[ieee 2013 2nd iapr asian conference on pattern recognition (acpr) - naha, japan...

Structure Feature Extraction for Finger-veinRecognition

Di Cao, Jinfeng Yang, Yihua ShiTianjin Key Lab for Advanced Signal Processing

Civil Aviation University of China,Tianjin, China

Email:jfyang@cauc.edu.cn

Chenghua XuInstitute of Electronics

Chinese Academy of Science, Beijing, China

Email: xuch@mail.ie.ac.cn

Abstract—A new finger-vein image matching method basedon structure feature is proposed in this paper. To describe thefinger-vein structures conveniently, the vein skeletons are firstlyextracted and used as the primitive information. Based on theskeletons, a curve tracing scheme depended on junction points isproposed for curve segment extraction. Next, the curve segmentsare encoded piecewise using a modified included angle chain,and the structure feature code of a vein network are generatedsequentially. Finally, a dynamic scheme is adopted for structurefeature matching. Experimental results show that the proposedmethod perform well in improving finger-vein matching accuracy.

Keywords—Finger-vein recognition; structure feature; modifiedincluded angle chain;

I. INTRODUCTION

As a new biometrical identification technology, finger-veinrecognition has drawn increasing attention from biometricscommunity in recent years [1], [2], [3], [4], [5], [6], [7],[8], [9], [10], [11], [12]. The finger-veins used for biometricsare superficial veins beneath the skin layer. In biologicaltissue, the veins are developed randomly and always activein liveness [13]. Compared with the traditional biometriccharacteristics (e.g. face, iris, and fingerprints), finger-veinpatterns exhibit some excellent advantages in real application.For instance, finger-vein patterns are universality, uniqueness,permanence and measurability, finger-vein images can becaptured noninvasively without the contagion and unpleasantsensations. These merits make the finger-vein trait highlyunique and fraud-proof for personal identification.

The finger-vein uniqueness is directly determined by therandomness of the finger-vein networks. In anatomy, fingerveins form a network along a finger and the network cannotbe broken unless some veins suffer rupture. Hence, networkbased finger-vein description is crucial for the understandingof finger-vein characteristics. The structure features related tothe finger-vein networks are therefore very crucial in finger-vein pattern representation [14], [15]. In graphic interpretation,a network can be further decomposed into curve segmentsand junctions. Thus, the structure features related to the curvesegments and junctions are capable of describing the veinnetwork pattern in terms of its topology and geometry. Toextract the structure features, we should first obtain the curvesegments and junctions and then encode them together usinga certain rule. However, how to extract and encode the curvesegments reliably is still an open problem for finger-veinrecognition.

Traditionally, two schemes are often adopted for curverepresentation. One is the landmark-based approach [16], [17],[18], the other is the chain-based approach [19], [20], [21],[22]. The former describes a curve by ordering the detectedlandmarks such as corners, protrusions and high curvaturepoints. This method is fast in computation but unreliable inshape description.

The latter regards a curve as a chain. In [19], the chainrepresented a curve was defined as a connected sequence ofline segments with specified length and direction. This kind ofchain was simple but sensitive to noise, stretch and rotation.To handle these problems, the included angle chain (IAC) hasbeen proposed in [20]. Although the IAC could perform wellin curve shape description, it is time-consuming and can noteffectively encode the curve segments crossed mutually in thevein networks.

Aiming at structure feature extraction and matching forfinger-vein recognition, the contributions of this paper are: 1) acurve tracing method is proposed for curve segment extraction;2) a modified included angle chain (MIAC) is proposed forcurve encoding; 3) a dynamic scheme is proposed for structurefeature matching. Experimental results show that the proposedmethod can give higher matching accuracy as well as savingtime cost.

II. FINGER-VEIN SKELETON EXTRACTION

As veins exist beneath the human skin, finger-vein imagesare often captured in a transillumination manner. Due to lightscattering, the captured finger-vein images are always not goodin quality. The separability is therefore very poor between thevenous regions and non-venous regions. Hence, finger-veinimage enhancement is a very important step for finger-veinskeleton extraction.

In order to reliably strength the finger-vein networks, anenhancement scheme proposed in [8] is used here. Consideringlight scattering in biological tissues, a scattering removalmethod is used to improve the visibility of finger-vein images.Then, a dictionary of even-symmetric Gabor filter is designedto exploit finger-vein information at different scales and orien-tations. Finally, a multiscale multiplication rule is adopted forfinger-vein enhancement after 2D convolution operation. Thefinal enhanced result is illustrated in Fig. 1(b), which obviouslyshows that the venous regions are much clearer than those inFig. 1(a).

2013 Second IAPR Asian Conference on Pattern Recognition

978-1-4799-2190-4/13 $26.00 © 2013 IEEE

DOI 10.1109/ACPR.2013.113

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2013 Second IAPR Asian Conference on Pattern Recognition

978-1-4799-2190-4/13 $31.00 © 2013 IEEE

DOI 10.1109/ACPR.2013.113

567

(a) (b) (c) (d)

Fig. 1. Finger-vein network skeleton extraction. (a) An original image. (b)The enhanced image. (c) The cluster image. (d) The skeleton of a finger-veinnetwork.

Then the finger-vein networks are extracted from the en-hanced finger-vein images. Here, preserving the topologicalstructure of a finger-vein network is very important for a net-work segmentation task. Although the contrast is significantlyimproved between venous regions and non-venous regions, it isstill difficult to obtain a perfect finger-vein network in practicesince the vein ridges with great intensity variation alwaysinterlace mutually. Hence, the k-means method is adopted forpixel clustering. The cluster image is shown in Fig. 1(c).

Assume that K is the cluster number of pixels, the pixelscorresponding to the Kth cluster center with the minimumintensity value are viewed as background pixels. Obviously,each pixel class correspond to a logical image. The binaryimage describes the venous regions is the combination imageof K − 1 logical images. To obtain the skeletons, the binaryimage is thinned by morphological operation, the redundantpoints and short segments deletion, as shown in Fig. 1(d).

III. STRUCTURE FEATURE EXTRACTION

A. Minutiae point detection

In fingerprint biometrics, a fingerprint pattern can be wellrepresented by a number of critical points in the fingerprintimage. These critical points are commonly referred as minutiaeand they are widely used as the feature to match a pair offingerprints [23]. The minutiae points are here refereed tothe ending points and junction points contained in the finger-vein skeletons. Each curve segment in a finger-vein networkis related to one or two kinds of these points. Based on theskeletons, a pixel-wise operation known as the cross numberconcept is employed to detect the minutiae points.

Assume that p0, · · · , p7 represent eight binary points ina 3 × 3 block centering at point p, as shown in Fig. 2(a),Ntrans(p) denotes the number of transition times between 0and 1 from p0 to p7, we can compute

Ntrans(p) =7∑

i=0

|pi+1 − pi|, if p = 1. (1)

where p8 = p0. For a point p on skeletons, that is p = 1,if Ntrans(p) ≥ 6, p is a junction point. Otherwise, ifNtrans(p) = 2, p is an ending point. In Fig. 2(b)-(d), weillustrate the detected minutiae points, where “◦” and “� ”are respectively used to mark the junction points and endingpoints.

(a) (b) (c)

p7 p0 p1

p6 p p2

p5 p4 p3

(d)

Fig. 2. Minutiae point detection. (a) A 3 × 3 block. (b)-(d) The detectedminutiae points in some finger-vein skeletons.

z1

z2

z3

z1

z2

z1

Fig. 3. Starting point location around a junction point.

B. Curve tracing

Obviously, compared to the ending points, the junctionpoints are more reliable in mining curve segments. Therefore,based on the detected junction points, a curve tracing methodis proposed here for curve segment extraction.

Given a junction point, we can see from Fig. 2(b)-(d) thatthree curve segments are connected together with it. So, threestart points should be located in advance for curve tracing.Assume that p is a junction point, there must be three realpoints among its 8 neighbors, p0, · · · , p7, as shown in Fig. 2(a).Thus, we can clockwise locate these three points, z1, z2, z3, asstart points, as shown in Fig. 3, where “+” is used to markthem.

Let Qm(m = 1, 2, 3) be a coordinate sequence of a curvesegment, qm be a moving point with a start zm, the processto evaluate Qm is briefly described as following.

1) Initialize (x0, y0)|Qm = (x0, y0)|qm , whereqm(x0, y0) = zm(x, y), and turn the other twostart points into zero;

2) Find a new location (xj , yj) for qm in its 8 neigh-bors, where qm(xj , yj) = 1 and let (xj , yj)|Qm =(xj , yj)|qm ;

3) Turn qm(xj , yj) into zero, and let j = j + 1;

4) Repeat step 2 and step 3 cyclically until qm is ajunction point or an ending point.

We refer the above procedure as curve tracing. Thus,the coordinate sequence denoted by Qm is a curve segmentcorresponding to a start point zm. By doing curve tracingaround any junction point, we can clockwise extract threecurve segments corresponding to it.

C. Structure feature representation

It is undoubted that directly using the extracted curvesegments and minutiae points for network structure featurerepresentation is undesirable in practice. Therefore, encoding

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Fig. 4. Traditional curve encoding methods. (a) Freeman chain code. (b)Included angle chain.

the curve segments is necessary for curve feature extraction.In this aspect, Freeman chain code [19] and included anglechain [20] had been used for curve encoding.

Freeman chain code is often used in coding the pixels alongthe length of a segment with a direction number, as shown inFig. 4(a). It is a complete representation of a curve and easy toimplement. In the framework of included angle chain, a curveis modeled by a number of linked equal length segments and asequence of codes using the included angles between a pair ofneighboring line segments, as shown in Fig. 4(b). It performswell for rotation, scaling and translation.

An ideal vein curve encoding method should have thefollowing characteristics: uniqueness, robustness and highlyefficient computation. However, these two methods are bothunsuitable for vein curve encoding. This is because: 1) Free-man chain code is sensitive to noise, rotation and start point.Any small disturbances along the boundary may cause greatchanges in the code; 2) the calculation of included anglechain is too complex for short vein curve segments; 3) theextracted curve segments vary greatly in length and unite acomplex network by junction points. Hence, a modified includeangle chain (MIAC) is proposed here for handling vein curveencoding problem.

Let �(Q) be the length of a curve segment Q. Three rulesof selecting points from Q are made according to �(Q), asshown in Fig. 5, where H1 represents a detected start pointzm(m = 1, 2, 3). If �(Q) = 2, Q is incomplete for includedangle computation. So a point H3 is added clockwise to H2

in the horizontal axes, as shown in Fig. 5(a). If �(Q) > 5,Q becomes redundant. The points selected from Q with twosteps are used for included angle computation, as shown inFig. 5(c). The curve segments with 2 < �(Q) ≤ 5 have non-redundant points in computation, as shown in Fig. 5(b). Let sibe an included angle, we define

si = acosd(dot(Hi −Hi+1, Hi+2 −Hi+1)

d(Hi, Hi+1)× d(Hi+1, Hi+2)), (2)

where acosd(·) is the inverse cosine expressed in degree,dot(·) is a scalar product operator, and d(·) is a Euclideandistance operator. Thus, a directional included angle chaincorresponding to a curve segment Q can be constructedas c = [s1, s2, · · · , sns ], where ns denotes the number ofincluded angles. Here, c is called a MIAC code.

Next, the structure feature of a finger-vein network isrepresented by the MIAC codes and the junction points. First,the junction points are numbered clockwise. Jk is defined as

(a) (b) (c)

��

��

��

��

��

��

��

��

��

��

��

��

��

�

��

��

Fig. 5. Modified included angle chain. (a) �(Q) = 2. (b) 2 < �(Q) ≤ 5.(c) �(Q) > 5.

[ ]kkk

kkkcccyxJ321

,,,,=

(xk, yk)c1

c2

c3

k

k

k

Fig. 6. Local structural feature generation.

the kth local structure code corresponding to the kth junctionpoint. Then, we define

Jk = [xk, yk, ck1 , c

k2 , c

k3 ], (3)

where (xk, yk) is the coordinate of the kth junction point, andcm(m = 1, 2, 3) denotes the mth MIAC code connected withthe kth junction point, as shown in Fig. 6. Finally, the structurefeature code (SFCode) of a network is represented by

J = [J1, J2, · · · , JN ], (4)

where N denotes the total number of the junction points in anetwork.

IV. STRUCTURE FEATURE MATCHING

Unfortunately, for an identical finger, its SFCodes oftenvary with the finger-vein images captured at different sessions.Comparing Fig. 7(a) with Fig. 7(b), we can clearly see thatthere are apparent differences in some local structures markedby two windows. Hence, a dynamic scheme with two steps isadopted here for implementing SFCode matching.

A. SFCode reduction

Assume that Ja and Jb are two SFCodes, both Ja and Jb

should be reduced to have the same code length for matching.First, some reference junction points contained in Ja and Jb

are detected. Let pai and pbj be two junction points in Ja and

Jb. If ‖pai − pbj‖ ≤ r is satisfying, then (pai , pbj) is a pair ofreference junction points. Apply this method to all the junctionpoints, and then we can obtain M(M ≤ N) pairs of referencejunction points, as shown in Fig. 7(c) and (d), each pair ofreference junction points is connected by a line.

Then, for the kth pair of reference junction points, wecompute Lk

m = min[�(cka,m), �(ckb,m)](m = 1, 2, 3), where Lkm

represents the minimum code length of two MIAC codes, cka,m

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��� ��� ��� ���

Fig. 7. Reference junction points detection. (a)-(b) Two skeletons of anidentical finger at two sessions. (c)-(d) Pairs of junction points.

and ckb,m. So the part of a MIAC code, cka,m or ckb,m, beyond

Lkm is pruned. Let cka,m = �cka,m�Lkm and ckb,m = �ckb,m�Lkm ,

where �·�Lkm denotes an operation of extracting Lkm compo-

nents from the forepart of a MIAC code, we can reconstructtwo pure MIAC codes without junction information

Jak = [cka,1, c

ka,2, c

ka,3],

Jbk = [ckb,1, c

kb,2, c

kb,3],

where k = 1, 2, · · · ,M . Thus, the reduced SFCodes areexpressed as {

Ja = [Ja1 , · · · , Ja

M ]

Jb = [Jb1 , · · · , Jb

M ], (5)

where the components of Ja and Jb are also arranged clock-wise. Obviously, Ja and Jb are dynamic though they have thesame code length.

B. MIAC matching

For Ja and Jb, the similarity measure between them isdefined as

S(Ja, Jb) =L∑

i=1

dis(Ja(i)− Jb(i))/L, (6)

where dis(s − t) = |s − t|, and L = �(Ja) or �(Jb). T is

given as a threshold, if S(Ja, Jb) < T is satisfying, the twofinger-vein images are from an identical finger, otherwise theyare two images without similarity.

Hence, only a dynamic part of an original SFCode is usedfor matching, which is helpful for improving the matchingaccuracy in despite of some local differences between twofinger-vein networks from the same class.

V. EXPERIMENTAL RESULTS

Here, the used finger-vein image database contains 48individuals, and all the images are 8-bit gray images with aresolution of 320× 240 and captured by a homemade imageacquisition system [8]. Each individual contributes 20 finger-vein images.

Based on this database, the number of genuine attemptsis 9120(48C2

20), and the number of imposter attempts is451200(20 × 20C2

48). Using the similarity measure definedby Eq. 6, the ROC (receiver operating characteristic) curveis plotted in Fig. 9, where false non-match rates (FNMR) and

� ���� ���� ���� ���� ����

����

����

����

����

���

����

����

����

����

���

�

��

��������������������������������������������������������� ���!�����""�

Fig. 8. ROC curves of different encoding methods.

TABLE I. TIME COST AND EER

Algorithm Time cost/s EERChain code 0.0525 0.0759

Differential chain code 0.0502 0.0693Included angle chain 0.1451 0.0631

Proposed method 0.0813 0.0582

false match rates (FMR) are shown in the same plot at differentthresholds on the matching scores, and EER (equal error rate)is the error rate where FNMR and FMR are equal. The ROCcurves on different curve encoding methods are plotted inFig. 8. The time costs and EERs of different encoding methodsare listed in Table I for further comparison. From Fig. 8 andTable I, we can see that MIAC has the best performance ofROC curve and makes the lowest EER with the shortest timecost. It indicates that MIAC is powerful in describing the veincurves.

For comparison with other methods, some finger-vein infor-mation exploited methods, for example, repeated line trackingbased method proposed in [14], local interconnection structurebased method proposed in [15] and the minutiae point methodproposed in [24] are first realized. The ROC curves on differentmethods are plotted in Fig. 9 respectively. From Fig. 9, we canclearly see that the proposed method makes the lowest EER.

All the experiments are implemented using MATLABR2010a on a standard desktop PC which is equipped witha Core i5 CPU 3.20 GHz and 2.00GB memory. Experimentalresults show that the proposed method not only makes thelowest EER but also contributes lower time cost. This indicatesthat the proposed method is effective and efficient in improvingfinger-vein recognition performance.

VI. CONCLUSION

In this paper, a new image matching method based onstructure feature has been proposed for finger-vein recognition.We firstly extracted the vein skeleton networks as the primitivestructure information. Then, based on the skeletons, the curvesegments are extracted using a curve tracing scheme. Third, amodified included angle chain was proposed for curve segmentencoding. Finally, a dynamic scheme is adopted for structure

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����

���

����

���

����

���

����

���

�

�

�

���������������� ��� ����� ������������������������

Fig. 9. ROC curves of different methods.

feature matching. Experimental results show that the proposedmethod had a good performance in improving finger-veinmatching accuracy.

ACKNOWLEDGMENT

This work is jointly supported by National Natural Sci-ence Foundation of China (No. 61073143, No.61379102,No.61001176), Tianjin Municipal Science and TechnologySupport Key Project (No. 07ZCKFGX03700).

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