A Hierarchal Framework for Finger-vein ImageClassification

Dun Tan, 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—For personal identification, the biometric systemsbased on finger-vein pattern have been successfully used inmany applications. The concern for the system efficiency over alarge database should not be negligible in the real situation. So,categorizing the finger-vein images to different classes is helpfulfor reducing pattern matching cost. In this paper, we propose alevel-based framework for roughly and automatically categorizingfinger-vein images. The proposed level-based framework consistsof two layers in classifying finger-vein images. In this framework,the imaging qualities and the image contents are respectively usedfor the first layer and second layer image feature representation.And the k-means algorithm is adopted for automatic finger-veinimage clustering. Using SVM scheme, we can achieve 99% CCR(correct classification rate) over a large image database. Finally,for comparison, the POC (Phase-Only-Correction) matching al-gorithm is used. Experimental results show that the proposedmethod has a good performance in the improving recognitionefficiency as well as recognition accuracy.

Keywords—Finger-vein image, clustering, hierarchal method,classification

I. INTRODUCTION

Biometrics has been widely used for personal verificationin security. Compared with traditional biometric technique,the finger-vein technique exists some exceptional advantagesin application, finger-vein patterns are superior in liveness,anti-counterfeiting and friendliness apart from uniqueness,universality, permanence and measurability. So finger-veinrecognition has drawn substantial attention in personal identi-fication [1], [2], [3], [4], [5], [6], [7], [8], [9], [10], [11], [12],[13], [14], [15], [16], [17], [18], [19], [20], [21].

In the real situation, there always large-scale users for abiometric recognition system in the real situation(e.g., airportsecurity, the national borders or criminal investigation). Tra-ditionally, the system should identify a person by matchingwith the whole users in database which will seriously effectthe matching efficiency. To improve the matching efficiency,categorizing the enrolled biometric images often is a desirablestrategy in practice. In this paper, we propose a method thatcan automatically categorize finger-vein images according tothe finger-vein properties. Compared with traditional finger-vein recognition system, a test finger-vein image only needsto be matched with the finger-vein images in the same category,which can greatly reduce the matching cost in the recognitionprocess.

For system efficiency improvement, many previous worksthat can automatically classify traditional biometric traits have

been done (e.g., fingerprint, iris, palmprint, etc.) to improvethe efficiency of system. Fadzilah [22] gives a review forthe conventional approaches (i.e. heuristic approach, syntacticapproach, neural approach, statistical approach), which havebeen applied to the fingerprint classification. Li and David [23]use a box-counting method to estimate the fractal dimensionsof the iris for the automatic coarse classification of irisimages. Zheng [24] presents a method that uses kernel clusteralgorithm to cluster the iris database into three subdatabasewith statistical features from wavelet coefficients. Xiang [25]reports a method that using palm print line features to classifypalmprint images. Unfortunately, no attentions have been paidon categorizing finger-vein images in the current literature. Toautomatically categorizes finger-vein images, two main issuesshould be addressed.

1) Unlike fingerprint which can be classified into fivecategories: whorl, right loop, left loop, arch, andtented arch by its ridge line feature [26] for itssupervised classification, there are few obvious visualinformation that can easily divide the finger-vein intoseveral classes under supervised learning.

2) It is difficult to find a statistic feature which can easilydivide the finger-vein images into several classeswith large interclass variation and small intraclassvariation.

In order to solve the above mentioned problems, a novelfinger-vein classification algorithm is introduced in this paperwhich can improve the efficiency of a finger-vein based recog-nition system. Before describing the proposed method in detail,some finger-vein image samples are listed in Fig. 1. These ROIimages are extracted from their original images captured usinga homemade finger-vein acquisition system, which is detailedin [15], [16].

From Fig. 1, we can clearly see that the finger-vein imagesvary in their appearance and content information. Moreover,whether the images look bright or dark in their appearance,the finger-vein image contents also vary in vessel networkcomplexity. Therefore, their appearance property is indepen-dent of the content property for finger-vein images. Inspiredby this observation, a hierarchal framework is proposed herefor finger-vein image categorization, as shown in Fig. 2.

In this framework, the appearance characteristic of finger-vein images is used for the first level classification, and thecontent characteristic is used for the second level classification.In this way, m categorizations can be achieved for a finger-

2013 Second IAPR Asian Conference on Pattern Recognition

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DOI 10.1109/ACPR.2013.151

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2013 Second IAPR Asian Conference on Pattern Recognition

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DOI 10.1109/ACPR.2013.151

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Fig. 1. ROIs of some finger-vein image samples.

Level 1

Database

Class 1 Class n

Subclass 1 Subclass i Subclass j Subclass m

Level 2

Fig. 2. A hierarchal framework for finger-vein image categorization.

vein image database. Now, the left important problem is howto effectively cluster the finger-vein images.

It is obvious that the supervised methods are undesirablein finger-vein image classification process. An unsupervisedmethod based on an improved k-means clustering algorithm istherefore used here for finger-vein image categorization. In thisaspect, we should determine 1) what features should be usedfor classification and 2) how many numbers are the finger-veinimage clusters.

The rest of the paper is organized as follows, Section 2describes the features used and analyses their performancefor image clustering. The experiment results are reported inSection 3, and some conclusions are described in Section 4.

II. FEATURE ANALYSIS

Feature extraction is an important part for a biometricrecognition system. So, finding appropriate features of thefinger-vein images (e.g. Luminance, texture, geometry, net-works etc.) is an essential part for finger-vein image clustering.For multiple level finger-vein image categorization, two impor-tant issues should be considered: 1) The dimension of featurevectors should not be very large for reducing the computationand memory cost in learning process; 2) The features shouldbe robustly to noise, rotation and translation.

Recently, many approaches have been proposed for finger-vein feature extraction [17], [18], [19], [20]. These approachesare useful in finger-vein feature analysis. For finding appropri-ate features to finger-vein image cluster, besides consideringthe issues which effect their clustering properties in multiplelevel, as shown in Fig. 2, the following appearance and contentfeatures are applied here for finger-vein image cluster analysis.

A. Appearance-based feature

As the differences of fingers in fat, thickness, vein distri-bution and shape, the appearances of finger-vein are differentin practice, as shown in Fig. 1. So the image appearancefeatures are chosen as the first level feature for finger-veinimage clustering. Some finger-vein image quality evaluationmethods have been proposed [27], [28]. Here, three imagefeatures, the gradient, image contrast and information entropy,are used for finger-vein image quality evaluation. The imageclarity is described as

G =1

(M − 1) ∗ (N − 1)M∑i=1

N∑j=1

√�f2x(i, j) +�f2y (i, j)

(1)where M is the number of row and N is the number of column,�f2x(i, j) represents the horizontal gradient and �f2y (i, j) isthe vertical gradient.

The image contrast is given by

C =1

(M) ∗ (N)

√√√√M∑i=1

N∑j=1

(f(i, j)− f)2 (2)

where f(i, j) represents grey value of a pixel and f is theaverage value of the whole image.

The information entropy is described as

S = −255∑k=0

P (k) log2 P (k) (3)

where P (k) represents the probability that pixels in the kthgrey-level in an image.

B. Content-based feature

From Fig. 1, it is clearly that the finger-vein imagesare various in the vessel network complexity. For the vesselnetwork content-based feature extraction, here, the followingmethods are used.

• Moment invariant features. It is well known thatmoment invariants are powerful in describing thegeometrical characteristics of different objects. SevenHu moments are of invariance in scale, translationand rotation [29], so we use them as content-basedfeatures.

• Wavelet coefficient features. The characteristic ofwavelet transform is particularly suitable for extractinglocal discriminative features [30]. In this paper, first,we consider using Mallat wavelets for images waveletdecomposition. Then, the mean and variance value ofthe high-frequency coefficients after 2-level wavelettransform are used as the part of feature vector. At last,a twelve dimensional feature vector can be obtained.

• Gabor-based features. From Fig. 1, we can see thefinger-vein exhibits some specific ridge textures. Ga-bor filter has been testified that it is powerful in captur-ing specific texture characteristics in image. So, Gaborfilter has been widely used in pattern recognition.

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Here, we used the method proposed in [20] for Gabor-based feature extraction. The eight filtered images aregenerated by 2D convolution operation between a ROIand a Gabor filter at eight orientations, and then usingthe average absolute deviation (AAD) of each blockto represent the local features of a filtered finger-vein image. Thus, for a normalized finger-vein image(160×80), 1024([16×8]×8) dimensional vectors canbe extracted. Since the dimension of feature vector ob-viously is too high for finger-vein image classification,PCA method is used for dimension reduction.

III. EXPERIMENTS

In this section, we build an image database which contains6000 finger-vein images from 600 individuals. Each individualcontributes 10 finger-vein images of the right forefinger. Thecaptured finger-vein images are 8-bit gray images with aresolution of 320 × 240, and all segmented ROIs are resizedto 80× 160 considering finger variations in profile and size.

A. Clustering number selection

Due to no prior categorization information, the finger-vein images should be automatically clustered under an un-supervised learning scheme. Here, an improved k-means algo-rithm [31] is used to implement clustering procedure. In thispaper, maximum and minimum distance algorithm is appliedto set initial clustering centers and the Silhouette validity indexis used to determine optimal number of cluster. The silhouetteindex can report the variability of clusters in interclass andintraclass level. The higher values of the silhouette indexesare, the better the clustering results are. In other words, thefeatures that can generate bigger silhouette index values aremore appropriate for finger-vein classification.

The silhouette index is defined as

Sil(i) =b(i)− a(i)

max{a(i), b(i)} (4)

where a(i) represents the average distance between ith patternand its intraclass patterns, and b(i) represents the averagedistance of between ith pattern and its all interclass patterns.The Silhouette indexes of clustering results on the appearance-based features and the content-based features are respectivelyshown in Fig. 3 and Fig. 4.

From Fig. 3 and Fig. 4, we can see that 1)the silhouetteindex values of the appearance-based features and the content-based features are the biggest when the clustering numberis two, 2)the used appearance features and invariant momentfeatures contribute higher silhouette index values than others.So these two kinds of features are chosen for the followingexperiment of finger-vein classification.

B. Levels of hierarchal framework

From Fig. 3 and Fig. 4, we can also see that the silhouetteindex values are maximum when the class number is two.However, the matching cost can not be reduced greatly if weclassify the finger-vein images into two categorization. So,considering reducing the matching cost, we must increase thecategory numbers.

2 2.5 3 3.5 4 4.5 5 5.5 60

0.1

0.2

0.3

0.4

0.5

0.6

0.7

The number of clustering

The

valu

e of

Silh

ouet

te in

dex

Image quality

Fig. 3. The variations of the silhouette indexes with different class numberfor appearance-based features.

2 2.5 3 3.5 4 4.5 5 5.5 60

0.1

0.2

0.3

0.4

0.5

0.6

0.7

The number of clustering

The

valu

e of

Silh

ouet

te in

dex

Hu momentWaveletGabor+PCA

Fig. 4. The variations of the silhouette indexes with different class numberfor content-based features.

To evaluate the correct rate of classification whether willbe affected by increasing the class number or not, we firstuse k-means to cluster 200 different finger-vein images from200 individuals into two, three, four, five and six classesrespectively based on the appearance-based and content-basedfeatures. Then, a nonlinear kernel SVM classifier [32] is usedto evaluate the classification results. The correct classificationrates (CCRs) of SVM is listed in Table I. From Table I, wecan see that the CCRs obviously decrease with increasing thecluster numbers. So the accuracy of a finger-vein based systemwill reduce with increasing the number of categories in thismanner.

Considering the appearance-based features and the content-based features are independent, two layer hierarchical model,as shown in Fig. 2, is desirable for increasing the class numberas well as reducing matching cost in practice. According to theresults from Fig. 3 and Fig. 4, the final categorization numberm is four, where the appearance-based features are used asthe first layer classification, and the content-based featuresare used as the second layer classification with the resultthat the content-based features can represent more detailedcharacteristics of image.

C. The valuation of the proposed hierarchical model

To evaluate the performance of the proposed hierarchicalmodel in finger-vein classification, we respectively extract two

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TABLE I. THE CORRECT CLASSIFICATION RATES ON THE

APPEARANCE-BASED AND CONTENT-BASED FEATURES

CCRsclass number appearance-based features content-based features

2 99.38% 99.5%3 95.5% 96.38%4 94.37% 88.83%5 81.43% 95.13%6 89% 94.13%

TABLE II. THE CORRECT CLASSIFICATION RATES OF THE PROPOSED

FRAMEWORK.

CCRsTraining samples level 1 level 2 The proposed framwork

200*2 99.5% 99.5% 99%400*2 99.5% 99.38% 98.88%600*2 99.42% 98% 97.42%

images from 200, 400, and 600 individual fingers as trainingsamples and one images as testing samples. Using differentSVMs as classifiers, the CCRs of each level classifier and ourclassify system based hierarchal framework is listed in Table II.

It is clearly that the error rates of each level are accumu-lated when the proposed framework is used for classification.From Table II, we can see that the CCR of the proposedhierarchal framework system is lower than that of each level.So, to categorize each labeled sample correctly, the wrongclassified samples should be discarded in training procedure.Compared Table II with Table I, we can clearly see that theCCRs of the proposed framework are obviously higher thanthose of the appearance-based features and the content-basedfeatures when the cluster number is four. So, the experimentalresults show that the proposed method has an encouragingpotential in classifying finger-vein images.

To evaluate the efficiency of the proposed method in finger-vein matching, the Phase Only Correlation (POC) measure [21]is applied here for image matching. The proposed algorithm isimplemented using MATLAB R2010a on a standard desktopPC which is equipped with a Core i3, CPU 2.7 GHz and 2GB RAM.

For the proposed framework, the matching time is com-puted by averaging the matching time in each sample catego-rization because the sample numbers of different classes aredifferent. The time costs for implementing a matching usingthe proposed method and the traditional matching methodare listed in Table III. Table III shows that the proposedmethod can reduce the matching time to 65.86% comparedwith traditional POC matching scheme.

In Fig. 5, two ROC curves are plotted together for compari-son. From Fig. 5, we can see that the proposed method also hasbetter ROC and makes the lower EER, as listed in Table III.Hence, the proposed method has a good performance inreducing the matching cost as well as improving the accuracyof finger-vein recognition.

TABLE III. RESULTS FROM THE DIFFERENT MATCHING PLANE

Method T(s) EERPOC 5.2942 0.233

POC+proposed 1.8075 0.212

0 0.2 0.4 0.6 0.8 10

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

FAR

FRR

POC+proposedPOC

Fig. 5. ROC curves on POC algorithm and our method.

IV. CONCLUSION

In this paper, we proposed a level-based framework forroughly and automatically categorizing finger-vein images.The proposed level-based framework consists of two layers inclassifying finger-vein images. In this framework, the imagingqualities and the image contents were respectively used for thefirst layer and second layer image feature representation. Be-sides, the k-means algorithm was adopted for automatic finger-vein image clustering and SVM was adopted for classifyingpatterns. The experimental results shown that the proposedmethod had a good performance in improving the recognitionefficiency as well as recognition accuracy.

In future, more reliable finger-vein features will be ex-plored for better categorizing finger-vein images. Besides, theperformance of the proposed framework will be tested in largescale finger-vein database.

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).

REFERENCES

[1] M. Xu and Q. Sun, Vasculature Development in Embryos and Its Reg-ulatory Mechanisms, Chinese Journal of Comparative Medicine, Vol.13,No.1, pp.45-49, 2003.

[2] J.F. Yang and B. Zhang, Scattering removal for finger-Vein imageenhancement, IEEE Int’l Conf. on Hand-based Biometrics, pp.193-197,2011.

[3] J.F. Yang and J.J. Wang, Finger-vein image restoration considering skinlayer structure, IEEE Int’l Conf. on Hand-based Biometrics, pp.188-192,2011.

[4] J.F. Yang and J.L. Yang, Multi-channel Gabor filter design for finger-veinimage enhancement, Proc. of the 5th Int’l Conf. on Image and Graphics,pp.87-91, Sep. 2009.

836836

[5] J.F. Yang and M.F. Yan, An Improved Method for Finger-vein ImageEnhancement, Proc. of Int’l Conf. on Signal Processing, pp.1706-1709,Oct. 2010.

[6] J.F. Yang, B. Zhang and Y.H. Shi, Scattering removal for finger-veinimage restoration,Sensors, vol.12, no.3, pp.3627-3640, Mar., 2012.

[7] E. Lee and K. Park, Restoration method of skin scattering blurred veinimage for finger vein recognition, Electron. Lett., vol.45, no.21, pp.1074-1076, 2009.

[8] J.F. Yang, J.L. Yang and Y.H. Shi, Finger-vein segmentation based onmulti-channel even-symmetric gabor filters, Proc. of IEEE Int. Conf.Intelligent Comput. and Intelligent Syst., pp.500-503, 2009.

[9] J.F. Yang, J.L. Yang and Y.H. Shi, Combination of gabor wavelets andcircular gabor filter for finger-vein extraction, Proc. of 5th Int. Conf.Intelligent Compt., pp.346-354, 2009.

[10] J.F. Yang, Y.H. Shi and J.L. Yang, Finger-vein recognition based on abank of Gabor filters, Proc. of Asian Conf. on Computer Vision, pp.374-383, 2009.

[11] J.F. Yang and X. Zhang, Feature-level fusion of fingerprint and finger-vein for personal identification, Pattern Recognition Letters, vol.33,no.5,pp.623-628, 2012.

[12] W. Song, T. Kim, H.C. Kim, J.H. Choi, H.J. Kong and S.R. Lee, Afinger-vein verification system using mean curvature, Pattern RecognitionLetters, vol.32, no.11, pp.1541-1547, Aug., 2011.

[13] A. Kumar and Y.B. Zhou Human identification using finger images,IEEE Transactions on Image Processing, vol.21, no.4, pp.2228-2244,Apr., 2012.

[14] D.M. Sun, D. Liu and H. Liu, Two modality-based bi-finger veinverification system, Proc. of Int’l Conf. on Signal Processing, pp.1690-1693, 2010.

[15] J.F. Yang and X. Li, Efficient finger-vein localization and recognition,Proc. International Conference on Pattern Recognition, pp.1148-1151,2010.

[16] J.F. Yang and Y.H. Shi, Finger-vein ROI localization and vein ridgeenhancement, Pattern Recognition Letters, 33(12):1569-1579, 2012.

[17] W.J. Da and Y.S. Huan, Driver identification using finger-vein patternswith radon transform and neural network, Expert Systems with Appli-cations, vol.36, pp.5793-5799, 2009.

[18] C.J. Hwan, S.Wonseok, K. Taejeong, L.S. Rae, and K.H. Chan, Fingervein extraction using gradient normalization and principal curvature,SPIE, vol.7251, pp.1-9, 2009.

[19] B.A. Rosdi, C.W. Shing and S.A. Suandi, Finger vein recognition usinglocal line binary pattern, Sensors, vol.11, pp.11357-11371, 2011.

[20] J.F. Yang, Y.H. Shi, J.L. Yang, Personal identification based on finger-vein features, Computers in Human Behavior, 27(5):1565-1570, 2011.

[21] E.C. Lee, H. Jung and D. Kim, New finger biometric method using nearinfrared imaging, Sensors, vol.11, pp.2319-2333, 2011.

[22] F. Ahmad and D. Mohamad, A Review on Fingerprint ClassificationTechniques, International Conference on Computer Technology and De-velopment, pp.411-415, 2009.

[23] Li Yu and David Zhang, Coarse iris classification using box-countingto estimate fractal dimensions, Pattern Recognition, 2005.

[24] T. Zheng and M. Xie, Kernel Cluster and SVMs-Based Algorithm forIris Rough Classification in Massive Databases, International Sympo-sium on Computational Intelligence and Design, pp.382-385, 2008.

[25] X. Wu, D. Zhang, K. Wang, et al, Palmprint Classification UsingPrincipal Lines, Pattern Recognition, 37(10): 1987-1998, 2004.

[26] E. Henry, Classification and uses of fingerprints, Rout-ledge,London,1900.

[27] H. Ma and K.J. Wang,A finger-vein Image Quality Assessment MethodUsing Object and Human Visual System Index, Intelligent Science andIntelligent Data Engineering, pp.498-506, 2013.

[28] L. Yang, G.P. Yang, and Y.L. Yin, Finger vein image quality evaluationusing support vector machines, Optical Engineering. 52(2):027003-1-027003-9, 2013.

[29] M.k. Hu, Visual pattern recognition by moment invariants, IRE Trans-action on Information Theory, vol.8, pp.197-182, 1962.

[30] D.G Shen, Discriminative wavelet shape descriptors for recognition of2D patterns, Pattern recognition pp.151-165, 1999.

[31] S.B. Zhou and Z. Y. Xu, New Method for Determining Optimal Numberof Cluster in K-means Clustering Algorithm, Computer Engineering andApplication of China. 46(16):27-31, 2010.

[32] S.R. Gunn, Support Vector Machines for Classification and Regression.University of Southampton, 1998.

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