Fingerprint Recognition Technique(PDF)

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Fingerprint Recognition Technique(PDF)

  1. 1. FINGERPRINT RECOGNITION Project ID: 1044 A Final Project Report Submitted to Biju Patnaik University of Technology, Rourkela In partial fulfilment of the requirement for the B.Tech Degree Submitted By SAILENDRA SAGAR PATRA SANDEEP KUMAR PANDA May - 2013 Under the guidance of Mrs. T. Mita Kumari APEX INSTITUTE OF TECHNOLOGY & MANAGEMENT Pahala, Bhubaneswar, Odisha – 752101, India
  2. 2. FINGERPRINT RECOGNITION A Final Project Report Submitted to Biju Patnaik University of Technology, Rourkela In partial fulfilment of the requirement for the B.Tech Degree Submitted By SAILENDRA SAGAR PATRA Regn. No.-0901314147 SANDEEP KUMAR PANDA Regn. No.-0901314126 May - 2013 Under the guidance of Mrs. T. Mita Kumari APEX INSTITUTE OF TECHNOLOGY & MANAGEMENT Pahala, Bhubaneswar, Odisha – 752101, India
  3. 3. APEX INSTITUTE OF TECHNOLOGY & MANAGEMENT Pahala, Bhubaneswar, Odisha – 752101, India CERTIFICATE This is to certify that the project work entitled ‘Fingerprint Recognition’ is a bonafide work done by Sailendra Sagar Patra bearing Registration No. 0901314147 of ECE branch and Sandeep Kumar Panda bearing Registration No. 0901314126 of ECE branch. This report is submitted in partial fulfilment of the requirements for the award of the B.Tech degree under Biju Patnaik University of Technology, Rourkela, during the year 2012-13. (Mrs T.Mita Kumari) (Dr. Satya Ranjan Pattanaik) Project Guide B.Tech Project Coordinator (Prof. R.C. Das) PRINCIPAL Institute Seal
  4. 4. FINGERPRINT RECOGNITION ii ABSTRACT The study and implementation of a fingerprint recognition system based on Minutiae based matching quite frequently used in various fingerprint algorithms and techniques. Human fingerprints are rich in details called minutiae, which can be used as identification marks for fingerprint verification. The goal of this project is to develop a complete system for fingerprint verification through extracting and matching minutiae. To achieve good minutiae extraction in fingerprints with varying quality, pre-processing in form of image enhancement, image Binarization and image segmentation is first applied on fingerprints before they are evaluated. Histogram equalization and Fourier Transform have been used for image enhancement. Then the fingerprint image is binarized using the local adaptive threshold method .Minutia Extraction is done by thinning and minutia marking technique. A simple algorithm technique is used for minutia matching. By using match score method we differentiate the two fingerprints are same or not.
  5. 5. FINGERPRINT RECOGNITION iii ACKNOWLEDGEMENT It is our proud privilege to epitomize our deepest sense of gratitude and indebtedness to our guide, Mrs. T.Mita Kumari, for her valuable instructions, guidance and support throughout our project work. Her inspiring assistance and affectionate care enabled us to complete our work smoothly and successfully. We again owe our special thanks to Dr. Satya Ranjan Pattanaik, B.Tech Project Coordinator for giving us an opportunity to do this project. We would also like to thank Prof. R.C. Das, Principal, AITM, and Bhubaneswar for his persistent drive for better quality in everything that happens at AITM. This report is a dedicated contribution towards that greater goal. Sailendra Sagar Patra (Regn. No.-0901314147) Sandeep Kumar Panda (Regn. No.-0901314126)
  6. 6. FINGERPRINT RECOGNITION iv TABLE OF CONTENTS ABSTRACT.........................................................................................................................ii ACKNOWLEDGEMENT .................................................................................................iii TABLE OF CONTENTS................................................................................................... iv LIST OF FIGURES ........................................................................................................... vi LIST OF ABBREVIATIONS ........................................................................................... vii 1. Introduction..................................................................................................................... 1 1.1 Introduction................................................................................................................. 1 1.2 What is a fingerprint? .................................................................................................. 1 1.3 What is Fingerprint Recognition? ................................................................................ 2 2. Algorithm Level Design and Enhancement Technique ................................................. 4 2.1 Algorithm Level Design .............................................................................................. 4 FLOW CHART................................................................................................................. 6 2.2 Fingerprint Image Enhancement Technique................................................................. 7 2.2.1 Histogram Equalization ........................................................................................ 7 2.2.2 Fingerprint Enhancement by Fourier transform..................................................... 8 2.2.3 Fingerprint Image Binarization ............................................................................. 9 2.3 Fingerprint Image Segmentation................................................................................ 10 2.3.1 Block direction estimation .................................................................................. 10 2.3.2 ROI extraction by Morphological operations....................................................... 11 2.4.1 Minutia Extraction.............................................................................................. 12 2.4.2 Minutia Marking................................................................................................. 13 2.4.3 False Minutiae Removal ..................................................................................... 14 2.4.4 Unify Terminations and Bifurcation.................................................................... 16 2.5 Minutia Matching...................................................................................................... 17 2.5.1 Alignment Stage ................................................................................................. 17 2.5.2 Match Stage........................................................................................................ 18 3. Results and Discussions................................................................................................. 20 3.1 Results for Minutiae Extraction algorithm ................................................................. 20 3.2 Comparison Results For Minutiae matching .............................................................. 25 3.2.1 Two Different Fingerprints ................................................................................. 25 3.2.2 Two Fingerprint of a same person with a Little Difference................................. 26
  7. 7. FINGERPRINT RECOGNITION v 4. Conclusion ..................................................................................................................... 27 References.......................................................................................................................... 28
  8. 8. FINGERPRINT RECOGNITION vi LIST OF FIGURES Figure 1.1: Fingerprint Image................................................................................................ 1 Figure 1.2: Two Minutia Features ......................................................................................... 2 Figure 1.3: Verification vs. Identification.............................................................................. 3 Figure 2.1: Simplified Fingerprint Recognition ..................................................................... 4 Figure 2.2: Minutia Extractor ................................................................................................ 4 Figure 2.3: Minutia Matcher ................................................................................................. 5 Figure 2.4: Steps Involved In Fingerprint Recognition .......................................................... 6 Figure 2.5: (a) Histogram of an image, (b) Histogram equalization of an image..................... 7 Figure 2.6: (a) Original image, (b) Enhanced Image after Equalization.................................. 8 Figure 2.7: (a) Original Image, (b) Image Enhancement by FFT............................................ 9 Figure 2.8: (a) Enhanced image, (b) Image after Binarization.............................................. 10 Figure 2.9: (a) Binarization image, (b) Direction Map ......................................................... 11 Figure 2.10: (a) Original Image, (b) Close Operation, (c) Open Operation, (d) ROI+Bound 12 Figure 2.11: (a) Bifurcation, (b) Termination, (c) Triple Counting Branch........................... 13 Figure 2.12: (a) Thinned Image, (b) Figure after Minutiae Extraction.................................. 14 Figure 2.13: False Minutia Points........................................................................................ 15 Figure 2.14: A Bifurcation to Three Terminations............................................................... 16 Figure 3.1: (a) Original Image, (b) Image after Histogram Equalization .............................. 20 Figure 3.2: (a) Histogram image, (b) Image Enhancement using FFT.................................. 21 Figure 3.3: (a) FFT Image, (b) Image after Binarization ...................................................... 21 Figure 3.4: (a) Binarization image, (b) Direction map of Binarization Image...................... 22 Figure 3.5: (a) Binarization Image, (b) ROI Image .............................................................. 22 Figure 3.6: (a) Open Operation, (b) Close Operation ........................................................... 23 Figure 3.7: (a) Adaptive Binarization, (b) ROI+BOUND image .......................................... 23 Figure 3.8: (a) ROI image, (b) Thinned Image.................................................................... 24 Figure 3.9: (a) Thinned Image, (b) Minutiae Marking after Thinning .................................. 24 Figure 3.10: (a) First Fingerprint, (b) Second Fingerprint, (c) Minutiae extraction of First Fingerprint, (d) Minutiae Extraction of Second Fingerprint ................................................. 25 Figure 3.11: (a) First Fingerprint, (b) Second Fingerprint, (c) Minutiae extraction of First Fingerprint, (d) Minutiae extraction of Second Fingerprint.................................................. 26
  9. 9. FINGERPRINT RECOGNITION vii LIST OF ABBREVIATIONS 1.AFRS (Automatic Fingerprint Recognition System)………………………………………2 2.FFT (First Fourier Transform)……………………………………………………………...8 3.ROI (Region Of Interest)…………………………………………………………………..10 4.CN (Crossing Number)…………………………………………………………………….13 5. FVC2000 (Fingerprint Verification competition 2000)…………………………………..20 6.IEEE(Institute Of Electrical Electronics Engineering)…………………………………….28
  10. 10. FINGERPRINT RECOGNITION 1 1. Introduction 1.1 Introduction Fingerprint recognition or fingerprint authentication refers to the automated method of verifying a match between two human fingerprints [1]. Fingerprints are one of many forms of biometrics used to identify an individual and verify their identity. Because of their uniqueness and consistency over time, fingerprints have been used for over a century, more recently becoming automated (i.e. a biometric) due to advancement in computing capabilities. 1.2 What is a fingerprint? Skin on human fingertips contains ridges and valleys which together forms distinctive patterns. These patterns are fully developed under pregnancy and are permanent throughout whole lifetime. Prints of those patterns are called fingerprints. Injuries like cuts, burns and bruises can temporarily damage quality of fingerprints but when fully healed, patterns will be restored. Through various studies it has been observed that no two persons have the same fingerprints, hence they are unique for every individual. Figure 1.1: Fingerprint Image Chapter-1
  11. 11. FINGERPRINT RECOGNITION 2 However, shown by intensive research on fingerprint recognition, fingerprints are not distinguished by their ridges and furrows, but by features called Minutia, which are some abnormal points on the ridges (Figure 1.2) Among the variety of minutia types reported in literatures, two are mostly significant and in heavy usage:  Ridge ending- the abrupt end of a ridge  Ridge bifurcation- a single ridge that divides into two ridges Figure 1.2: Two Minutia Features 1.3 What is Fingerprint Recognition? Fingerprint recognition (sometimes referred to as dactyloscopy) is the process of comparing questioned and known fingerprint against another fingerprint to determine if the impressions are from the same finger or palm. It includes two sub-domains: one is fingerprint verification and the other is fingerprint identification (Figure 1.3). In addition, different from the manual approach for fingerprint recognition by experts, the fingerprint recognition here is referred as AFRS (Automatic Fingerprint Recognition System) [2,3], which is program- based.
  12. 12. FINGERPRINT RECOGNITION 3 However, in all fingerprint recognition problems, either verification(one to one matching) or identification(one to many matching), the underlining principles of well defined representation of a fingerprint and matching remains the same. Figure 1.3: Verification vs. Identification
  13. 13. FINGERPRINT RECOGNITION 4 2. Algorithm Level Design and Enhancement Technique 2.1 Algorithm Level Design A fingerprint recognition system constitutes of fingerprint acquiring device, minutia extractor and minutia matcher Figure 2.1: Simplified Fingerprint Recognition For fingerprint acquisition, optical or semi-conduct sensors are widely used [3]. They have high efficiency and acceptable accuracy except for some cases that the user’s finger is too dirty or dry. The minutia extractor and matcher modules have been explained in detail in the next part for algorithm design and other subsequent sections. To implement a minutia extractor, a three-stage approach is widely used by Researchers. They are pre processing, minutia extraction and post processing stage. Figure 2.2: Minutia Extractor Chapter- 2
  14. 14. FINGERPRINT RECOGNITION 5 For the fingerprint image pre processing stage, Histogram Equalization and Fourier Transform have been used to do image enhancement. And then the fingerprint image is binarized using the locally adaptive threshold method. The image segmentation task is fulfilled by a three-step approach: block direction estimation, segmentation by direction intensity and Region of Interest extraction by Morphological operations. For minutia extraction stage, iterative parallel thinning algorithm is used. The minutia marking is a relatively simple task. For the post processing stage, a more rigorous algorithm is developed to remove false minutia. Also a novel representation for bifurcations is proposed to unify terminations and bifurcations. Figure 2.3: Minutia Matcher The minutia matcher chooses any two minutiae as a reference minutia pair and then matches their associated ridges first. If the ridges match well, the two fingerprint images are aligned and matching is conducted for all remaining minutia.
  15. 15. FINGERPRINT RECOGNITION 6 FLOW CHART Figure 2.4: Steps Involved In Fingerprint Recognition LOAD IMAGE HISTOGRAM EQUALIZATION ENHANCEMENT USING FFT BINARIZATION RIDGE DIRECTION THINING MINUTIA MARKING TEMPLATE ALLIGN AND MATCH TEMPLATE ROI IMAGE AQUAISATION PRE PROCESSING STAGE MINUTIAE EXTRACTION MINUTIAE MATCH
  16. 16. FINGERPRINT RECOGNITION 7 2.2 Fingerprint Image Enhancement Technique The first step in the minutiae extraction stage is Fingerprint Image enhancement. This is mainly done to improve the image quality and to make it clearer for further operations. Often fingerprint images from various sources lack sufficient contrast and clarity. Hence image enhancement is necessary and a major challenge in all fingerprint techniques to improve the accuracy of matching. It increases the contrast between ridges and furrows and connects the some of the false broken points of ridges due to insufficient amount of ink or poor quality of sensor input. In our project we have implemented three techniques: Histogram Equalization, Fast Fourier Transformation and Image Binarization. 2.2.1 Histogram Equalization Histogram equalization is a technique of improving the global contrast of an image by adjusting the intensity distribution on a histogram [4]. This allows areas of lower local contrast to gain a higher contrast without affecting the global contrast. Histogram equalization accomplishes this by effectively spreading out the most frequent intensity values. The original histogram of a fingerprint image has the bimodal type(Figure 2.5(a), the histogram after the histogram equalization occupies all the range from 0 to 255 and the visualization effect is enhanced(Figure 2.5(b)). (a) (b) Figure 2.5: (a) Histogram of an image, (b) Histogram equalization of an image
  17. 17. FINGERPRINT RECOGNITION 8 (a) (b) Figure 2.6: (a) Original image, (b) Enhanced Image after Equalization 2.2.2 Fingerprint Enhancement by Fourier transform The image was divided into small processing blocks (32 by 32 pixels) and the Fourier transform was performed according to:               N vy M ux jyxfvuF 2exp),(),( (2.1) for u = 0, 1, 2, ..., 31 and v = 0, 1, 2, ..., 31. In order to enhance a specific block by its dominant frequencies, the FFT of the block was multiplied by its magnitude a set of times [4] .Where the magnitude of the original FFT = abs (F(u ,v)) = |F(u ,v)|. The enhanced block is obtained according to:   k vuFvuFFyxg ),(,),( 1   (2.2) Where F-1 (F(u,v)) is done by:                     N vy M ux jvuF MN yxf M x N y 2exp, 1 ),( 1 0 1 0 (2.3) for x = 0, 1, 2, ..., 31 and y = 0, 1, 2, ..., 31.
  18. 18. FINGERPRINT RECOGNITION 9 The k in formula (2) is an experimentally determined constant, which was k=0.45 to calculate. While having a higher "k" improves the appearance of the ridges, filling up small holes in ridges, having too high a "k" can result in false joining of ridges. Thus a termination might become a bifurcation. (a) (b) Figure 2.7: (a) Original Image, (b) Image Enhancement by FFT The enhanced image after FFT has the improvements to connect some falsely broken points on ridges and to remove some spurious connections between ridges. The shown image at the left side of Figure2.7(b) is also processed with histogram equalization after the FFT transform. 2.2.3 Fingerprint Image Binarization Fingerprint Image Binarization is to transform the 8-bit Gray fingerprint image to a 1- bit image with 0-value for ridges and 1-value for furrows. After the operation, ridges in the fingerprint are highlighted with black colour while furrows are white. A locally adaptive Binarization method is performed to binarized the fingerprint image. Such a named method comes from the mechanism of transforming a pixel value to 1 if the value is larger than the mean intensity value of the current block (16x16) to which the pixel belongs [Figure 2.8(a) and Figure 2.8(b)].
  19. 19. FINGERPRINT RECOGNITION 10 (a) (b) Figure 2.8: (a) Enhanced image, (b) Image after Binarization 2.3 Fingerprint Image Segmentation In general, only a Region of Interest (ROI) is useful to be recognized for each fingerprint image. The image area without effective ridges and furrows is first discarded since it only holds background information. Then the bound of the remaining effective area is sketched out since the minutia in the bound region is confusing with those spurious minutia that are generated when the ridges are out of the sensor [5]. To extract the ROI, a two-step method is used. The first step is block direction estimation and direction variety check, while the second is intrigued from some Morphological methods. 2.3.1 Block direction estimation The direction for each block of the fingerprint image with WxW in size(W is 16 pixels by default)is estimated. The algorithm is: I. The gradient values along x-direction (gx) and y-direction (gy) for each pixel of the block is calculated. Two Sobel filters are used to fulfill the task. II. For each block, following formula is used to get the Least Square approximation of the block direction.           22 2 tan yx yx gg gg  (2.4) for all the pixels in each block.
  20. 20. FINGERPRINT RECOGNITION 11 The formula is easy to understand by regarding gradient values along x-direction and y direction as cosine value and sine value. So the tangent value of the block direction is estimated nearly the same as the way illustrated by the following formula.     22 sincos cossin2 2tan    (2.5) After the estimation of each block direction, those blocks without significant information on ridges and furrows are discarded based on the following formulas:         2222 /2 yxyxyx ggWWggggE (2.6) For each block, if its certainty level E is below a threshold, then the block is regarded as a background block. The direction map is shown in the following diagram (assuming there is only one fingerprint in For each block, if its certainty level E is below a threshold, then the block is regarded each image.) (a) (b) Figure 2.9: (a) Binarization image, (b) Direction Map 2.3.2 ROI extraction by Morphological operations Two Morphological operations called ‘OPEN’ and ‘CLOSE’ are adopted[5,6]. The ‘OPEN’ operation can expand images and remove peaks introduced by background
  21. 21. FINGERPRINT RECOGNITION 12 noise [Figure 2.10(c)]. The ‘CLOSE’ operation can shrink images and eliminate small cavities [Figure 2.10(b)]. (a) (b) Fig 2.10(d) show the interest fingerprint image area and its bound. The bound is the subtraction of the closed area from the opened area. Then the algorithm throws away those leftmost, rightmost, uppermost and bottommost blocks out of the bound so as to get the tightly bounded region just containing the bound and inner area. (c) (d) Figure 2.10: (a) Original Image, (b) Close Operation, (c) Open Operation, (d) ROI+Bound 2.4 POST PROCESSING STAGE 2.4.1 Minutia Extraction Ridge Thinning is to eliminate the redundant pixels of ridges till the ridges are just one pixel wide. An iterative, parallel thinning algorithm is used. In each scan of the full
  22. 22. FINGERPRINT RECOGNITION 13 fingerprint image, the algorithm marks down redundant pixels in each small image window(3x3). And finally removes all those marked pixels after several scans. The thinned ridge map is then filtered by other three Morphological operations to remove some H breaks, isolated points and spikes. 2.4.2 Minutia Marking After the fingerprint ridge thinning, marking minutia points is relatively easy. The concept of Crossing Number (CN) is widely used for extracting the minutiae [6]. In general, for each 3x3 window, if the central pixel is 1 and has exactly 3 one-value neighbors, then the central pixel is a ridge branch [Fig 2.11(a)]. If the central pixel is 1 and has only 1 one-value neighbour, then the central pixel is a ridge ending [Fig 2.11(b)] ,i.e., if Cn(P) = =1 it’s a ridge end and if Cn(P) = = 3 it’s a ridge bifurcation point, for a pixel P. (a) (b) (c) Figure 2.11: (a) Bifurcation, (b) Termination, (c) Triple Counting Branch 0 1 0 0 1 0 1 0 1 0 0 0 0 1 0 0 0 1 0 1 0 0 1 1 1 0 0
  23. 23. FINGERPRINT RECOGNITION 14 Fig 2.11(c) illustrates a special case that a genuine branch is triple counted. Suppose both the uppermost pixel with value 1 and the rightmost pixel with value 1 have another neighbour outside the 3x3 window, so the two pixels will be marked as branches too. But actually only one branch is located in the small region. So a check routine requiring that none of the neighbors of a branch are branches is added. Also the average inter-ridge width D is estimated at this stage. The average inter ridge width refers to the average distance between two neighbouring ridges. The way to approximate the D value is to scan a row of the thinned ridge image and sum up all pixels in the row whose value is one. Then divide the row length with the above summation to get an inter ridge width. For more accuracy, such kind of row scan is performed upon several other rows and column scans are also conducted, finally all the inter-ridge widths are averaged to get the D. Together with the minutia marking, all thinned ridges in the fingerprint image are labelled with a unique ID for further operation. The labelling operation is realized by using the Morphological operation. Minutiae extraction from thinned image in [Figure 2.12(b) and Figure 2.12(a).] (a) (b) Figure 2.12: (a) Thinned Image, (b) Figure after Minutiae Extraction 2.4.3 False Minutiae Removal At this stage false ridge breaks due to insufficient amount of ink & ridge cross connections due to over inking are not totally eliminated. Also some of the earlier methods
  24. 24. FINGERPRINT RECOGNITION 15 introduce some spurious minutia points in the image. So to keep the recognition system consistent these false minutiae need to be removed. Here we first calculate the inter ridge distance D which is the average distance between two neighbouring ridges. For this scan each row to calculate the inter ridge distance using the formula: Inter ridge distance = sum of all pixel with value 1 row length Finally an averaged value over all rows gives D. All we label all thinned ridges in the fingerprint image with a unique ID for further operation using a MATLAB morphological operation BWLABEL. Now the following 7 types of false minutia points are removed using these steps (Figure 2.13). m1 m2 m3 m4 m5 m6 m7 Figure 2.13: False Minutia Points 1. If d(bifurcation, termination) < D & the 2 minutia are in the same ridge then remove both of them (case m1) 2. If d(bifurcation, bifurcation) < D & the 2 minutia are in the same ridge them remove both of them (case m2, m3) 3. If d(termination, termination) ≈ D & the their directions are coincident with a small angle variation & no any other termination is located between the two terminations then remove both of them (case m4, m5, m6) 4. If d(termination, termination) < D & the 2 minutia are in the same ridge then remove both of them (case m7)
  25. 25. FINGERPRINT RECOGNITION 16 2.4.4 Unify Terminations and Bifurcation Since various data acquisition conditions such as impression pressure can easily change one type of minutia into the other, most researchers adopt the unification representation for both termination and bifurcation. So each minutia is completely characterized by the following parameters at last: 1) x-coordinate, 2) y-coordinate, and 3) orientation. The orientation calculation for a bifurcation needs to be specially considered. All three ridges deriving from the bifurcation point have their own direction. The bifurcation is broken into three terminations. The three new terminations are the three neighbour pixels of the bifurcation and each of the three ridges connected to the bifurcation before is now associated with a termination respectively [6,7][Figure 2.14]. Figure 2.14: A Bifurcation to Three Terminations Three neighbors become termination(Left) Each termination has their own orientation(right) And the orientation of each termination (tx,ty) is estimated by following method : A ridge segment is tracked whose starting point is the termination and length is D. All coordinates of points in the ridge segment are summed up. The above summation is then divided with D to get sx. And sy can be obtained using the same way. The direction is obtained from: atan ((sy-ty)/(sx-tx)) (2.7) 0 0 1 1 1 0 0 0 1
  26. 26. FINGERPRINT RECOGNITION 17 2.5 Minutia Matching Given two set of minutia of two fingerprint images, the minutia match algorithm determines whether the two minutia sets are from the same finger or not. An alignment-based match algorithm is used. It includes two consecutive stages: one is alignment stage and the second is match stage [7]. 1. Alignment stage. Given two fingerprint images to be matched, any one minutia from each image is chosen, and the similarity of the two ridges associated with the two referenced minutia points is calculated. If the similarity is larger than a threshold, each set of minutia is transformed to a new coordination system whose origin is at the referenced point and whose x- axis is coincident with the direction of the referenced point. 2. Match stage: After obtaining two set of transformed minutia points, the elastic match algorithm is used to count the matched minutia pairs by assuming two minutia having nearly the same position and direction are identical. 2.5.1 Alignment Stage The ridge associated with each minutia is represented as a series of x-coordinates (x1,x2…xn) of the points on the ridge. A point is sampled per ridge length L starting from the minutia point, where the L is the average inter-ridge length. And n is set to 10 unless the total ridge length is less than 10*L. So the similarity of correlating the two ridges is derived from: 5.0 0 22 0            m i ii m i ii Xx Xx S (2.8) Where (xi~xn) and (Xi~XN) are the set of minutia for each fingerprint image respectively. And m is minimal one of the n and N value. If the similarity score is larger than 0.8, then the next step is executed else the next pair of ridges are continued to match. For each fingerprint, all other minutia are translated and rotated with respect to the reference minutia according to the following formula:
  27. 27. FINGERPRINT RECOGNITION 18 _ _ _ = ∗ − − − ( . ) Where  ,, yx the parameters of the reference minutia and TM are is TM = cosθ −sinθ 0 sinθ cosθ 0 0 0 1 ( . ) The following diagram illustrates the effect of translation and rotation: X’-axis Y-axis X F E y Y’-axis D D X-axis Figure 2.15: Effect of Translation and Rotation The new coordinate system is originated at minutia F and the new x-axis is coincident with the direction of minutia F. No scaling effect is taken into account by assuming two fingerprints from the same finger have nearly the same size. 2.5.2 Match Stage The matching algorithm for the aligned minutia patterns needs to be elastic since the strict match requiring that all parameters  ,, yx are the same for two identical minutiae is impossible due to the slight deformations and inexact quantization of minutia [8]. The elastic matching of minutia is achieved by placing a bounding box around each template minutia. If the minutia to be matched is within the rectangle box and the direction Ө
  28. 28. FINGERPRINT RECOGNITION 19 discrepancy between them is very small, then the two minutiae are regarded as a matched minutia pair. Each minutia in the template image either has no matched minutia or has only one corresponding minutia. The final match ratio for two fingerprints is the number of total matched pair over the number of minutia of the template fingerprint. The score is 100*ratio and ranges from 0 to 100. If the score is larger than a pre-specified threshold, the two fingerprints are from the same finger. However, the elastic match algorithm has large computation complexity and is vulnerable to spurious minutia. ℎ = ( ℎ ) max( 1 2) ( . )
  29. 29. FINGERPRINT RECOGNITION 20 Chapter- 3 3. Results and Discussions A fingerprint image is taking as an input from FVC2000 (Fingerprint Verification competition 2000) to test the experiment performance [8]. This algorithm is used for differentiating fingerprints by performing minutiae extraction. 3.1 Results for Minutiae Extraction algorithm STEP1: First we take original fingerprint image and performing image enhancement by using Histogram equalization. Figure 3.1(a) shows the original fingerprint image and its corresponding histogram Equalized image is shown in Figure 3.1(b) (a) (b) Figure 3.1: (a) Original Image, (b) Image after Histogram Equalization
  30. 30. FINGERPRINT RECOGNITION 21 STEP2: For better enhancement we use histogram Equalization image as an input to the FFT algorithm. Histogram equalized image is shown in Figure 3.2(a) and its enhanced image after FFT is shown in Figure 3.2(b). (a) (b) Figure 3.2: (a) Histogram image, (b) Image Enhancement using FFT STEP3: The enhanced image is binarized using Binarization algorithm. Enhanced image is shown in Figure 3.3(a) and corresponding Binarized image shown in Figure 3.3((b). (a) (b) Figure 3.3: (a) FFT Image, (b) Image after Binarization
  31. 31. FINGERPRINT RECOGNITION 22 STEP4: Binarized image is shown in Figure 3.4(a) and its corresponding directional map shown in Figure 3.4(b). (a) (b) Figure 3.4: (a) Binarization image, (b) Direction map of Binarization Image STEP5: For extracting the un necessary area we use ROI algorithm. Binarized image is shown in 3.5(a), and Its corresponding ROI image shown in 3.5(b).The size of binarized image 256*256, but the size of ROI image is 224*224. (a) (b) Figure 3.5: (a) Binarization Image, (b) ROI Image
  32. 32. FINGERPRINT RECOGNITION 23 STEP6: Figure 3.6(a) shows the open area of the ROI Image and Figure 3.6(b) shows the close area of the ROI Image. (a) (b) Figure 3.6: (a) Open Operation, (b) Close Operation STEP7: After Binarization the image is an input to the region of interest algorithm. it shows the actual area of the fingerprint image by bounding the region with some useful colours in Figure 3.7(b). (a) (b) Figure 3.7: (a) Adaptive Binarization, (b) ROI+BOUND image
  33. 33. FINGERPRINT RECOGNITION 24 STEP8: From ROI+BOUND image we found the ROI image. We use ROI image is an input to the thinned algorithm. The Binarized image is shown in Figure 3.8(a) and its corresponding image is shown in Figure 3.8(b). From the thinned figure We conclude that Bifurcation and termination points are shown clearly. (a) (b) Figure 3.8: (a) ROI image, (b) Thinned Image STEP9: We apply thinned image an input to the minutiae extraction algorithm for finding minutiae point. Figure 3.9(a) shows thinned Image and Figure 3.9(b) shows minutiae marked thinned image. Here red dots show the termination (ridge ending) points and yellow dots show the bifurcation points. (a) (b) Figure 3.9: (a) Thinned Image, (b) Minutiae Marking after Thinning
  34. 34. FINGERPRINT RECOGNITION 25 3.2 Comparison Results For Minutiae matching For simulation result, we have taken the threshold value of 0.65. 3.2.1 Two Different Fingerprints Figure3.10 (a), Figure 3.10(b) shows two different fingerprint and Figure 3.10(c), Figure 3.10(d) shows corresponding minutiae marked image. The match score value between the two images is 0.37. As this value is less than the threshold value, we can conclude that these two fingerprints are of two different persons. (a) (b) (c) (d) Figure 3.10: (a) First Fingerprint, (b) Second Fingerprint, (c) Minutiae extraction of First Fingerprint, (d) Minutiae Extraction of Second Fingerprint
  35. 35. FINGERPRINT RECOGNITION 26 3.2.2 Two Fingerprint of a same person with a Little Difference Figure3.11 (a), Figure 3.11(b) shows two different fingerprint, here Figure 3.11(b) shows the little modify of Figure 3.11(a). Figure 3.10(c), Figure 3.10(d) shows corresponding minutiae marked image. The match score value between the two images is 0.68. As this value is Greater than the threshold value, we can conclude that these two fingerprints are of a same person. (a) (b) (c) (d) Figure 3.11: (a) First Fingerprint, (b) Second Fingerprint, (c) Minutiae extraction of First Fingerprint, (d) Minutiae extraction of Second Fingerprint
  36. 36. FINGERPRINT RECOGNITION 27 Chapter- 4 4. Conclusion The above implementation was an effort to understand how Fingerprint Recognition is used as a form of biometric to recognize identities of human beings. It includes all the stages from enhancement to minutiae extraction of fingerprints. There are various standard techniques are used in the intermediate stages of processing. The relatively low percentage of verification rate as compared to other forms of biometrics indicates that the algorithm used is not very robust and is vulnerable to effects like scaling and elastic deformations. Various new techniques and algorithm have been found out which give better results. Also a major challenge in Fingerprint recognition lies in the pre processing of the bad quality of fingerprint images which also add to the low verification rate. The reliability of any automatic fingerprint system strongly relies on the precision obtained in the minutia extraction process. A number of factors are detrimental to the correct location of minutia. Among them, poor image quality is the most serious one. In this project, we have combined many methods to build a minutia extractor and a minutia matcher. The following concepts have been used- segmentation using Morphological operations, minutia marking by specially considering the triple branch counting, minutia unification by decomposing a branch into three terminations. There is a scope of further improvement in terms of efficiency and accuracy which can be achieved by improving the hardware to capture the image or by improving the image enhancement techniques. So that the input image to the thinning stage could be made better this could improve the future stages and the final outcome.
  37. 37. FINGERPRINT RECOGNITION 28 References [1] Fingerprint database - FVC2002 (Fingerprint Verification Competition 2002) [2] Rafael C .Gonzalez, Richard E Woods digital image processing”2nd edition, 2002. [3] K. Jain, F. Patrick, A. Arun , “Handbook of Biometrics”, Springer Science Business Media, LLC, 1st edition, pp. 1-42, 2008. [4] D. Maio, and D. Maltoni, “Direct gray-scale minutia detection in fingerprints”, IEEE Transactions Pattern Analysis and Machine Intelligence, vol. 19(1), pp. 27-40, 1997. [5] D. Maltoni, D. Maio, and A. Jain, S. Prabhakar, “4.3: Minutiae-based Methods’ (extract) from Handbook of Fingerprint Recognition”, Springer, New York, pp. 141- 144, 2003. [6] E. Hastings, “A Survey of Thinning Methodologies”, Pattern analysis and Machine Intelligence, IEEE Transactions, vol. 4, Issue 9, pp. 869-885, 1992. [7] K. Nallaperumall, A. L. Fred, and S. Padmapriya, “A Novel Technique for Fingerprint Feature Extraction Using Fixed Size Templates”, IEEE 2005 Conference, pp. 371- 374, 2005. [8] P. Komarinski, P. T. Higgins, and K. M. Higgins, K. Fox Lisa, “Automated Fingerprint Identification Systems (AFIS)”, Elsevier Academic Press, pp. 1-118, 2005.
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