ENHANCEMENT OF FINGER VEIN IMAGE USING MULTIFILTERING ALGORITHM
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1 ENHANCEMENT OF FINGER VEIN IMAGE USING MULTIFILTERING ALGORITHM Kayode A. Akintoye 1, 2, Mohd Shafry Mohd Rahim 1 and Abdul Hanan Abdullah 1 1 Department of Computer Science, Faculty of Computing, Univeriti Teknologi Malayia, Skudai, Johor, Malayia 2 Department of Computer Science, Federal Polytechnic, Ado-Ekiti, Nigeria hafry@utm.my ABSTRACT Finger vein image are prone to variou noie due to imperfect capturing device ued. A a reult of tranmiion error, pixel element in the capturing enor malfunction, memory location fault and analog-to-digital converion time error, noie could alo occur. Uing ingle filtering method cannot give a atifactory reult of the vein image. Hence, thi paper propoed a multifilter algorithm that capable of locating the region of interet of the vein and de-noie the image from corrupted noie while the edge of the image are kept intact. The algorithm doe not need to have prior knowledge of the image and the corrupted noie. The fuion of imple mak filter algorithm and mixed filter algorithm of Median and Wiener filter i ued. Experimental reult coded in MATLAB uing SDUMLA-HMT finger vein databae how that the propoed algorithm i feaible and efficient. Keyword: vein pattern, vein enhancement, finger vein, median filter, wiener filter. INTRODUCTION Finger vein biometric recognition ytem i becoming popular nowaday due to it propertie uch a univerality, uniquene, tability and tough reitance to forgery (Verma and Dubey, 2012). Thi i a a reult that the vein are located under the kin and not viible to the naked eye in ome cae. Hence, they have tough reitance to forgery (Yang et al., 2014). The complex pattern exiting inide the finger permit appraiing of a good et of feature that can be ued for individual identification. It i oberved that finger vein information i ued to identify the vein pattern in an individual finger (Gupta and Gupta, 2015). Near infrared light i ued to can vein for haemoglobin within the blood of a finger when an individual finger i kept over the ening device. The flowing of deoxygenated haemoglobin in thee vein aborb the infrared ray, appearing on the guide a dark line, and keeping the ret of the part of the finger tructure a white. Hence, image experience quality degradation becaue of tranmiion of imperfect array of light, low contrat and blurred image from the quality of light and diminihing colour which caued noie (Syarif et al., 2016). Alo, the capturing of tiny hair and kin texture (Gupta and Gupta, 2015), which much reemble vein are till pending problem that caue noie. Generally, image noie i caued becaue of enor and circuitry of a canner or digital camera. The noie can be decribed a the factor that diturb the image recognition and undertanding. Thi noie can be additive Gauian White noie, Pepper noie, and Speckle noie. The detection of the region of the vein and the noiy pixel in the vein image depend on the performance of finger vein image filtering ytem. Filter are mainly for noie removal, contrat enhancement, edge dicovery, and feature of the image location (Sharfia and Suchithra, 2015). Hence, a new image from an original image i produced. The tudy how that there are available exiting filter technique to improve the image through the filtering of the noie, enhancement the contrat of the image and extraction of the foreground image from the background. However, there i no ingle exiting filter technique that can remove thee noie and keep the edge of the vein enhanced and it information intact. The choice of uing multifiltering algorithm in thi reearch work i to apply imple Mak filter algorithm by Lee (Lee et al., 2009) to locate the region to interet of the finger vein image and to ue mixed filter algorithm of Median and Wiener filter to de-noiing image for any kind of noie (Yadav et al., 2015). Vein information i critical for the feature extraction and claification in the finger vein image proceing. LITERATURE SURVEY There are different filter for removing different noie like Mean filter, Median filter & Weiner filter (Sharfia and Suchithra, 2015). In thi ection, Median filter and Wiener filter are dicued. Median filter Median filter i a claical non-linear filtering cheme which ha ability to preerve harp edge of image while removing impulive-type noie. Mean filter and median filter are uing in removing the impule noie only that Mean filter change the mean of the pixel value but refue to preerve detail of the image. Many vital detail are get rid of with the mean filter (Rani & Kamboj, 2013). Thu Mean filter i not uitable for enhancement of finger vein image. In application, median i computed by firt orting all the pixel value that urround the neighbourhood to numerical array and afterward, the middle pixel value i replaced by the pixel being conidered. For intant, the pixel value of a point (x, y) i replaced by the median of pixel value of 8-neighbourhood of a point (x, y) in median filter. Auming image I i an input image obtained after pre-proceing, F i the moving filter mak, and M i the output image. While the pixel value in the 8-neighbourhood filter mak are organized in 644
2 acending order by uing equation 1, the median value i determined by uing equation 2. The value of M(x, y) i then replaced by the obtained median value. The computation i carried out baed on equation 3. Orderet F(0) F(1) F(2)... F( N2) F( N1) (1) F F F ( N/2) ( N/21) median F (2) (( N 1)/2) 2 M( x, y) median { I( x, y);( x, y) F} (3) Where N Number of pixel in neighbourhood F (0) Minimum value F (N-1) Maximum value Wiener filter Wiener filter i a linear filtering cheme for removing blur in image created becaue of linear motion or unfocued optic. Thi cheme wa ued to implement patial moothing and it model complexity control a related to the window ize chooing (Jain, 1989). Converely, it implement an optimal tranaction between invere filtering and noie moothing uch that it proce minimize the overall mean quare error. Thu, the additive noie i removed while the blurring i inverted imultaneouly. The Wiener filter i a linear etimation of the original image (Dipti and Dubey 2015). The Wiener filter computation i hown in equation 4 and implified in equation 5. The tranfer function of wiener filter i given a: G( u, v) H *( u, v) P ( u, v) 2 H( u, v) P ( u, v) P ( n, v) n (4) When dividing the equation P, then it become: G( u, v) H *( u, v) 2 Pn ( n, v) H( u, v) P ( u, v) Where H(u, v) = Degradation function H*(u, v) = Complex conjugate of degration function P n (u, v) = Power Spectral Denity of Noie P (u,v) = Power Spectral Denity of un-degraded image The P n /P i the reciprocal of the ignal-to-noie THE PROPOSED METHOD The propoed method i well known a preproceing of image in digital image proceing technology (Sharifara et al., 2013). The Figure-1 how the workflow of propoed finger vein enhancement uing multifilter. Thi propoed technique ue the Mak filter with fuion of the Median filter and Wiener filter. The proce tart with application of imple Mak filter method to locate the region of interet (ROI) in the captured image. Gamma Corrector i ued to improve the image contrat, and thereafter, the fuion filter called Median Wiener (MW) filter, which combination of Median filter and Wiener filter i applied. The magnitude of all the vector within a mak i taken care of by the Median filter, and ort according to the magnitude. It i therefore uggeted that denoiing mut be improved at pixel level uch that every pixel noie mut be removed. Thu, there i need to divide an image in ub image to perform pixel level filtering. The pixel with the Median magnitude i ued to replace the pixel contemplated. Wiener filter out noie that ha corrupted an Image (Kumar et al., 2010). The operational algorithm i hown below. (5) Figure-1. Propoed ytem workflow for enhance finger vein pattern. 645
3 Median Wiener filter Thi propoed multifilter, Median Wiener (MW) filter, incorporate Median filter and Wiener filter to enhance the quality image. MW filter i an improved Median filter uing Wiener filter. The fundamental uperiority of the propoed filter over mot other filter in that it efficiently remove any noie from Mak filter image while preerving thin line and edge in the original image. The development framework i hown in Figure-2 equal to 106 ubject with 6 finger in 5 variation, which are 3180 (that i, 106 x 6 x 5) image. The fore-finger and middle-finger of both left and right hand of 35 people by 20 variation of each peron, which are equal to 2,800 (that i, 35x4x20) finger vein image databae wa ued for thi experiment. The ample of the image i hown in figure 3a a the original image. To de-noie the corrupted noie and enhance quality finger vein image, three filter, that, Median filter, Wiener filter, and Median Wiener (MW) filter wa conidered. Median filter preerve the harp edge and remove the alt and pepper noie while Wiener filter remove the noie better than the Median filter. Multifiltering Median Wiener filter, which i an advance verion remove any type noie efficiently and preerve the edge better than Median Filter and Wiener filter. The region of interet, noie removal and enhancement of the vein image are the focu of the experiment. The extracted vein image wa converted to a binary image for further proceing. The experimental work and image enhancement reult are ummarized a follow: Figure-2. Framework of MW filter development. Operational algorithm Require: Finger vein (FV) Image, I Enure: Enhanced Image, I 1. Read-in FV databae Image 2. Apply Lee_region (Mak filter) algorithm to get the finger image region i. determine lower half tarting point of image ii. contruct mak for filtering iii. filter image uing mak (upper part & lower part of filtered image) iv. fill region between upper and lower edge v. ave finger edge 3. Re-izing the Image 4. Cropping the image uch that all extra portion i removed 5. Uing Gamma Corrector to improve the image contrat 6. De-noie the image baed on MW filter 7. Printout Enhanced Image, I 8. Convert the Enhanced image to binary image EXPERIMENTAL SETUP AND DISCUSSION The finger vein image databae, which wa created by the Machine Learning and Data Mining Lab, Shandong Univerity (SDUMLA), China (Yin et al., 2011) i employed in thi reearch work. A device, deigned by Joint Lab for Intelligent Computing and Intelligent Sytem of Wuhan Univerity wa ued to capture image of the finger vein under near infrared illumination. The databae contain raw finger vein image from 106 volunteer peron, coniting of index, middle and ring finger of both left and right hand of each peron. Hence, a total finger vein image available are (a) Original Image (b) Detecting Region of Interet (c) Image Cropping (d) Image Sharpened 646
4 (e) Gamma Correction (g) Median Filter (f) Hitogram Equalization (h) Wiener Filter The finger hape of an individual i different a hown in the Figure-3a, and the finger region i located uing imple mak a hown in Figure-3b. However, the making value i meaured in the Y track for individual X point, and the boundary point between the finger and the background in the Y track i the maximum point of the making value. Hence, finger region i cropped out a hown in Figure-3c. Figure-3d i to harpen the image while Gamma Corrector i ued to improve the image contrat and Hitogram equalization i for normalization a hown in Figure-3e and Figure-3f repectfully. Median filter i ued to preerve edge of the image pattern while removing noie and the reult i hown in Figure-3g. Median filter i intended to run through the pixel entry by entry, and replace each pixel with the median of neighbouring entrie. Wiener filter i applied to filter out any unknown noie that ha corrupted the pixel. The reult i hown in Figure-3h. The reult of thi tage can finally convert to binary a in cae of figure-3i for further feature extraction proce. The three filter were evaluated on SDUMLA- HMT finger vein databae uing Peak Signal-to-Noie Ratio (PSNR) a howing in Table-1. Baed on the ratio value between ame image and different image, MW filter of each category i the larget compare to the ratio value of median filter and wiener filter. PSNR i the ratio between the maximum poible power of a ignal and the power of corrupting noie ignal. Thu, it i concluded that Median Wiener filter i an effective filter to enhance quality finger vein image without looen it information. (i) Binary Image Figure-3. Region of interet and vein pattern enhancement of the finger vein image. Table-1. MW filter evaluation uing PSNR. Category hand finger Median filter Wiener filter MW filter Right index Right middle Right ring Left index Left middle Left ring CONCLUSIONS The image preproceing i the initial tage of in the proceing tage of vein recognition cheme. The operation uch a detection the region of interet, filtering and enhancement of vein are performed to get better quality of image for next tage, which i the feature extraction. Hence, thi paper ha hown clearly that uing fuion of Median and Wiener filter are better multifiltering algorithm becaue Median filter keep edge of the image while only remove the noie and Wiener filter retore the corrupt blurred image. Therefore, Median filter and Wiener filter are giving effective reult with repect to noie on capturing the vein image. Alo, the propoed ytem give better reult in the term of Peak ignal noie ratio (PSNR). REFERENCES Dipti Verma, Sipi Dubey Proceing and Enhancement of Palm Vein Image in Vein Pattern Recognition Sytem. International Journal of Computer Science Mobile Computing. 4(4):
5 Gupta P. and Gupta P An Accurate Finger Vein Baed Verification Sytem. Digital Signal Proceing Jain A. K Fundamental of Digital Image Proceing. Prentice-Hall, Inc. Kumar S., Kumar P., Gupta M. and Nagawat A. K Performance Comparion of Median and Wiener Filter in Image De-Noiing. International Journal of Computer Application ( ) Volume. 12. Lee E. C., Lee H. C. and Park K. R Finger Vein Recognition Uing Minutia Baed Alignment and Local Binary Pattern Baed Feature Extraction. International Journal of Imaging Sytem and Technology. 19(3): Rani V. and P. Kambo j Image Enhancement uing Hybrid Filtering. International Journal of Science and Reearch (IJSR), India Online ISSN: Sharfia G. S. and Suchithra H Removal of Salt and Pepper Noie of an Image Uing Hybrid Median Filter. Sharifara A., Rahim M. S. M. and Bahardoot M A Novel Approach to Enhance Robutne in Digital Image Watermarking Uing Multiple Bit-Plane of Intermediate Significant Bit. Informatic and Creative Multimedia (ICICM), 2013 International Conference on Syarif M. A., Ong T. S., Teoh A. B. and Tee C Enhanced Maximum Curvature Decriptor for Finger Vein Verification. Multimedia Tool and Application Verma D. and Dubey S Two Level Centre of Gravity Computation-an Important Parameter for Offline Signature Recognition. International Journal of Computer Application. 54(10). Yadav A. K., Roy R., Kumar R., Kumar C. S. and Kumar A. P Algorithm for De-Noiing of Color Image Baed on Median Filter. Image Information Proceing (ICIIP), 2015 Third International Conference on Yang L., Yang G., Yin Y. and Zhou L A Survey of Finger Vein Recognition. Chinee Conference on Biometric Recognition Yin Y., Liu L. and Sun X Sdumla-Hmt: A Multimodal Biometric Databae. Chinee Conference on Biometric Recognition
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