,Ub < Sr Bb> O,Ub:;:' Sr Bb:::; O,Ub < Sr

(1 8)

where Bb and Ub represent the below boundary and the upper boundary of DS, Bb = VT;;"sition - DT H, Ub =

VTj ·t· + DT H and DT H is the threshold determining POSt ton � . whether two minutiae from different fingerprmts are at the same position of the feature vector.

If It only has elements of zero value in DS of V I:nin, there is no matching point in DS between VI:nin and VT';'in. Otherwise the elements of non-zero value VT�n

Proceedings of the 2011 International Conference on Wavelet "\nalysis and Pattern Recognition, Guilin, 10-13 July, 2011

Figure 6. The transform process from a fea­ture circle C to a feature vector V.

in VT�in and the elements V I;::in corresponding in V I;"in are compared using the weight. The difference between the VT�:ight and VI �'!,ight is described by DW, there is a threshold called VT H, if the absolute value of DW is smaller than VT H, the element is matched, and the local matching score LM S between the two feature vector adds one to itself.

One feature vector VT';'in in STmin is compared with every feature vector in SImin. The feature vector which has the biggest LM S is the matching vector to the VT';'in. Every feature vector in STmin has a matching vec­tor in SImin, and the two feature vectors are considered as a matching pair, each of which has aLMS. The number of local matching scores is equal to the element num ber of STmin. Then the sum of LMS is the global matching score GMSbetweenSTmin andSlmin.

Let N umberT is the number of non-zero value element inSTmin. If N;:::r,�TT is smaller than VT H, the fingerprint image T and I are matched. Otherwise, T and I are not matched.

3 Experiments

We perform experiments on DBIA, DB2A, DB3A, DB4A of the public databases FVC2002, which contains 3200 fingerprints from 400 different fingers, to evaluate the performance of our algorithm. The test is performed with Matlab under Windows XP on Celeron M, 1.6GHz and 1536M machine.

The result is measured by the value of equal error rate (EER) experimented on DBIA, DB2A, DB3A, DB4A, and compares our algorithm with previous works at evalu­ating the performance of the proposed methods. These pre-

vious methods are all finished on FVC2002 database. Table 1 shows the results.

method DBl(%) DB2(%) DB3(%) DB4(%) Tico[14] 4. 0 4. 2 7.1 7. 7 Chen[5] 4. 6 5. 3 8. 9 6. 7

Benhammadi[l ] 4. 2 2. 6 10. 6 5. 1 Lumini[9] 4. 2 3. 9 15.0 6. 7

Liu[8] 4. 3 4. 0 10. 1 4. 6 Zsolt[7] 4. 3 4. 8 7. 5 4. 6

Our method 3. 5 3. 9 5.6 4. 2

Table 1. EER

4 Snmmary and Future Work

In this paper, we have developed a novel topology-based representation technique for fingerprint verification. It uti­lizes neighbor structure information to match the point pat­terns at principle preliminarily, and carries on global match­ing at last. The proposed representation is invariant to rota­tion and translation by analyzing the relationship of minu­tiae(genuine and spurious), not capturing more information about fingerprints. As the matching method by relationship of point sets, the structures are robust to missing and spuri­ous minutiae. Then, the structures are not influenced by the resolution of fingerprint images, which is convenient to deal with the fingerprint matching at different resolutions. In ad­dition, local noise only affects the structures in a local field, resulting from the structure has a good structure stability. If the noise affect the whole image, like white noise, this problem should be solved in preprocessing stage, and it has been given some attracting solution in many previous work. Experimental results show that the performance of the pro­posed algorithm is efficient. In future work, we would like to optimize our algorithm further and try to achieve the bet­ter performance, which includes the consideration to deal with the large difference of two feature sets in waiting com­parison. Then we will consider affect to the structures by the nonlinear transformations of fingerprint images. Con­sidering the structures as a "tension" draw on analogy with physics, we want to measure the non-linear transformation through the change of the structures.

ACKNOWLEDGMENT

This work was supported by the grant 60973154 and 61075015 from the NSFC, NCET-07-0338 from the Min­istry of Education, China. This work was also partially supported by the Fundamental Research Funds for the Cen­tral Universities, HUST:201OZD025, and Hubei Provin-

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Proceedings of the 2011 International Conference on Wavelet "\nalysis and Pattern Recognition, Guilin, 10-13 July, 2011

cial Science Foundation under Grant 201OCDA006 and 20 lOCDB0660 1 , China.

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