Feature extraction using crossing number (cn) and ridge tracking technique

Feature extraction utilise crossing tot (cn) and rooftree tracking techniquePROPOSED ALGORITHMFEATURE EXTRACTION USING CROSSING bet (CN) AND RIDGE TRACKING TECHNIQUEThe various steps involved in feature extraction atomic number 18 as disposed below3.2.1 adjustive BINARIZATIONThe enhanced greyscale run across is converted to a binary kitchen stove use adaptive binarization 1. Global thresholding is non used for binarization because of possibilities of non-uniform illumination on the progress of scanner. Thus victimisation adaptive binarization with a window size of 91 x 91 (This size was finalised after a number of attempt and errors). The algorithmic rule can be outlined as followsAlgorithm adaptative binarizationInput Enhanced greyscale cooking stove e(x,y).Output Binarized mental image bin(x,y).For severally pel (i) of e(x,y)Compute local mean (ml) in the 91 x 91 neighborhood of the picture element.If ml e(xi,yi) so, bin(xi,yi) = white. Else bin(xi,yi)= blac k.End For.-3.2.2. THINNINGThe binarised image is skeletonised using mesial axis shift key (MAT)1 to obtain a single pixel hack ridgeline structure. The thinning algorithm can be outlined as followsAssumptionsRegion bear witnesss atomic number 18 put on to suffer value 1(white) and background meridians to have value 0(black).Notations1. The 8 neighbour notation of a centre pixel p1 is as shown.p9p2p3p8p1p4p7p6p52. n (p1) is the number of non zero neighbours of p1. I.e. n (p1) = p2 + p3 + . + p9.3. t (p1) is the number of 0-1 transitions in the ordered sequence p2, p3,p9,p2.Algorithm ThinningInput Binarized image bin(x,y).Output One pixel thinned image th(x,y).Steps 1. W.r.t the neighborhood notation a pixel p1 in bin(x,y). is flagged for cutting off if the following conditions are satisfied2 n(p1) 6 .t(p1)=1.p2 V p4 V p6 = 0p4 V p6 V p8 = 02. Delete all the flagged pixels from bin(x,y).3. W.r.t the neighborhood notation a pixel p1 in bin(x,y) is flagged for deletion if t he following conditions are satisfied2 n(p1) 6 .t(p1)=1.p2 V p4 V p8 = 0p2 V p6 V p8 = 04. Delete all the flagged pixel from bin(x,y).5. Go to step 1 if bin(x, y) is not same as the previous bin(x, y) (indicating that single pixel thickness is blush not obtained)6. Assign the image bin(x, y) obtained from step 4. to th(x, y).Thus one iteration of the thinning algorithm consists ofapplying step 1 to flag border points for deletiondeleting the flagged pointsapplying step 3 to flag the remaining border points for deletion anddeleting the flagged points.The canonic procedure is applied iteratively until no further points are deleted, at which condemnation the algorithm terminates, yielding the skeleton of the region.3.2.3 ESTIMATING SPATIAL CO-ORDINATES DIRECTION OF MINUTIAE POINTS.Minutiae representation is by far, the approximately widely used manner of fingerprint representation. Minutia or abject details mark the regions of local discontinuity within a fingerprint image. These are locations where the the ridge comes to an end( vitrine ridge ending) or branches into two (type bifurcation). Other forms of the minutiae includes a very short ridge (type ridge dot), or a closed loop topology (type enclosure).The different types of minutiae are illustrated Figure 1. There are more than 18 different types of minutiae 2 among which ridge bifurcations and endings are the most widely used. Other minutiae type may simply be expressed as multiple ridge endings of bifurcations. For instance, a ridge dot may be delineated by two opposing ridge endings placed at either extremities. flat this simplification is redundant since many matching algorithms do not even distinguish between ridge ending and bifurcations since their types can get flipped.The template simply consists of a list of minutiae location and their orientations. The feature extractor takes as input a gray scale image I(x,y) and produces a garbled set of tuples- M = m1,m2,m3mN.Each tuple mi co rresponds to a single minutia and represents its properties. The properties extracted by most algorithms include its face and orientation. Thus, all(prenominal) tuple mi is usually represented as a triplet xi, yi, i. The crossing number (CN) method is used to perform extraction of the spatial coordinates of the minutiae points. This method extracts the bifurcations from the skeleton image by examining the local neighborhood of each ridge pixel using a 33 window. The CN of a ridge pixel p is addicted as followsCN=0.5 i=18pi-pi+1 p(9) =p(1) .For a pixel p if CN= 3 it is a bifurcation point. For each extracted minutia along with its x and y coordinates the orientation of the associated ridge segment is also recorded. The minutia military commission is found out using a ridge tracking technique. With reference to figure 3.3 once the x and y coordinates of the bifurcation point are known, we can track the three directions from that point. Each direction is tracked upto 10 pixel len gth. Once tracked we construct a triangle from these three points. The midpoint of the smallest side of the triangle is then affiliated to the bifurcation point and the angle of the resulting line segment is found which is the minutia direction.AssumptionsRidges are assumed to have value 0 (black) and background points to have value 1(white).NotationsThe 8 neighbor notation of a center pixel p1 is as antecedently shown.The algorithm for extracting the minutiae using the crossing number technique can be outlined as followsAlgorithm Crossing numberInput vitiated image th(x,y).Output date with (x,y) coordinates and orientation thita of each minutia.Steps 1. For any pixel p in th(x,y) compute the crossing number (CN) CN=0.5 i=18pi-pi+1 p(9) =p(1) .2. If CN= 3, the pixel p is declared as a bifurcation point and its x and y coordinates, i.e. p.x and p.y are recorded.3. The orientation at the bifurcation points p. is calculated using tracking algorithm.Fingerprint matching Process-Ea ch minutiae may be described by a number of attributes such as its purview (x,y), its orientation , its quality etc. However, most algorithms consider only its position and orientation information. Given a pair of fingerprints and their corresponding minutiae features to be matched, features may be represented as an illogical set given byI1 = m1,m2.mM where mi = (xi, yi, i)I2 = m1,m2.mN where mi = (xi, yi , i )hither the objective is to find a point mj in I2 that exclusively corresponds to each point mi in I1. Usually points in I2 is related to points in I1 through a nonrepresentational transmutation T( ). and so, the technique used by most minutiae matching algorithms is to ascertain the commuteation function T( ) that maps the two point sets . The resulting point set I2 is given byI2 = T(I1) = m1,m 2,m 3.mMm1 = T(m1)m N = T(mN)The minutiae pair mi and mj are considered to be a match only if(xi-xj)2+(yi-yj)2r0min( i j , 360 i j ) Here r0 and 0 denote the tolerance wind ow.The matcher can make on of the following assumptions on the nature of the transformation TRigid edition Here it is assumed that one point set is rotated and shifted version of the other(a).Affine Transformation Affine transformations are generalization of Euclidean transform. hurl and angle are not preserved during transformation.Non-linear Transformation Here the transformation may be due to any arbitrary and complex transformation function T(x,y).The problem of matching minutiae can be treated as an instance of generalized point human body matching problem. In its most general form, point pattern matching consists of matching two unordered set of points of possibly different cardinalities and each point. It is assumed that the two pointsets are related by some geometric relationship. In most situations, some of the point correspondences are already known (e.g. control points in an image registration problem 5,4,6,7)andthe problem reduces to finding the most optimum geometr ical transformation that relates these two sets. However, in fingerprints, the point correspondences themselves are unknown and wherefore the points have to be matched with no precedent assumption making it a very challenging combinatorial problem. There have been several prior mountes where general point pattern techniques havebeen applied. Some of these have been discussed here.Ranade and Rosenfield 8 proposed an iterative approach for obtaining point correspondences. In this approach, for each point pair mi, mj they assign pij , the likeliness of the point correspondence and c(i, j, h, k), a cost function that captures the correspondence of other pairs(mh,m_k) as a result of matching mi with mj. In each iteration pij is incremented if it increases the compatibility of other points and is decremented if it does not. At the point of convergence, each point mi is assigned to the point argmaxk(pik). While this is a fairly accurate approach and is robust to non-linearities, the i terative nature of the algorithm makes it unsuitable for most applications.The hough transform 9 approach or the transformation clustering approach reduces the problemof point pattern matching to detecting the most probable transformation in a transformation search space. Ratha et al 10 proposed a fingerprint matching algorithm based on this approach. In this technique, the search space consists of all the realistic parameter under the assumed distortionmodel. For instance, if we assume a rigid transformation, then the search space consists of all possible combinations of all translations (x,y) , scales s and rotations and . However, to avoid computation complexity the search space is usually discretized into small cells. Therefore the possible transformations form a finite set withx 1x,2x . . .Ixy 1y,2y . . .Jy 1, 2 . . . Ks s1, s2 . . . sLA four dimensional accumulator of size (I J K L) is maintained. Each cell A(i, j, k, l) indicatesthe likelihood of the transformation para meters (ix,jy, k, sl). To determine the optimal transformation, every possible transformation is try on each pair of points. The algorithm used is summarized belowfor each point mi in fingerprint T. for each point m_j in fingerprint Ifor each k 1, 2 . . . Kfor each sl s1, s2 . . . sLcompute the translations x,yExplicit alignment An illustration of the relation alignment using ridges associated with minutiae mi and mjxy=xiyi-s1cosk -sinksink coskxjyj (1)d Let (ix,jy) be the quantized versions of (x,y) respectively.e If Tmi matches with m_j increase the evidence for the cell Aix,jy, k, slAix,jy, k, sl = Aix,jy, k, sl+13.The optimal transformation parameters are obtained using(*x,*y, *, s*) = argmax(i,j,k,l) Aix,jy, k, slReferencesGonzalez, Woods, and Eddins. Digital Image bear upon using matlab. Prentice Hall, 2004.D. Maltoni, D. Maio, A.K. Jain, S. Prabhakar, Handbook of Fingerprint Recognition, Springer, 2003, ISBN 0-387-95431-7.R.Thai, Fingerprint image enhancement and featu re extraction. Australia.Anil Jain, Salil Prabhakar, Lin Hong, and Sharath Pankanti. Filterbank-based fingerprint matching. In Transactions on Image Processing, volume 9, pages 846-859, May 2000.Anil Jain, Arun Ross, and Salil Prabhakar. Fingerprint matching using minutiae caryopsis features.In International Conference on Image Processing, pages 282-285, october 2001.L. Hong, Y. Wang, and A. K. Jain. Fingerprint image enhancement Algorithm and performanceevaluation. Transactions on PAMI, 21(4)777-789, August 1998.L. Brown. A survey of image registration techniques. ACM Computing Surveys, 1992.A. Ranade and A. Rosenfeld. Point pattern matching by relaxation. signifier Recognition, 12(2)269-275, 1993.R. O. Duda and P. E. Hart. Use of the hough transformation to detect lines and curves in pictures. Communications of the ACM, 15(1), 1972.N. K. Ratha, K. Karu, S. Chen, and A. K. Jain. A real-time matching system for large fingerprint databases. Transactions on var. Analysis and Machi ne Intelligence, 18(8)799-813, 1996.