The International Journal of Engineering and Science (IJES)


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The International Journal of Engineering & Science is aimed at providing a platform for researchers, engineers, scientists, or educators to publish their original research results, to exchange new ideas, to disseminate information in innovative designs, engineering experiences and technological skills. It is also the Journal's objective to promote engineering and technology education. All papers submitted to the Journal will be blind peer-reviewed. Only original articles will be published.

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The International Journal of Engineering and Science (IJES)

  1. 1. The International Journal of Engineering And Science (IJES)||Volume||1 ||Issue|| 2 ||Pages|| 248-252 ||2012|| ISSN: 2319 – 1813 ISBN: 2319 – 1805 Extraction of Dynamic Region of Interest (ROI) for Palmprint using Templates Databases Mr. P.Srinivas 1 Mrs. Y.L. Malathilatha2 Dr. M.V.N.K Prasad 31. Associate Professor, CSE Department, Geethanjali College of Engineering & Te chnology(GCET), Hyderabad, A.P.2. Associate Professor, CSE Department, Swami Vivekananda, Institute of Technology (SVIT), Hyderabad, A.P.3. Assistant Professor, Institute of Development and Research in Banki ng Technology (IDRBT), Hyderabad, A.P.----------------------------------------------------------------Abstract-----------------------------------------------------------Bio metric recognition predicated on palm-print features contains different processing stages such as dataacquisition, pre-processing, feature extraction and matching. This paper fixates on the pre-processing sectionwhich is quite important in providing high accuracy in pattern recognition. Preprocessing is utilized to aligndifferent palmprint images and to segment the central part for feature ext raction. In this paper we imp lement amethod of Dynamic Region Of Interest depending on the size of the image. Most of the existing work uses staticregions fro m palm print, not utilizing significant portion of the palm. Intuitively, the more area utilized forfeature extraction and matching, the better the recognition use of templates databases.Keywords: Palmprint, Reg ion of Interest (ROI), Wrin kles.---------------------------------------------------------------------------------------------------------------------------------------Date of Submission: 11, December, 2012 Date of Publication: 25, December 2012----------------------------------------------------------------------------------------------------------------------------- ----------I. Introduction Bio metrics is considered to be one of the steps 1) Binarzing the palm image 2)robust, reliable, efficient, utilizer-amicable, secure Extracting the shape of the hand or palm 3)mechanis ms in the present automated world. Detecting the key point 4) Establishing aBio metrics can provide security to a wide variety coordinate system and 5) Ext racting the ROI. Mostof applications including secure access to of the research uses Otsu‟s method for binarizingbuildings, computer systems, laptops, cellular the hand image [1]. Otsu‟s method calculates thephones and ATMs. Fingerprints, Iris, Vo ice, Face, suitable global threshold value for every handand palmp rint are the different physiological image. According to the variances between twocharacteristics utilized for identifying an classes, one of the classes is the background whileindividual. Palmprint verificat ion system utilizing the other one is the hand image. The boundarybiometrics is one of the emerging technologies, pixels of the hand image are traced utilizingwhich recognizes a person predicated on the boundary tracking algorith m [2]. The key pointsprinciple lines, wrinkles and ridges on the surface between fingers are detected utilizing severalof the palm. These line structures are stable and different implementations including tangent [3],remain unchanged throughout the life of an Bisector [4], [5] and Finger predicated [6], [7].individual. More importantly, no two palmp rintsfro m different individuals are the same, and The tangent predicated approachnormally people do not feel uneasy to have their considers the edges of two finger holes on thepalmprint images taken for testing. Therefore binary image wh ich are to be traced and thepalmprint predicated recognition is considered prevalent tangent of two fingers holes is found toboth utilize- amicable as well as fairly accurate be axis X. The middle po int of the two tangentbiometric system. points is defined as the key points for establishing Bio metric recognition predicated on the coordinate system [3]. Bisector predicatedpalm-print features contains different processing approach concentrates on not joining the fingers bystages such as data acquisition, pre-processing, converting the upper region of the fingers and thefeature ext raction and matching. This paper fixates lower component of the image to white. It aims inon the pre-processing section which is quite determining two centroids of each finger gaps forimportant in providing high accuracy in pattern the image alignment since only the centre ofrecognition. Preprocessing is utilized to align gravities within the defined three finger gapdifferent palmprint images and to segment the region. After locating the three finger gaps thecentral part for feature extraction. Most of the centre of gravity of the gaps can be determined.preprocessing involves generally five prevalent Then the two centroids of each finger gap are connected to obtain the three lines. The line The IJES Page 248
  2. 2. Extraction of Dynamic Region of Interest (ROI) for Palmprint using Templates Databasesthrough the centroids of each finger gap regionintersects the palm of a key point and the points to 2.1 Location of figure web pointssetup a coordinate system [4]. All these The follo wing processes are performed toapproaches utilize only the information on the locate finger web locations using binary palmprintboundaries of fingers. While Ku mar et al proposes utilize all informat ion in palm [8] they fit anellipse to a binary palmprint image. According to 1. Image is converted to binary with grey value 0orientation of ellipse, a coordinates system is or 1.established. Most of the preprocessing algorithm 2. Boundary tracing 8-connected pixels algorith msegments square regions for feature extraction, but is applied on the binary image to find the boundarysome of them segment circular [9] and half of palmprint image. The starting point is theelliptical reg ions [10]. bottom left point “Ps” as shown in figure 2 and the tracing direction is counter clockwise. The end Generally there are t wo kind of images point is also “Ps”. And these boundary pixels areutilized in palm-p rint recognition: Online and collected in Boundary pixel vector (BPV).Offline. On line images are those taken with digital 3. Euclidean distance is calculated between BPVcameras or scanners. Offline ones are those and Ps with formu laproduced by ink on paper [11]. The database we DE (i) = (Xp − Xb (i) + (Yp – Yb (i))utilize for testing our method is PolyU [12] that (1)utilizes online images. The images in this database where ( Xp , Yp ) are the X and Y co-ord inates ofare low-resolution ones and are suitable for real- the Ps ( Xb(i), Yb(i) ) is the co-ordinate of thetime application testing. A sample of the images border pixel, and DE (i) is the Euclid ian distancefro m database is shown in Figure 1. between Ps and Ith border pixel. A Distance distribution diagram shown in figure 3 is The rest of this paper is organized as constructed using the vector DE. The constructedfollows: Section 2 prov ides proposed Dynamic diagram pattern is similar to geometric shape ofROI ext raction method. Section 3 discusses the the palm. In the figure 3, three local minima andexperimental results. Finally Conclusions are four local maxima can be visually perceived whichpresented in section 4. resembles the four-finger tips (local axima) and four finger webs (local min ima) i.e. valley between fingers. 4. The first and the third finger web point is taken and the slope joining this two lines is calculated utilizing formu la tan α =Y/X, (2) where Y= y 1-y 3, X= x1-x3, (x1, y 1) & (x3, y 3) are the co-ordinates of FW1 & FW3 finger web point respectively, α is the slope of the line. Figure 1: Image of Poly U database Table 1: Notation used in this paper FW Figure web point X x-coordinate of boundary pixels Y y- coordinate of boundary pixels Xb x-coordinate of border p ixel Yb y-coordinate of border pixel Figure 2: Boundary pixels of palm image Ps Starting point in the image Xp x-coordinate of P Yp y-coordinate of PII. 2. Proposed Methodology For Palm Extraction Figure 3 : Distance distribution diagram Image prepossessing is conventionally the 2.2 Dynamic ROI Extractionfirst and essential step in pattern recognition. In The following steps are performed to ext ract thethis paper a Method [13] is adopted which uses ROI.finger webs as the datum points to develop an 1. The image is then rotated at an angle α to alignapproximate Region OF Interest to which changes the straight line joining FW3(x3, y3) & FW1(x1,are made to surmount the limitations of existing y1) with the horizontal axis as shown in figure The IJES Page 249
  3. 3. Extraction of Dynamic Region of Interest (ROI) for Palmprint using Templates Databases Figure 8: Boundary of Binary image N plottedFigure 4: Image Q after rotation with finger web using b1 & b2 matrices point2. A fter rotation, we reiterate step 1 to 5 of section2.2 are applied to get finger web points of therotated image as the co-ordinates of finger webpoints changes after rotation. The finger websafter rotation are named as FR1, FR2 and FR3.3. Now boundary tracing algorith m is applied on 4. For Width: The maximu m Y-coordinate in thethe binary image figure 5 and X & Y co-ord inates b2- mat rix is calculated using (3)of all the boundary pixels are stored in different Ym=max (b2)-kmatrices. X co-ordinate values of boundary pixels (3)are stored in b1-matrix and Y co-ordinate values where k=15 is chosen empirically forare stored in b2-matrix. Plots between b1-matrix experimental purpose. Then for th is new Ym there(X-co-ordinate) and boundary pixels and b2-matrix will be two X coordinates (say X1 and X2) on theand boundary pixels is shown in figure 6 and boundary as shown in figure 9 and can be foundfigure 7 respectively. The boundary of a binary fro m matrix b 1 wh ich is show in the figure 10aimage obtained by drawing a plot between b1- and 10b. Now width of ROI is calculated using (4).matrix and b2-matix and shown in figure 8. as shown in W idth = abs(X1-X2) (4) Figure 5: Binary Image Dimension Figure 9: Plotting X1 & X2 on boundary plot and inverted Figure 6 : Plot of X co -ordinates (b1-matrix) (a)against the boundary pixels (b) Figure 10: a) Max Y-Coordinate and b) X1 and Figure 7 : Plot of Y co-ord inates (b2-matrix) X2 valuesagainst the boundary pixels For Height: To calculate the height we require maximu m Y-coordinate (Ymax) and minimu m Y- coordinate(Ymin ).The Ymax can be calculated utilizing (5) by subtracting it fro m P which is the length of the The IJES Page 250
  4. 4. Extraction of Dynamic Region of Interest (ROI) for Palmprint using Templates Databases demonstrates that fine-tuned size ROI cover 1. diminutively minuscule area and valued Ymax=P-Ym (5) informat ion is missed where as dynamic size ROI extracts maximu m size ROI and 99.9% ROIsThe Ymin can be calcu lated utilizing (6) by find without background information.the minimu m Y-coordinate out of all three web The ROI images we obtained fro m eachpoints after complementing it with the length of palm image had maximu m Size of ROI 201* 174image. and minimu m Size of ROI 137*163 shown in figure 13.Ymin = min( P-y1,P-y2 ,P-y 3) (6)where y1,y2 and y3 are y-coordinate of web pointsFR1,FR2 and FR3 respectively.Height is the distinguishment between Ymax andYmin and is calculated utilizing (7) shown infigure 11. Height=abs(Ymax-Ymin ) (7) Figure 11: For the lo west left most point of rectangle Palmp rint Image Size of ROI 201 * 1746. We have calculated height and width of palmprint image. Now, to get maximu m ROI Square (a)region we require top left most point and lowestright most point, vividly it will be (X1, Ym) rightmost points and (X1, P-Ym) as the lowest leftmostpoint. The Dynamic ROI extracted is shown infigures 12. Figure 12: Palmp rint Images and corresponding Dynamic ROI Extracted III. Experi mental Result We experimented our approach on HongKong Polytechnic University Palmprint database Palmp rint Image Size of ROI 137 * 163[12].The database was acquired at Hong KongPolytechnic University (Ch ina) utilizing camera. (b)In its current version the database contains, Figure 13: a) Palmprint Images and corresponding Maximu m size Dynamic ROI Ext racted (201 *7752(8-b it) grey-scale images corresponding to386 subjects. The experiment has been performed 174)on a system of 2.0GHz CPU and 256 MB of RAM. b) Palmp rint Images and corresponding Minimu m size Dynamic ROI Ext racted (137 *Most of the researchers [13-18] ut ilized the PolyUPalmp rint database [12] and they ext racted fine- 163)tuned size 128* 128 ROI. Result of the proposedAlgorith m are co mpared with fine-tuned size ROIextraction Algorithm[13].The The IJES Page 251
  5. 5. Extraction of Dynamic Region of Interest (ROI) for Palmprint using Templates Databases VI. Conclusions international conference on audio and video Palm segmentation is the key step in based biometric person authentication, pp.palmprint recognition system. Seg mentation of 668-678, 2003.palm includes separation of palm which is in [9] A. Kumar & D.Zhang “Integrating Shapebetween the wrist and fingers of hand images. In and texture for hand verification” inthis paper, we propose a Dynamic ROI extract ion Proceedings of Third Internationaltechnique depending upon the size of the image. Conference on Image & Graph ics, pp. 222-The proposed method extracts maximu m possible 225, 2004.ROI region without background informat ion when [10] C. Poon.D.C.M. Wong and H.C. Shen “ Acompared to the existing fixed ROI extract ing New Method in Locating and Segmentingtechniques [13-18] .We found that the efficiency palmprint into region-of-interest”, inof our proposed approach agrees with the other proceedings of the 17th Internationalsystems in the state of art and is better for the Conference on Pattern Recognition, vol. 4,future feature extract ion and matching. pp. 533-536, 2004 [11] D. Zhang, W. Shu, “Two NovelReferences Characteristics in Palmp rint verification:[1] J.S. Noh, K.H. Rhee, “Palmprint datum point invariance and line matching”, identification algorith m using Hu invariant Pattern Recogn., vol. 32 pp. 691-702, 1999. mo ments and Otsu binarization”, in: [12] The Hong Kong Polytechnic University Proceeding of Fourth Annual ACIS PolyU Palmprint Database, International Conference on Computer and metrics . Information Science, 2005, pp. 94–99. [13] Lin, C-L., Chaung, T.C. and Fan, K-C.[2] J. Doublet, M. Revenu, O. Lepetit, “Robust (2005) „Palmp rint verification using grayscale distribution estimat ion for hierarchical deco mposition‟, Pattern contactless palmprint recognition”, in: Recognition, Vo l. 38, No. 12, pp.2639– Proceedings of the First IEEE International 2652(2005). Conference on Bio metrics: Theory, [14] Chen, J., Moon, Y., Wong, M., Su, G.: Applications, and Systems, 2007, pp. 1–6. Palmp rint authentication using a symbolic[3] D. Zhang, W.K. Kong, J. You, M. Wong, representation of images. Image and Vision “On-line palmp rint identification”, IEEE Co mputing 28, 343– 351 (2010) Transactions on Pattern Analysis and [15] Huang, D., Jia, W., Zhang, D.: Palmprint Machine Intelligence 25 (9) (2003) 1041– verification based on principal lines. Pattern 1050. Recognition 41, 1316– 1328 (2008)[4] W. Li, D. Zhang, Z. Xu, “Palmprint [16] Jia, W., Huang, D., Zhang, D.: Palmprint identification by Fourier transform”, verification based on robust line orientation International Journal of Pattern Recognition code. Pattern Recognition 41, 1504–1513 and Artificial Intelligence 16 (4) (2002) (2008) 417–432. [17] Sun, Z., Tan, T., Wang, Y., Li, S.Z.: Ord inal[5] X. Wu, K. Wang, D. Zhang, “HMMs bas ed Palmp rint Representation for Personal palmprint identification”, in n Proceedings Identificat ion. In: Proceedings of IEEE of ICBA. 2004, vol. 3072, 2004, pp. 775– International Conference on Co mputer 781. Vision and Pattern Recognition, vol. 1, pp.[6] C.C. Han, “A hand-based personal 279–284 (2005) authentication using a coarse-to-fine [18] Zhang, D., Kanhangad, V., Luo, N., Ku mar, strategy”, Image and Vision Co mputing 22 A.: Robust palmprint verification using 2D (11) (2004) 909– 918. and 3D features. Pattern Recognition 43,[7] C.C. Han, H.L. Cheng, C.L. Lin, K.C. Fan, 358–368 (2010) “Personal authentication using palm-print [19] CASIA Palmprint Database, features”, Pattern Recognition 36 (2) (2003) 371–381. l[8] A. Ku mar, D.C.M. Wong, H.C. Shan and [20] IIT Delh i Touchless Palmprint Database A.K. Jain, “Personal verification is using version 1.0, palmprint & hand geometry biometric”, in Database_Palm AVBPA 2003, proceedings of 4th The IJES Page 252