A Combined Method for Finger Vein Authentication System
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(IJCSIS) International Journal of Computer Science and Information Security, Vol. 9, No. 7, July 2011 A Combined Method for Finger Vein Authentication System Azadeh Noori Hoshyar Assoc. Prof. Dr. Ir.Riza Sulaiman Department of Computer Science Department of Industrial Computing University Kebangsaan Malaysia University Kebangsaan Malaysia Bangi, Malaysia Bangi, Malaysia email@example.com firstname.lastname@example.org Afsaneh Noori Hoshyar Department of Industrial Computing University Kebangsaan Malaysia Bangi, Malaysia Afsa1986@yahoo.com Abstract— Finger vein as a new biometric is developing in security purposes. Since the vein patterns are unique between each individual and located inside the body, forgery is extremely Vein patterns are located inside the body. Therefore, it difficult. Therefore, the finger vein authentication systems have provides a high level of accuracy due to the uniqueness and received extensive attention in public security and information complexity of vein patterns of the finger. It is difficult to forge. security domains. According to the importance of these systems, Epidermis status cannot effect on recognition system . the different techniques have been proposed to each stages of the Finger vein systems provide user-friendly environment. system. The stages include image acquisition, preprocessing, Therefore, finger vein is a good candidate for authentication segmentation and feature extraction, matching and recognition. and security purposes. While the segmentation techniques often appear feasible in theory, deciding about the accuracy in a system seems important. According to the importance of finger vein authentication Therefore, this paper release the conceptual explanation of finger system, this paper proposes a system as shown in figure1. vein authentication system by combining two different techniques in segmentation stage to evaluate the quality of the system. Also, it applies Neural Network for authentication stage. The result of this evaluation is 95% in training and 93% in testing. Keywords- Finger Vein authentication; Vein recognition; Verification; Feature extraction; segmentation I. INTRODUCTION A wide variety of systems require the reliable personal Figure 1. The scheme of finger vein authentication system authentication schemes to confirm or identify an individual In the proposed system, different filters are applies for pre- requesting their services. The purpose of these schemes is processing stage. Since there are different techniques on ensuring that only a legal user and no one else can access to segmentation stage of authentication systems such as matched provider services. Among different authentication traits such as filter , morphological methods , repeated line tracking fingerprints, hand geometry, vein, facial, voice, iris and method  and maximum curvature points in image profiles signature, finger vein authentication is a new biometric , the lack of experiment on combining two different identification technology using the fact that different person techniques of “gradient-based threshold” and “maximum has a different finger vein patterns. The idea using vein patterns curvature points in image profile” was found to improve the as a form of biometric technology was first proposed in 1992, quality of verification system, while the previous studies while researches only paid attentions to vein authentication in considered just a single technique for segmentation purpose. In last ten years. Vein patterns are sufficiently different across next step, Neural Network is applied to evaluate the quality of individuals, and they are stable unaffected by ageing and no training and testing, finally Neural Network is trained and significant changed in adults by observing. It is believed that tested for pattern recognition purpose. the patterns of blood vein are unique to every individual, even among twins . 15 http://sites.google.com/site/ijcsis/ ISSN 1947-5500 (IJCSIS) International Journal of Computer Science and Information Security, Vol. 9, No. 7, July 2011 Experimental results of this work show that the system is valid for user authentication purpose even in high security Input image environments, as it was the initial intention given the nature of human finger vein. II. FINGER VEIN AUTHENTICATION SYSTEM cropping The steps of finger vein authentication system are explained in the following. Reducing noise A. Image Acquisition The first step in finger vein authentication system is capturing the image of finger veins. The quality of captured image helps to identify the veins of fingers as well. Image Increasing contrast Acquisition can be done in two ways; i) using infrared- sensitive digital camera with wavelength between 700nm to Figure 3. Image enhancement process 1000nm and banks of LEDs ; ii) using digital camera with CCD sensor and IR filter which is located on the camera with C. Segmentation and Feauture extractions wavelength 700nm to 1000nm and banks of LEDs . In this stage, the enhanced finger vein image is segmented Therefore, as shown in figure 2, the Near-infrared rays and the features are extracted. Since there are different methods generated from a bank of LEDs (light emitting diodes) for segmentation, this paper propose the combination of two penetrate the finger and are absorbed by the hemoglobin in the segmentation methods as "Gradient-based thresholding using blood. The areas in which the rays are absorbed (veins) thus morphological operation" and "Maximum Curvature Points in appear as dark areas in an image taken by a CCD camera Image Profiles" to segment and extract the features. The (charge-coupled device) located on the opposite side of the features of first segmentation method are merged with features finger. The CCD camera image will be transferred to PC for of second segmentation method to obtaine an accurate record next step of authentication . for each finger vein images. 1) Gradient-based thresholding using morphological operation: In this segmentation method, the gradient of image by alpha filter is created. Then, thresholding is performed on gradient of image. The high gradient values which are more than threshold value in the image fall as edge (vein). After the vein determination in an image, the morphological operations are employed to make an image smoother. The proposed morphological operations are „majority‟ to remove extra pixels, „openning‟ to smooths Figure 2. Image Acquisition system the contour of image and breaks narrow passages, „bridge‟ to connects the neighbor pixels which are disconnected. As stated above, the better quality can make recognition The original and obtained image after first segmentation system more accurate. For this purpose, the noise is reduced on method (includes performing gradient, thresholding, next step. morphological operation) are shown in figure 4. B. Pre-Processing As the image has been taken by camera has redundant parts which needs to be cropped. Therefore, only the central part of finger vein image can be taken in Matlab by a simple line; I2= imcrop (I, rect); (1) Where „I‟ is an image and „rect‟ is the position for cropping. a b The next step in this section is reducing the noise of finger Figure 4. a) Original image b) Obtained image after first vein image to improve segmentation. Since the captured image segmentation has much noise, therefore it needs to be improved for getting better quality. For this purpose, the enhancement Functions The total process for the "Gradient-based thresholding such as ‟medfilt2‟, „medfilt2‟ can be employed. As the final using morphological operation" method is shown as figure 5. step for image pre-processing, the image contrast can be increased using commands in Matlab such as „histeq‟. Figure 3 shows the total process for enhancing the image. 16 http://sites.google.com/site/ijcsis/ ISSN 1947-5500 (IJCSIS) International Journal of Computer Science and Information Security, Vol. 9, No. 7, July 2011 Gradient of image The total process for the "Maximum Curvature Points in Thresholding Image Profiles” method is shown as figure 5. Calculating the cross sectional profile of image 'majority' operation calculating curvatures 'open' operation Calculating the Local 'bridge' operation maximum of curvatures Segmented image Calculating the scores of center points Figure 5.Total process of first method for finger vein extraction Assigning scores to center 2) Maximum Curvature Points in Image Profiles: In this points segmentation method, the curvatures of image profiles are checks, then, the centerlines of veins are obtained by Figure 5. Total process of second method for finger vein extraction considering the positions where the curvatures of a cross- The following features in the first and second methods of sectional profile are locally maximal. The centerlines are "Gradient-based thresholding using morphological operation" connected to each other; finally, the vein pattern is achieved. and "Maximum Curvature Points in Image Profiles" are This method is robust against temporal fluctuations in vein extracted to train Neural Network. width and brightness (N.Miura, A.Nagasaka, and T.Miyatake 2005). The extracted features for the first method are as follows. The algorithm for achieving the pattern can be divided into 2 Sum(~BW2(:)) : The number of black pixels in the stages; segmented vein image. Extracting the centreline positions of veins: The first step of Bwperim(BW2): Perimeter of foreground(veins) in algorithm is to detect the centerline positions. For this segmented vein image. purpose, the cross-sectional profile of finger vein image is calculated to obtain the intensity value of each pixel along the Bwdist(BW2): Number to each pixel that is the distance line in an image. In created matrix of intensity, when the between the pixel and the nearest nonzero pixel of BW2. intensity is positive, it is considered as curvature until it Bwarea(BW2): The area of the foreground (veins) in becomes negative again. The maximum differences of segmented vein image. intensities between two pixels are considered as a vein pixel in a row of matrix. Connecting center positions of veins: For connecting the The extracted features for the second method are as follows. center positions, all the pixels are checked. If a pixel and two Cross sectional profile of segmented vein image in neighbors in both sides have large values, the horizontal line vertical direction: Sum of the intensities of pixels in is drawn. If a pixel and two neighbors in both sides have segmented vein image in vertical direction. small values, a line is drawn with a gap at a pixel position. Therefore, the value of a pixel should be increased to connect Cross sectional profile of segmented vein image in the line. The last condition on connecting the center positions horizental direction: Sum of the intensities of pixels in of veins is a pixel has large value and two neighbors in both segmented vein image in horizental direction. sides have small values, a dot of noise is created in pixel position, and therefore the value of a pixel should be reduced. Cross sectional profile of segmented vein image in Figure 6 shows the result of second segmentation. oblique1 direction: Sum of the intensities of pixels in segmented vein image in oblique1 direction. Cross sectional profile of segmented vein image in oblique2 direction: Sum of the intensities of pixels in segmented vein image in oblique2 direction. Curvatures score: Sum of the calculated scores of curvatures in segmented vein image. a b D. Matching and Recognition by Neural Network The table which is created using the combination of Figure 6. a) Original image b) Obtained image after second segmentation "Gradient-based thresholding using morphological operation" 17 http://sites.google.com/site/ijcsis/ ISSN 1947-5500 (IJCSIS) International Journal of Computer Science and Information Security, Vol. 9, No. 7, July 2011 and "Maximum Curvature Points in Image Profiles" methods is applied for training Neural Network and also estimating the quality of training and testing for proposed model. This assess is done by comparing the true output and the output of the model. For training Neural Network the table is divided into two tables of training and testing. Therefore, training table has data are used for training purpose and testing table has data are used for testing purpose. Also another two tables are created as training output and testing output. The data of training and testing output considered as the name of image. Therefore, the Neural Network has been trained and then simulated to assess the model quality by comparing the true output and the model output. In training Neural Network, the epochs and goal were considered „200000‟ and „0‟. The best run occurred when the performance become close to the goal. As figure 6, the performance becomes „0.183054‟ from „200000‟ which is close to the goal „0‟. Figure 8. The VAF index for training and testing After training, the Neural Network is simulated using the simulation command in Matlab as the following. R=sim(net,Features) R= The result of Network simulation Net = Created Neural Network Features = obtained features from previous section Figure 6. Training process After simulation the R is obtained as Figure 9. The result of this trainig are shown as figure 7. It shows the differences between output and actual output in training and testing.The blue line is output and the red line is actual output. a b Figure 7 a) Output and actual output in training b) Output and actual output in testing The Variance Accounted For (VAF) index which use to assess the quality of the model is estimated as 95% for training and 92% for testing as shown in figure 8 . Figure 9. Simulation result R=7.1756‟ shows the image belongs to the 7th person in the table which was trained in Neural Network. Therefore, R recognizes the person who is dealing with a system. This 18 http://sites.google.com/site/ijcsis/ ISSN 1947-5500 (IJCSIS) International Journal of Computer Science and Information Security, Vol. 9, No. 7, July 2011 recognition can be employed in different applications of Conference on Bioinformatics and Biomedical Engineering, ICBBE security. Shanghai  A.D.Hoover, V.Kouznetsova, and M.Goldbaum. 2000. Locating blood vessels in retinal images by piecewise threshold probing of a matched III. CONCLUSION filter response. IEEE Transactions on Medical Imaging 19: 203 - 210. This paper proposed a combined method for finger vein  Walter, T., J. Klein, P. Massin, and F. Zana. 2000. Automatic Segmentation and Registration of Retinal Fluorescein Angiographies— authentication system. “Gradient-based threshold” and Application to Diabetic Retinopathy. In InternationalWorkshop on “Maximum curvature points in image profiles” were combined Computer Assisted Fundus Image Analysis: Denmark. to obtain precious features. The Neural Network was trained by  Miura, N., A. Nagasaka, and T. Miyatake. 2004. Feature Extraction of the features to evaluate the quality of the system. Also, Neural Finger- Vein Patterns Based on Repeated Line Tracking and Its Network was applied to individual recognition. Application to Personal Identification. Machine Vision and Applications 15:194-203. 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