FINGER PRINTINGS

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  • 1. FINGERPRINT BASED PERSON VERIFICATION IN SPATIAL DOMAIN USING MATLAB CHAPTER 11 INTRODUCTION As our everyday life is getting more and more computerized, automated security systemsare getting more and more important. Today, most of the banking transactions can be performedover the Internet and soon they can also be performed on mobile devices such as cell phones andPDAs. This rapid progress in wireless communication system, personal communication systemand smart card technology in our society makes information more susceptible to abuse. Due tothe growing importance of the information technology and the necessity of the protection andaccess restriction, reliable personal identification is necessary. The key task of an automated security system is to verify that the users are in fact whothey claim to be. There are three main methodologies when performing this verification. Thesecurity system could ask the user to provide some information known only to the user, it couldask the user to provide something only the user has access to or it could identify some sort oftrait that is unique for the user. Identifying some trait that is unique for the user is known asbiometric security.[6] A biometrics system is a pattern recognition system that establishes theauthenticity of a specific physiological or behavioral characteristic possessed by a user. Fingerprint biometric is an automated digital version of the old ink-and-paper methodused for more than a century for identification, primarily by law enforcement agencies. Thebiometric device requires each user to place a finger on a plate for the print to be read.Fingerprint biometrics currently has three main application areas: large-scale Automated FingerImaging Systems (AFIS) generally used for law enforcement purposes; fraud prevention inentitlement programs; and physical and computer access. A major advantage of finger imaging isthe longtime use of fingerprints and its wide acceptance by the public and law enforcementcommunities as a reliable means of human recognition. Others include the need for physicalcontact with the optical scanner, possibility of poor-quality images due to residue on the fingersuch as dirt and body oils (which can build up on the glass plate), as well as eroded fingerprintsfrom scrapes, years of heavy labor or mutilation.[1]Dept. 0f ECE, MCE, HASSAN Page 1
  • 2. FINGERPRINT BASED PERSON VERIFICATION IN SPATIAL DOMAIN USING MATLABWe are introducing two domains for the processing of an image. They are1. Spatial domain2. Frequency domain1. Spatial domain refers to the image plane itself, and approaches in this category are basedondirect manipulation of pixels in an image. The term spatial domain refers to the aggregate ofpixels composing an image. Spatial domain methods are procedures that operate directly on thesepixels. Spatial domain processes will be denoted by the expression[9]g(x,y)=T[f(x,y)];Where f(x,y) is the input image, g(x,y) is the processed image, and T is an operator on f, definedover some neighborhood of (x,y);2. Frequency domain processing techniques are based on modifying the Fourier transformof animage.Enhancement techniques based on various combinations of a method from these two categoriesare not usual. There is no general theory of image enhancement. When animate is processed forvisual interpretation, the viewer is the ultimate judge of how well particular method works.Visual evaluation of image quality is a highly subjective process, thus making the definition of a“good image” an elusive standard by which to compare algorithm performance.1.1 Biometric classification Biometric characteristics can be divided in two main classes, as represented in the followingfigure:[2] Physiological are related to the shape of the body. Examples include, but are not limitedto fingerprint, face recognition, hand and palm geometry and iris recognition. Behavioral are related to the behavior of a person. Characteristic implemented by usingbiometrics are signature verification, keystroke dynamics, and voiceDept. 0f ECE, MCE, HASSAN Page 2
  • 3. FINGERPRINT BASED PERSON VERIFICATION IN SPATIAL DOMAIN USING MATLABFig1.1 Biometric classification1.2 Characteristics of Biometrics: To compare the relative merits of fingerprint as a biometric, we consider the followingcharacteristics of what constitute a good biometric:1. Universality - each person has the characteristic2. Uniqueness - the characteristic is unique per person3. Permanence - characteristic remains the same over time4. Collectability - how easy is it to measure the characteristic5. Performance - accuracy, speed, and resource requirements6. Acceptability - culturally accepted by the population7. Circumvention - robust against fraudulent attacksDept. 0f ECE, MCE, HASSAN Page 3
  • 4. FINGERPRINT BASED PERSON VERIFICATION IN SPATIAL DOMAIN USING MATLAB1.3 Applications of biometrics1. Commercial applications such as computer network login, electronic data security, e-commerce, Internet access, ATM or credit card use, physical access control, mobile phone, PDA,medical records management, distance learning, etc.[3]2. Government applications such as national ID card, managing inmates in a correctional facility,driver’s license, social security, welfare-disbursement, border control, passport control, etc.3. Forensic applications such as corpse identification, criminal investigation, parenthooddetermination, etc1.4 Biometric fingerprint Fingerprint biometric is an automated digital version of the old ink-and-paper method usedfor more than a century for identification, primarily by law enforcement agencies. The biometricdevice requires each user to place a finger on a plate for the print to be read. Fingerprintbiometrics currently has three main application areas: large-scale Automated Finger ImagingSystems (AFIS) generally used for law enforcement purposes; fraud prevention in entitlementprograms; and physical and computer access. A major advantage of finger imaging is the long-time use of fingerprints and its wide acceptance by the public and law enforcement communitiesas a reliable means of human recognition. Others include the need for physical contact with theoptical scanner, possibility of poor-quality images due to residue on the finger such as dirt andbody oils (which can build up on the glass plate), as well as eroded fingerprints from scrapes,years of heavy labor or mutilation.[4] Fingerprints are produced by sweat glands in the fingertip that coats the ridges of thefingerprint. This solution leaves behind a facsimile of the fingertip ridges called a latent print,when it comes in contact with a surface. In fingerprint literature, the terms ridges and valleys areused to describe the higher and lower parts of the papillary lines that we can see on our fingertip.The frictional ability of the skin is the reason we have ridges and valleys on our fingers. A fetus’Dept. 0f ECE, MCE, HASSAN Page 4
  • 5. FINGERPRINT BASED PERSON VERIFICATION IN SPATIAL DOMAIN USING MATLABfingerprint ridges are fully developed at the age of seven month. After formation, an infant’sgrowing fingerprint ridges are like drawing a face on a balloon with a ballpoint pen and theninflating the balloon to see the same face expand uniformly in all direction. This means thecharacteristic of the fingerprint does not change throughout the lifetime except for injury, diseaseor decomposition after death. However after a small injury on the fingertip, the pattern will growback as the fingertip heals.[5] Sir Francis Galton, a British anthropologist scientifically proved in the late 19th century thatno two fingerprints are exactly alike. According to his calculations, the odds of two individualfingerprints being the same are 1 in 64 billion. No identical twins will have the same fingerprints.A fingerprint can be looked at from different levels: the global level, the local level and the veryfine level. At the global level, you find the singularity points, called core and delta points. Thesesingularity points are very important for fingerprint classification, but they are not sufficient foraccurate matching. Figure 2.1 shows the core and delta points of two fingerprint’s pattern; loopand whorl. Loops have one delta, whorl have two. Fig 1.2: Core and delta points 2.2 show a fingerprint image with sweat pores and minutiae points visible. The black linescorrespond to the ridges in the fingerprint and the white line corresponds to the valley. The whitedots in the ridges correspond to the sweat pores and are marked with empty circles on a singleridge line. Minutiae details are marked with black-filled circles. In order to establish comparisonbetween fingerprint images, AFIS’s often rely on procedures based on local features. Globalfeatures are mainly employed to reduce the computational cost associated to fingerprintcomparison procedure.Dept. 0f ECE, MCE, HASSAN Page 5
  • 6. FINGERPRINT BASED PERSON VERIFICATION IN SPATIAL DOMAIN USING MATLAB Figure 1.3: Sweat pores1.5 Fingerprint classification There are three main structures that make up fingerprints. These are loops, whorls and arches.[7]Loops Loops are comprised of one or more ridges entering from one side, curving, and then goingout the same side it entered. The ridges in loops double back on themselves. All loops haveelements called a delta and a core. The delta is a triangular area usually shaped like a T-junction,while a core is the centre of the pattern. About 65% of fingerprints have loops.Loops can be divided into two groups:Radial loops – these flow downward and toward the radius (or the thumb side) Fig.1.4 Radial loopUlnar loops – which flow toward the ulnar (or the little finger side). The ulnar loop is morecommon. Fig.1.5 Ulnar loopDept. 0f ECE, MCE, HASSAN Page 6
  • 7. FINGERPRINT BASED PERSON VERIFICATION IN SPATIAL DOMAIN USING MATLABWhorls Whorls have a circular pattern and have at least two deltas and a core. Whorls look a little liketarget shapes or whirlpools – circles within circles. Whorls make up 35% of patterns seen inhuman fingerprints and can be sub-grouped into four categories:Plain whorls – which are either concentric circles like a bull’s eye or spirals like a woundspring. Fig.1.6 Plain whorlCentral pocket loop whorls – these resemble a loop with a whorl at its end. Fig.1.7 Central pocket loopDouble loop whorls – these occur when two loops collide to produce an “S” shaped pattern. Fig.1.8 Double loopAccidental loop whorls – these are slightly different from other whorls and are irregular. Fig.1.9 Accidental loop whorlArchesArches are the least common pattern making up only 5% of all pattern types. Arches areridgelines that rise in the centre and create a wave like pattern. The ridges enter from one sideand exit the other side with a rise in the middle. They do not have a delta or a core and can bebroken into two sub-groups:Dept. 0f ECE, MCE, HASSAN Page 7
  • 8. FINGERPRINT BASED PERSON VERIFICATION IN SPATIAL DOMAIN USING MATLABPlain arch – which has a gentle rise. Fig.1.10 Plain archTented arches - have a steeper rise than plain arches. Fig.1.11Tented arches1.6 Objective of the project From the discussion above, this project has set two objectives. 1. To design and develop a fingerprint biometric template system that can process every fingerprint image inserted by the user. 2. To implement the fingerprint biometric template system in GUI of MATLAB software.1.7 Methodology 1. Input fingerprint images are stored in an image repository on the host pc. In software development, this project uses 256 gray-sales bitmap images with sizes of 400 pixels x 500 pixels as a test vector. . 2. The Fingerprint Biometric Template will process and enhance the image at the image processing stage. 3. A simple matching system using point matching is designed to validate the system.Dept. 0f ECE, MCE, HASSAN Page 8
  • 9. FINGERPRINT BASED PERSON VERIFICATION IN SPATIAL DOMAIN USING MATLAB1.8 Literature surveyThere is archaeological evidence that fingerprints as a form of identification have been used atleast since 7000 to 6000 BC by the ancient Assyrians and Chinese. Clay pottery from these timessometimes contain fingerprint impressions placed to mark the potter. Chinese documents bore aclay seal marked by the thumbprint of the originator. Bricks used in houses in the ancient city ofJericho were sometimes imprinted by pairs of thumbprints of the bricklayer. However, thoughfingerprint individuality was recognized, there is no evidence this was used on a universal basisin any of these societies. In the mid-1800’s scientific studies were begun that would establishedtwo critical characteristics of fingerprints that are true still to this day: no two fingerprints fromdifferent fingers have been found to have the same ridge pattern, and fingerprint ridge patternsare unchanging throughout life. These studies led to the use of fingerprints for criminalidentification, first in Argentina in 1896, then at Scotland Yard in 1901, and to other countries inthe early 1900’s. 1.9 ORGANIZATION OF THE REPORTThis project gives information about the identification of person by using fingerprint in specialdomain using matlab code.chapter 1 covers the introduction of the project and classification of fingerprint and meaning ofthe fingerprintChapter 2 covers the implementation of the finger print in special domain and description of theblock diagramChapter 3 covers the experimental results ,applications, advantages and challengesChapter 4 covers the conclusion of this project and referencesDept. 0f ECE, MCE, HASSAN Page 9
  • 10. FINGERPRINT BASED PERSON VERIFICATION IN SPATIAL DOMAIN USING MATLAB CHAPTER 22 IMPLIMANTATION IN SPATIAL DOMAINSpatial domain refers to the image plane itself, and approaches in this category are based ondirect manipulation of pixels in an image. The term spatial domain refers to the aggregate ofpixels composing an image. Spatial domain methods are procedures that operate directly on thesepixels. Spatial domain processes will be denoted by the expression[2]g(x,y)=T[f(x,y)];Where f(x,y) is the input image, g(x,y) is the processed image, and T is an operator on f, definedover some neighborhood of (x,y);2.1 Block DiagramDept. 0f ECE, MCE, HASSAN Page 10
  • 11. FINGERPRINT BASED PERSON VERIFICATION IN SPATIAL DOMAIN USING MATLAB Fig.1.12 Block Diagram for fingerprint verification in Spatial Domain2.2 Image Acquisition The task of acquisition is to enroll persons and their fingerprints into the system. When thefingerprint images and the user name of a person to be enrolled are fed to the processing, theimages will be enhanced and thinned at the image processing stage. The following code is usedfor image registration which is present in the address ’c:Documents andsettingsAdminDesktopfolder nameImage name’.[6]a=imread (C:Documents and SettingsAdminDesktopab.bmp);figure (1), imshow (a);*NOTE: The finger print images must be present in the above mentioned address. In the abovecode ‘a indicates the folder name and ‘b.bmp’ indicates the image name which is in .bmp format.2.3 Low pass filteringDept. 0f ECE, MCE, HASSAN Page 11
  • 12. FINGERPRINT BASED PERSON VERIFICATION IN SPATIAL DOMAIN USING MATLAB We are convolving the output of the previous process with a properly defined masking matrixis:[4] h= (1/25)*ones (5, 5); A low pass filter is the basis for most smoothing methods. An image is smoothed bydecreasing the disparity between pixel values by averaging nearby pixels (see Smoothing anImage for more information). Using a low pass filter tends to retain the low frequency information within an image whilereducing the high frequency information. An example is an array of ones divided by thenumber of elements within the kernel, such as the following 3 by 3 kernel:(The above array is an example of one possible kernel for a low pass filter. Other filters may includemore weighting for the center point, or have different smoothing in each dimension)The acquired image is passed through low pass filter to reduce the noise for further processing.We are convolving the input image with a properly defined masking matrix is h= (1/25)*ones (5, 5); Image filtering is useful for many applications, including smoothing, sharpening,removing noise, and edge detection. A filter is defined by a kernel, which is a small array appliedto each pixel and its neighbors within an image. In most applications, the center of the kernel isaligned with the current pixel, and is a square with an odd number (3, 5, 7, etc.) of elements ineach dimension. The process used to apply filters to an image is known as convolution, and maybe applied in either the spatial or frequency domain. Noise elimination is the process that removes all the undesired pixels in the image (blackpixel that occur as noise in the image). These undesired images can destroy the quality of theDept. 0f ECE, MCE, HASSAN Page 12
  • 13. FINGERPRINT BASED PERSON VERIFICATION IN SPATIAL DOMAIN USING MATLABimage and thus will reduce the ability and accuracy of the feature extraction process. Theseundesired pixels are meaningless and can create false minutiae in minutiae extraction process. The sizes of the structuring elements that have been used are 5 x 5 structuring elementand 3 x 3 structuring elements. Each structuring elements has its own condition for identifyingthe noise. LOW PASS FILTERING MODULE Fig 1.12 low pass filtering process2.4 Binarization Binarization is the process that converts a gray scale image, which has 256 of gray-level (0 to255) to a black and white (0 and 1) binary images. This is important because binary images arevery simple to store and manipulate, as each pixel is represent by a single bit. The binary imagesare also very easy to generate compared to a gray scale image.[7] To convert a gray scale image to a binary image is not an easy task. A reasonable thresholdto separate the black pixel from the white pixel is very difficult to find. This is because for agiven gray value, it can represent ridges in some area but it may represent valley in the otherarea. The average gray value can be used as a threshold value for the Binarization process. If thegray value of a particular pixel is lower than the threshold value, then the new value assigned forthat pixel is ‘0’(representing the ridges), else the new value is set to ‘0’(representing the valleys). Where Vmean is the average gray value, V(x,y) is the gray value of the particular pixel and r.cis the total pixels in the image.Dept. 0f ECE, MCE, HASSAN Page 13
  • 14. FINGERPRINT BASED PERSON VERIFICATION IN SPATIAL DOMAIN USING MATLAB BINARIZATION 1.13 Binarization process2.5 Thinning Thinning process is used to skeletonized the binary image by reducing all lines to a singlepixel thickness. There are two main approaches to find the skeleton of a binary region. The firstapproach basically calculates the distance to the region edge for all pixels belonging to theregion. Then those pixels that have the largest distance to the region edge are selected to belongto the region skeleton. Although the approach is simple and yields an intuitively pleasingskeleton the method is seldom implemented in its pure form since it is very computationallyexpensive. The second approach instead iteratively deletes edge point pixels from the region until just theskeleton remains. For a pixel to be deleted, the following conditions must hold• The pixel is not an endpoint.• The removal does not break the connectedness of the skeleton.• The removal does not cause excessive erosion of the region. The method used in this system takes on the second approach. It uses a modified versionof the thinning algorithm first suggested by Zhang and Suen(Zhang and Suen, 1984). Themethod consists of removing all pixel of the image except those pixels that belong to theskeleton. In order to preserve the connectivity of the skeleton, all iteration is divided into twosubiterations. A pixel p0 is defined to have at least one pixel in its eight-connectivityneighborhood that belongs to the background. A pixel is marked for deletion in the firstsubiteration if all of the following conditions hold for its eight-connectivity neighborhood: • The number of neighbors that belong to the region must be between two and six. This ensures that the endpoint pixels of a skeleton line are preserved. • All neighbors that belong to the background must be connected. This prevents the deletion of those pixels that lie betweens the end point pixels of a skeleton line.Dept. 0f ECE, MCE, HASSAN Page 14
  • 15. FINGERPRINT BASED PERSON VERIFICATION IN SPATIAL DOMAIN USING MATLAB • p1, p3 or p5 must belong to the background. • p3, p5 or p7 must belong to the background. If all conditions are met, the point is marked for deletion. However, the point is not deleteduntil all region points are processed to ensure that the data structure is not changed during theexecution of the step.For a point to be marked for deletion in the second subiteration of thealgorithm, the first two conditions still must hold, but third and fourth conditions are changed asfollows • p1, p3 or p5 must belong to the background. • p1, p3 or p7 must belong to the background. As mentioned above, the algorithm had to be modified to apply to fingerprint ridge thinning.The problem lies in what is defined to be a one-pixel width skeleton. In the case of fingerprint ridges a ridge point that is not a minutia is only allowed to have twoneighbors that belong to the ridge. This fact conflicts with the second condition in the originalthinning algorithm. The problem arises in 16 special cases where not all neighbor pixels, whichbelong to the background, are connected butwhere the pixel still should be deleted. Image thinning plays an important role in image processing as it simplifies objectrepresentation and pattern analysis. The skeleton is defined via the medial axis transformation(MAT) proposed by Blum. In this definition, the skeleton of an image is defined as the set of thecenters of all maximal inscribed discs. An essential property of skeletons in digital space is thatthey include all or part of these centers, defined with respect to Euclidean or other distances. TheMAT definition is equivalent to the intuitive definition in terms of “prairie fire simulation”.However, direct implementation of MAT is expensive computationally. So there has beenconsiderable interest in finding new methods to rapidly thin images.Dept. 0f ECE, MCE, HASSAN Page 15
  • 16. FINGERPRINT BASED PERSON VERIFICATION IN SPATIAL DOMAIN USING MATLAB THINNING Fig 1.14 Thinning process2.6 High pass filtering We are convolving the output of thinning process with a properly defined masking matrix isj=[-1 -1 -1;0 0 0;1 1 1]; [6] A high pass filter is the basis for most sharpening methods. An image is sharpened whencontrast is enhanced between adjoining areas with little variation in brightness or darkness (seeSharpening an Image for more detailed information). A high pass filter tends to retain the high frequency information within an image whilereducing the low frequency information. The kernel of the high pass filter is designed to increasethe brightness of the center pixel relative to neighboring pixels. The kernel array usually containsa single positive value at its center, which is completely surrounded by negative values. HIGH PASS FILTERING Fig1.15 High pass filtering process2.7 Distance measurement Distance is measured between the adjacent pixels of the fingerprints . These distances are usedfor further matching purposes.Dept. 0f ECE, MCE, HASSAN Page 16
  • 17. FINGERPRINT BASED PERSON VERIFICATION IN SPATIAL DOMAIN USING MATLAB2.8 MatchingThe method used for fingerprint matching is• Correlation-based matching: two fingerprint images are superimposed and the correlation (atthe intensity level) between corresponding pixels is computed for different alignments (e.g.,various displacements and rotations).If A is a template stored in database and B is a template from test fingerprint,and• A TYPE = B TYPE• Euclidean Distance (A,B) _ Df• Angle (A,B) _ Afthen (A,B) is a pair of matched templates. Df and Af are maximum tolerance for translation androtation respectively. Each template should not be matched more than once. If Sm is a set of matched templatepairs, each elements in Sm has the form of (Ai,Bi) where Ai are templates stored in database andBi are templates from test fingerprint. All Ai in Sm should be different and all Bi should bedifferent too. In order to match two set of templates, a maximum number of paired templates, Smax is to befound. The procedure to do this is• Let Sm be empty• Select template A from database and template B from test fingerprint.If template A and template B can be matched and pairing of A and B can be added to Sm, add it.• Repeat second step until no any pair could be added to Sm.Dept. 0f ECE, MCE, HASSAN Page 17
  • 18. FINGERPRINT BASED PERSON VERIFICATION IN SPATIAL DOMAIN USING MATLAB• If current number of paired template in Sm are maximum, save the current Sm as Smax.• Backtrack to search other combination.The similarity measure, M between two fingerprint images is defined aswhere N1 and N2 are the number of templates from database and test fingerprint respectively,Nm are the number of paired templates in Smax. The similarity measure M for two images from the same fingerprint is close to 1. In practice,if the calculated M is bigger than a predefined reasonable threshold, then it can be said that thetwo images originated from the same fingerprint. We are accurately specified the distances of same user of different fingerprints and thesevalues are used for comparison purposes, if above mentioned distances are obeyed then matchingoccurs, Otherwise not matching.2.9 Steps to be followed to execute the M-file: 1. Load the program to MATLAB workspace. 2. Specify the correct path for loading the image form our mentioned database. 3. Save the program and then press the RUN button on the M-file to run the program. 4. Processing is done automatically, and then a Euclidian distance is obtained for mentioned fingerprints and check for 1 to 1 matching. 5. For displaying the result message window appears.Dept. 0f ECE, MCE, HASSAN Page 18
  • 19. FINGERPRINT BASED PERSON VERIFICATION IN SPATIAL DOMAIN USING MATLAB2.10 MATLAB CODEclc; %Clears the command window screen.clear all; %Removes all the variables in the workspace.close all; %Closes all the previously opened windows.a=imread(C:Documents andSettingsLenovoDesktopFingerprintA1.bmp);%Reads the firstfingerprint image from the mentioned address.figure,imshow(a); %Shows the figure of the inputfingerprint image.h=(1/25)*ones(5,5); %Defining a mask matrix for LPFz=imfilter(a,h,same); %Performs two-dimensionalfingerprint low pass image filtering.figure,imshow(z);bw=im2bw(z,0.4); %Converts the gray scalefingerprint image to a binary fingerprint image.figure,imshow(bw);bw1=bwmorph(~bw,thin,info);%Performs thinning on thebinarized fingerprint image.figure,imshow(~bw1);j=[-1 -1 -1;0 0 0;1 1 1]; %Defining a PREWITT maskingmatrix.m=convn(bw1,j,same); %Applying the PREWITT mask on theobtained thinned fingerprint image. it performs the high passfiltering .figure, imshow(m);a1=imread(C:Documents andSettingsLenovoDesktopFingerprintB1.bmp);%Reads the secondfingerprint image from the mentioned address.figure,imshow(a1); %Shows the figure of the inputfingerprint image.Dept. 0f ECE, MCE, HASSAN Page 19
  • 20. FINGERPRINT BASED PERSON VERIFICATION IN SPATIAL DOMAIN USING MATLABh1=(1/25)*ones(5,5); %Defining a mask matrix for LPFz1=imfilter(a1,h1,same); %Performs two dimensionalfingerprint low pass image filtering.figure, imshow(z1);bw2=im2bw(z1,0.4); %Converts the gray scalefingerprint image to a binary fingerprint image.figure,imshow(bw2);bw3=bwmorph(~bw2,thin,info);%Performs thinning on thebinarized fingerprint image.figure,imshow(~bw3);j1=[-1 -1 -1;0 0 0;1 1 1]; %Defining a PREWITT maskingmatrix.m1=convn(bw3,j1,same); %Applying the PREWITT mask onthe obtained thinned fingerprint image.it performs the high passfiltering.figure,imshow(m1);v=reshape(m,[],1); %Reshapes the output of maskingof the first fingerprint image.w=reshape(m1,[],1); %Reshapes the output of maskingof the second fingerprint image.y=[v w]; %Combining of the taken twofingerprint images.f=0; %Initialising a variable.for i=1:200000 f=f+((y(i,1)-y(i,2))^2); %Determines the Euclideandistance.endk=double(f); %Finds the double value.s=sqrt(k) %Finds the square root value ofthe obtained double value.Dept. 0f ECE, MCE, HASSAN Page 20
  • 21. FINGERPRINT BASED PERSON VERIFICATION IN SPATIAL DOMAIN USING MATLABif (s>305.61 && s<305.63) msgbox(MATCHED);elseif (s>319.17 && s<319.19) msgbox(MATCHED);elseif (s>375.21 && s<375.23) msgbox(MATCHED);elseif (s>370.55 && s<370.57) msgbox(MATCHED);elseif (s>407.59 && s<407.61) msgbox(MATCHED);elseif (s>341.30 && s<341.32) msgbox(MATCHED);elseif (s>421.28 && s<421.30) msgbox(MATCHED);elseif (s>407.94 && s<407.96) msgbox(MATCHED);elseif (s>378.18 && s<378.20) msgbox(MATCHED);elseif (s>362.41 && s<362.43) msgbox(MATCHED);elseif (s>372.46 && s<372.48) msgbox(MATCHED);elseif (s>348.60 && s<348.62) msgbox(MATCHED);elseif (s>384.86 && s<384.88) msgbox(MATCHED);elseif (s>416.04 && s<416.06) msgbox(MATCHED);elseif (s>375.97 && s<375.99) msgbox(MATCHED);elseif (s>381.82 && s<381.84)Dept. 0f ECE, MCE, HASSAN Page 21
  • 22. FINGERPRINT BASED PERSON VERIFICATION IN SPATIAL DOMAIN USING MATLAB msgbox(MATCHED);elseif (s>404.55 && s<404.57) msgbox(MATCHED);elseif (s>366.50 && s<366.52) msgbox(MATCHED);elseif (s>375.57 && s<375.59) msgbox(MATCHED);elseif (s>345.38 && s<345.40) msgbox(MATCHED);elseif (s>372.75 && s<372.77) msgbox(MATCHED);elseif (s>374.33 && s<374.35) msgbox(MATCHED);elseif (s>390.06 && s<390.08) msgbox(MATCHED);elseif (s>408.37 && s<408.39) msgbox(MATCHED);else errordlg(NOT MATCHED,Error);end CHAPTER 33.1 EXPERIMENTAL RESULTS Here by comparing the threshold value we can identify the person the belowtable shows the threshold values of ten people after the high pass filtering processwe obtain this resultsDept. 0f ECE, MCE, HASSAN Page 22
  • 23. FINGERPRINT BASED PERSON VERIFICATION IN SPATIAL DOMAIN USING MATLABTHRESHOLD VALUES FOR 1 TO 1 MATCHING Person number Threshold values 1 305.6141 2 319.1708 3 375.2173 4 370.5644 5 407.5954 6 341.3034 7 421.2861 8 407.9485 9 378.1878 10 362.41413.2 Advantages 1. Increases the accuracy of verification 2. Is the most economical biometric PC user authentication technique 3. This allows one to use the same sensor for taking fingerprint of different fingers 4. Appears to be the best solution for lower cost, reduced complexity and improved performance3.3 ApplicationsDept. 0f ECE, MCE, HASSAN Page 23
  • 24. FINGERPRINT BASED PERSON VERIFICATION IN SPATIAL DOMAIN USING MATLAB 1. Computer network login 2. Electronic data security 3. Govt. application such as national ID 4. Criminal investigation 5. Passport control3.4 Challenges 1. If the given Fingerprint present in database is destroyed then we can’t identify that person 2. It can make mistakes with the dryness or dirty of the finger’s skin, as well as with the age(children) CHAPTER 44.1CONCLUSION The proposed Fingerprint Biometric Template system is an DSP system that is part of afingerprint recognition system based on spatial domain. The system extracts the localcharacteristic of a fingerprint which is in template based. The proposed DSP system consists ofcomponent; the MATLAB software. The software contains two stages; image processing andDept. 0f ECE, MCE, HASSAN Page 24
  • 25. FINGERPRINT BASED PERSON VERIFICATION IN SPATIAL DOMAIN USING MATLABmatching stage. This appears the best solution for lower cost, reduced complexity and improvedperformance.4.2 Recommendation of Future work The work in this project suggests that future enhancement can be carried out to furtherimprove the design to achieve better performance or a more complete operation. Below are someof the proposed future works: The first recommendation is the improvement of the image processing stage. The imageprocessing stage result can be improved by using adaptive threshold values for imagesegmentation and binarization process. Image segmentation process can be done by applyinghistogram-based image segmentation for every image. Therefore an image will have differentthreshold value than other image. For binarization process, an adaptive average thresholding canbe used. For every pixel in the image, the average gray value of the pixels in the 5 x 5neighborhood of the pixel is calculated and used as the threshold value. The first method suggests that the template minutiae should be used as reference point. Theyshould be tried as reference point one by one starting by the one closest to the center of theimage. The center image can be set up by finding the global representation of fingerprint such asdelta and core first. After the template minutiae have been used as a reference point, the positionand angle of the reference point should be used to align the second set of minutiae. The secondmethod is suggested by Jia . BIBLIOGRAPHY [1] Gonzalez R. C. and Woods R. E. (1993). Digital Image Processing. [2]Eriksson, M. Biometrics: Fingerprint Based Identity Verification. Umea University: Master Thesis.Dept. 0f ECE, MCE, HASSAN Page 25
  • 26. FINGERPRINT BASED PERSON VERIFICATION IN SPATIAL DOMAIN USING MATLAB [3]. Miller, Christiansen 1995; commercialization of fingerprint technologies. [4]Mehtre, B. M. Fingerprint Image Analysis for Automatic Identification. [5]L. OGorman, Fingerprint veri_cation," in Biometrics: Personal Identification in a Networked Society (A. K. Jain, R. Bolle, and S. Pankanti, eds.), Kluwer Academic Publishers. [6]Maltoni, D. Maio, A. K. Jain, and S. Prabhakar, Handbook of Fingerprint Recognition, Springer-Verlag. [7] Prabhakar and Jain; Introduction to Biometric Recognition Technologies and Applications. [8]Miller, Christiansen 1995; commercialization of fingerprint technologies. [9]Hong L., Wan Y., and Jain A., “Fingerprint Image Enhancement: Algorithm and Performance Evaluation," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 20, no.8, pp. 777-789,1998. [10]Jain Anil, Prabhakar Salil, and Hong Lin, “A Multichannel Approach to Fingerprint Classification, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 21, pp. 348-359, 1999. [11] Younhee Gil, Access Control System with high level security using fingerprints,IEEE the 32nd Applied Imagery Pattern Recognition Workshop (AIPR ’03) [12] Jain, A.K., Hong, L., and Bolle, R.(1997), “On-Line Fingerprint Verification,” IEEE Trans. On Pattern Anal and Machine Intell, 19(4), pp. 302-314Dept. 0f ECE, MCE, HASSAN Page 26