Finger Vein Recognition Using Gabor Filter and Support ... - IEEE Xplore

IEEE IPAS’14: INTERNATIONAL IMAGE PROCESSING APPLICATIONS AND SYSTEMS CONFERENCE 2014

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Finger Vein Recognition Using Gabor Filter and Support Vector Machine Souad Khellat-kihel1,2, Reza abrishambaf2, Nuno Cardoso2, João Monteiro2, Mohamed Benyettou1 1 Laboratory of Modeling and Optimization of Industrial Systems (LAMOSI) Department of Informatics, Faculty of Mathematic and Informatics University of Sciences and Technology Mohamed-Boudiaf B.P. 1505 EL M’NAOUER 31000 ORAN – ALGERIA. Email:[email protected] 2

Centro Algoritmi University of Minho, Campus of Azurém 4800-058, Guimarães PORTUGAL

Abstract— Nowadays biometric identification systems are widely spread since the safety of those systems has been proven. They exhibit a large number of advantages when compared to other identification systems such as key and password that are subject to falsification and loss. Among biometric systems, finger vein recognition based on venous network has been considered recently in the literatures. This paper aims to present a finger vein recognition system using Support Vector Machine (SVM) based on a supervised training algorithm. The proposed system is divided in several phases, each performing a specific task. Two pre-processing schemes are employed in order to assess the efficiency in terms of recognition rate. Simulation results show that using Gabor filters in preprocessing for codifying the venous network and SVM for the classification can improve the recognition rate when compared to the existing methods. Keywords— Biometrics, Venous Network, Support Vector Machines, Classification.

I.

INTRODUCTION

Biometric refers to an identification and authentication technology which consists of transforming biological, morphological and behavioral characteristics into a numerical print [1]. The characteristics vary from face, fingerprints, hand geometry, handwriting, iris, retinal, vein, and voice. Recently finger vein has been adopted in biometric systems [1]. In the 1990’s researchers have found that the venous system was unique to each individual [2]. The veins of the finger are a network of blood vessels under the skin and they can be used for biometric identification [3]. In addition to unicity, universality, permanence and measurability, the systems based on the finger’s venous network are much more robust against fraudulent reproduction 978-1-4799-7069-8/14/$31.00 ©2014 IEEE

than other biometric systems. The finger’s veins authentication technology has several important characteristics [2-3]: •

Resistant to criminal tampering: because veins are inside the body, there is a lower risk of falsification.

• Unique and constant: finger vein patterns are different even among identical twins and remain constant during the human’s lifetime. • Contactless acquisition: near-infrared light are used for image acquisition which, therefore, leads to have a non-invading and contactless acquisition to ensure both convenience and cleanliness of the users. • Simple feature extraction: the finger’s venous network is relatively stable and clearly captured, enabling the use of low-resolution cameras to take vein images with small-size and perform simple image processing. Naoto Miura [3] proposed a matching by using a binarization of the veins images and for the elimination of the noise a transform of distance was used. For the recognition, the Hidden Markov Model (HMM) was employed. The disadvantages of this system are the huge amount of time for processing and the method is not able to recognize the deformed veins images. In [4], they extracted the minutiae (bifurcations and terminations) and then they used the Hausdorff distance to analyze the similarities between minutiae. Jinfeng Yang [5] proposed a recognition system based on a combination of Gabor wavelets and a circular Gabor filter. For the classification a similarity measure has been used. This method shows a good performance when a Gabor filter is used. The general algorithm of [6] includes the

IEEE IPAS’14: INTERNATIONAL IMAGE PROCESSING APPLICATIONS AND SYSTEMS CONFERENCE 2014

2D Gabor filters and then the Euclidean distance is calculated. However this process involves several comparison stages. In [7] Xi Chen et al used the Maximum Margin Locality Preserving Projection (MMLPP) for the discriminative feature extraction. Chaotic Random Projection was used for cancellable biometric template generation. The main disadvantages of the mentioned works are the use of the total image with distance calculations which requires more memory and execution time. Considering those methods, one can conclude the effectiveness of the Gabor filter. For this reason, we adopted the Gabor filter for the enhancement and feature extraction to extract 256 features, presenting these features with only one representative vector instead of the total 2D veins image. This codification with Gabor filter is used for the fingerprint [8] however it has not been used for the codification of the veins images. The Support Vector Machine (SVM) based on supervised training been proposed for recognizing the individuals with their finger vein images. This has been shown for a long time convergence during the classification. There are researches in the literatures showing that the SVM has been used for the finger veins images but with another objective. For instance, in [9], they used the SVM to classify the local areas in the finger vein images. Local areas that includes a large, medium and small amount of vein patterns. Lu Yang ([10] evaluated the quality of finger vein images using SVM with classifying the finger vein images into two classes namely, high and low quality finger vein images. Kuan-Quan Wang [11] used SVM for classification but they used the Gaussian filter in the preprocessing and the LBPV (Local Binary Pattern Variance) for the feature extraction. Kang Ryoung Park [12] used the SVM for combining the Hamming distance and the Euclidean distance. Additionally this is a binary SVM meaning that the output is genuine or imposter. Moreover the SVM has shown a good performance on the other biometric modalities (fingerprint [8], palm print [13]). The remaining of this paper is structured as follows: the finger vein capturing is presented in section 2. Section 3 presents the Support Vector Machines. The proposed architecture of the finger vein recognition demonstrated in section 4. The experimental results on the PKU V4 and PKU V2 databases are presented in section 5. Finally, section 6 concludes the paper. II. VENOUS NETWORK CAPTURING Vein patterns are invisible to the naked eye and at the same time visible by a sensor sensitive to near-infrared light (wave lengths between 700 and 1000 nanometres) [2-3]. Nearinfrared light passes through the tissues of the human body and it is blocked by dyes such as haemoglobin or melanin. As haemoglobin exists densely in blood vessels, near-infrared light shining causes the veins to appear as dark lines of shade in the captured image [3]. There are two methods used for capturing vein pattern images: "light reflection" Fig. 1[2] and "light transmission" Fig. 2[2]. In the case of "light reflection" the light source and the image sensor are placed on the same side of the finger and the image sensor captures the reflected light of the finger

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surface. In the case of the "light transmission", the finger is placed between the image sensor and the light source and the near-infrared light passes through the finger where it is captured by the image sensor.

(a)

(b)

Fig. 1. (a) Light reflection method, (b) Image captured by the reflection method

(a)

(b)

Fig. 2.: (a) Light transmission method, (b) Image captured by the transmission method

III. THE SUPPORT VECTOR MACHINES

In the recent years, new methods of learning are developed based on the statistical learning theory of Vapnik and Chervonenkis. One of these methods, called Support Vector Machine (SVM). The SVM is a group of training techniques intended to solve problems of discrimination (to decide to which class a pattern belongs) or regression (to predict the numerical value of a variable). The classifier uses the idea of the optimal hyper plane to calculate a border between groups of points. There are many linear classifiers (hyper planes) that separate the data. However only one of these achieves maximum separation. The objective of the SVM is not only to find a separator between the classes but finding the optimum hyper plane. To overcome the disadvantages of non-linearity case, the SVM change the data space by using the Kernel (in our case we used the Gaussian kernel). The nonlinear transformation may allow a linear separation for the examples in a new space. This new space is called space of characteristics Fig. 3.

Fig. 3. The non linearity

IEEE IPAS’14: INTERNATIONAL IMAGE PROCESSING APPLICATIONS AND SYSTEMS CONFERENCE 2014

The SVMs approach passes through two steps (Fig. 4): • Training: The search for an optimal hyper plane of separation by maximizing the margin, with the resolution of a quadratic program and determination of the Lagrange multipliers [14]. • Test: after the determination of the Lagrange multipliers, it applies the decision function to the test examples for class determination [14].

Input space (x)

Transformation

Features space ()

Optimal Hyperplane

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This can limit the amplification of the noise and increase more local contrast. CLAHE can be computed as follows:

S = Hist (i ) *

255

(1)

M *M

where Hist is the cumulative distribution of the local histogram and it reflects the number of appearance of the gray levels in a portion i of the image. S is the new value of the pixel and M is the size of the local window (Fig. 7).

Capture

Enhancement

Feature extraction and codification

Classification

Class(x)

Fig. 4. The Support Vector Machine steps

The principle of SVM explained in the previous part is summarized in solving binary classification problems, but the problems encountered in reality are the multi classes type and hence the importance to extend the SVM principle to problems more than two classes. There were several attempts to combine binary classifiers to determine this problem (multi classes). There are also tests to incorporate the classification of several classes in the process of SVM so that all the classes are treated simultaneously. The main methods for this issue are One Versus One and One versus All [15]. The SVM OneVersus-All suppose the construction of N classifiers and N comparisons for the decision however the SVM One–VersusOne require N (N-1)/2 classifiers for a problem of N classes. IV. THE PROPOSED APPROACH

Result

SVM

Codes veins images database

Fig.5. The finger vein recognition process

Histogram Equalization

Median Filter

Segmentation

Gabor Filter Fig. 6. The image enhancement process

Since biometric system is a pattern recognition system, it is composed of several modules and we tried to improve each module as presented in this section. Fig. 5 presents the general architecture of our recognition system. Recognition through the venous network depends mainly on the quality of captured images, so it is essential to integrate the preprocessing module to improve the quality of the images. A. Image Enhancement As Fig. 6 shows two preprocessing schemes are used in the proposed approach. The first method is based on Histogram Equalization and Median Filter and the second one is based on Gabor filter only. The reason for using two methods is to better understand the efficiency of the Gabor filter. 1) Histogram Equalization and Median Filter : The image Histogram Equalization is used to adjust the distribution of the gray levels. However this method is not suitable for the finger vein images [16]. The Contrast Limited Adaptive Histogram Equalization (CLAHE) [17] serves to limit the size of the local histogram.

(a)

(c)

(b)

(d)

Fig.7. (a) original image, (b) the histogram of (a), (c) the preprocessed image with CLAHE, (d) the histogram of (c).

IEEE IPAS’14: INTERNATIONAL IMAGE PROCESSING APPLICATIONS AND SYSTEMS CONFERENCE 2014

The median filter step aims to reduce the noise and the irregularities of the image to improve the visual quality of the image. Fig. 8 represents the result of median filter.

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produce a feature vector called FingerCode. This technique does not use singular points explicitly and it is robust to noise. We have chosen the characteristics texture extraction of veins images by the Gabor filter that captures the local orientation and the information of the frequencies of the venous network. Therefore they are adapted for the extraction of texture fingerprint image information [20]. We tried to extract the characteristics from texture of the images with representing the finger veins by a vector of 256 elements. The general shape of this filter [8] is given in (2): 2

Fig.8. The preprocessed image with the CLAHE and median filter

G ( x, y , f , θ ) = exp{

In real applications, the segmentation of images of finger vein networks is still a critical step to extract finger vein features from veins images since the finger vein images are in low contrast. We applied the thresholding segmentation method with a global threshold. The objective is to extract the venous network from the background (Fig. 9).

2

−1 x ' y' [ 2 + 2 ]}cos(2π fx ') 2 δ x' δ y'

x ' = x sin θ + y cos θ y ' = x sin θ − y cos θ

(2)

(3)

Where f is the frequency of the sine plane wave along the direction from the x-axis, and δx’ and δx’ are space constants of the Gaussian envelope along x’ and y’ ,respectively. As a result, we obtain a vector of 256 elements to represent the image of the venous network. This features vector will be used for learning and classification of the individuals.

(a)

(b)

Fig.9. (a) Preprocessed image, (b) Segmented image

2) Gabor Filter

In our system the enhancement also consists of the 2D Gabor filter: This Bi-dimensional filter has shown great efficiency for the finger vein images enhancement fusing the phase and direction features to identify the finger vein [18-19]. Because the finger vein consists of many lines, it has stable and obvious direction which makes it suitable to be viewed as a texture image. 2D Gabor filters are widely used for extracting the features of textured images. This version of the 2D Gabor filter is basically a bidimensional Gaussian function centered at origin (0, 0). Fig. 10 depicts the finger vein image and the result of the Gabor filter.

C. The classification For classification we used the Support Vector Machines for the solids mathematical bases which support it with two algorithms One-Versus-One and One-Versus-All and for the kernel we use the Gaussian. In the One-Versus-One approach, each class is discriminated from another requiring N (N-1)/2 functions decisions (classifiers) but in the One-Versus-All, each class is opposed to all the others and requires N decisions functions. We have used two different databases the first one is composed of 20 individuals each individual has 10 catches of index, available on the Internet from the PKU (V2) databases but the second one is composed of 20 individuals each individual has 8 catches of index, PKU Finger Vein Database (V2,V4), available at [21] (Fig. 11):

(a) Image from DB1 (a)

(b)

Fig. 10. (a) The finger vein image, (b) The enhanced finger vein image

B. The feature extraction A Gabor filter based approach was developed by Youssef et al in [20] where a bank of Gabor filters have been used to

(b) Image from DB2

Fig. 11. Comparison of the acquired image

The two approaches were used with a training and a test set. The training set consists of 20 classes (individuals) with six trials for each class that result 120 feature vectors in total. The test set also contains 20 classes with four other tests for each class and therefore a total of 80 features vectors representing the venous networks. This process related to the

IEEE IPAS’14: INTERNATIONAL IMAGE PROCESSING APPLICATIONS AND SYSTEMS CONFERENCE 2014

first database. The same partition was applied to the database i.e. 60% of the captures will correspond learning phase and 40 % for the test. The different obtained by these approaches will be discussed following section.

second to the results in the

V. RESULTS The evaluation of any biometric recognition system is to determine the recognition rate which represents the probability which the system can identify a person who already exists in the data bases. We applied two types of the pre-processing. The first pre-processing (P1) consist of the contrast amelioration, the application of the median filter and the segmentation. However the second pre-processing (P2) consist of the 2D Gabor filter application. We represent also our results with the ROC curve (Fig. 12). TABLE I.

Data Bases

RECOGNITION RATE

Data Bases 1

Data Bases 2

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(ODLPP) algorithm to reduce the dimensionality of highdimensional Gabor feature vector. The LDA, LPP, DLPP, OLPP, and ONPP adopt the PCA for the pre-processing however the last method ODLPP used the Gabor filter for the pre-processing and all these works apply an Euclidean metric based on the nearest neighbour classifier. Their effort is to approve the effectiveness of the ODLPP. ƒ ƒ ƒ

PCA: Principal Components Analysis. LPP: Locality Preserving Projection. DLPP: Discriminant Locality Preserving Projection. ƒ OLPP: Orthogonal Locality Preserving Projection. ƒ ONPP: Orthogonal Neighbour Preserving Projection. ƒ ODLPP: Orthogonal Discriminant Locality Preserving Projection. Fig. 12 presents the ROC curve for the recognition system demonstrating the recognition rate versus the False Acceptance Rate (FAR). Fig. 13 depicts the recognition rate of the proposed system when comparing with the existing methods. VI. CONCLUSION AND FUTURE WORKS

Preprocessin g

P1

P2

P1

P2

Support Vector Machine s

OneVersusAll

OneVers usOne

OneVers usAll

One Vers usOne

OneVersu s-All

One Ver susOne

One Ver susAll

One Ver susOne

Rate recogniti on (%)

93.33

95

98.7 5

98.7 5

91.66

95

93. 33

93. 33

In this paper, a finger vein recognition system based on SVM has been presented. The proposed approach contains several phases, namely, acquisition, enhancement, feature extraction and classification. The system has been implemented in MATLAB with real images from two databases. In the pre-processing phase, two methods have been used: the first method was based on histogram equalization, median filter and segmentation. The second method was based on a 2D Gabor filter. The reason for having two pre-processing methods was to demonstrate how Gabor filter performs comparing to other methods. Also, in the feature extraction phase, Gabor filters have been used. In the first database, Gabor filter pre-processing exhibits a better recognition rate. However, in the second database, the first pre-processing scheme performs better in One-Versus-One. As a future work, the proposed approach will be tested on other databases. We will focus on the integration of Region of Interest (RoI) into the venous system and possibly establishing a multimodal scheme where several biometric systems will be combined to form a unit identification system. ACKNOWLEDGEMENT Souad Khellat-kihel is supported by the Erasmus Mundus program EU-MARE NOSTRUM (EUMN) grant agreement number: 2011-4050/001-001-EMA2. This work has been supported by FCT - Fundação para a Ciência e Tecnologia within the Project Scope: Pest-OE/EEI/UI0319/2014.

Fig. 12. The finger vein ROC curve

In Table 2, we put different results on the PKU V2 (i.e. DB1) database. Xi Chen and Jiashu Zhang [22] proposed the methods given at the table II, they includes the Gabor based Optimized Discriminant Locality Preserving Projections

IEEE IPAS’14: INTERNATIONAL IMAGE PROCESSING APPLICATIONS AND SYSTEMS CONFERENCE 2014

TABLE II. Study [22]

PREVIOUS WORKS

Method

Recognition Rate

PCA

88.5%

LPP

91.65%

DLPP

92.54%

OLPP

93.86%

ONPP

93.88%

ODLPP SVM (proposed)

95.17%

[22] [22] [22] [22] [22] Proposed method

98.75%

Fig. 13. SVM recognition rate comparing with the existing results

REFERENCES [1]

B. Wassila, “ Identification biométrique des individus par leurs empreintes palmaires,” Mémoire de magister , Université des Sciences et de la Technologie d'Oran USTO-MB, 2007. [2] Hitachi, “ Finger Vein Authentication- White Paper,” Copyright , 2006. [3] M. Naoto, A. Nagasaka, and M. Takafumi, “ Feature extraction of finger-vein patterns based on repeated line tracking and its application to personal identification,” Machine Vision and Applications, SpringerVerlag, 2004; S. 194 -203. [4] Y. Chengbo, Q. Huafeng, “ Biometric Identification Technology Finger Vein Identification Technology,” Tsinghua University Press , Chinese, 2009; S. 81-87. [5] J. Yang, “ Combination of gaborwavelets and circular gabor filter for finger-vein extraction,” Lecture Notes in Computer Science, September2009, S.346-354. [6] Z. Hong, L. Zhi and Z. Qijun, “ Finger vein recognition based on Gabor filter,” Springer-Verlag Berlin Heidelberg, 2013. [7] X. Chen, X. Bai and X.Tao, “ Chaotic Random Projection for Cancelable Biometric Key Generation,” Springer-Verlag Berlin Heidelberg, 2013. [8] Y. Elmir, Z. Elberrichi and R. Adjoudj, “ Support vector Machine Based Fingerprint identification,” CTCI 2012 conference , Adrar university, Algeria , 2012. [9] L. Hyeon Chang, K. Byung Jun, L. Eui Chul and P. Kang Ryoung , “ Finger vein recognition using weighted local binary pattern code based on a support vector machine, ” Journal of Zhejiang University: Science C (Impact Factor: 0.3).01/2010; 11:514-524. [10] Y. Lu, Y. Gongping, Y. Yilong and Rongyang Xiao, “ Finger vein image quality evaluation using support vector machines,” Opt. Eng. 52(2), 027003 (Feb 18, 2013). doi:10.1117/1.OE.52.2.027003 ,2013.

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[11] W. KUAN-QUAN, S. KRISA, XIANG-QIAN WU and QIU-Sm ZHAO, “finger vein recognition using LBP variance with global matching,” Proceedings of the International Conference on Wavelet Analysis and Pattern Recognition, Xian, 15-17 July, 2012. [12] P. Kang Ryoung “ finger vein recognition by combining global and local features based on SVM,” Computing and Informatics, Vol. 30,295–309, 2011. [13] W. Boukhari and M. Benyettou, “Identification Biométrique des Individus par leurs Empreintes Palmaires «Palmprints»: Classification par la Méthode des Séparateurs à Vaste Marge (SVM), ” CIIA 2009. [14] N. Ayat, “ Sélection de modèle automatique des machines à vecteurs de support - application à la reconnaissance d’images de chiffres manuscrits,” Thèse de doctorat, Ecole De Technologie Supérieure, Québec, 2004. [15] S. Khellat-kihel , “Reconnaissance des individus par leurs réseaux veineux,” master thesis, Univesity of sciences and technology USTOMB, 2012. [16] L. Caixia, “ The Research on Finger Vein Image Preprocessing Based on Mathematical Morphology,” College of Information Science and Engineering, Zaozhuang University, China, Springer-Verlag London Limited ,2012. [17] K. Wang, Z. Yuan, “ Finger vein recognition based on wavelet moment fused with PCA transform,” J Pattern Recognition and Artificial Intelligence, Chinese, 2007; S. 692-697. [18] P. Prabhakar, T. Thomas, “ Finger Vein Identification Based On Minutiae Feature Extraction With Spurious Minutiae Removal. 3rd Int. Conf. on Advances in Computing and Communications,” IEEE Computer society, 2013. [19] K.Wang, L. Jingyu and P.Oluwatoyin, “Finger Vein Identification Based On 2-D Gabor Filter,” 2nd Int. Conf. on Industrial Mechatronics and Automation, 2010 . [20] Y. Elmir, “ L’identification biométrique par les empreintes digitales, ” Mémoire de magister, Université des Sciences et de la Technologie d'Oran USTO-MB, LAMOSI, 2007. [21] LI Wenxin, http://ai.pku.edu.cn/ [22] X. Chen and Z. Jiashu, “ Optimized Discriminant Locality Preserving Projection of Gabor Feature for Biometric Recognition,” International Journal of Security and Its Applications Vol. 6, No. 2, April 2012 .