Fingerprint Distortion Removal and Enhancement by Effective Use of Contextual Filtering

Transcription

1 Fingerprint Distortion Removal an Enhancement b Effective Use of Contetual Filtering b Mubeen Ghafoor PE0800 A thesis submitte to the Electronics Engineering Department in partial fulfillment of the requirements for the egree of DOCTOR OF PHILOSOPHY IN ELECTRONIC ENGINEERING Facult of Engineering Mohamma Ali Jinnah Universit Islamaba Nov, 04

2 Copright 04 b Mubeen Ghafoor, PE0800 All rights reserve. Reprouction in whole or in part in an form requires the prior written permission of Mubeen Ghafoor or esignate representative. ii

3 eicate to m late granparents Mr. & Mrs. Abul Ghafoor Piracha (Ma their souls rest with pleasures) iii

4 ACKNOWLEDGMENT I woul like to thank Dr. Imtiaz (m supervisor), Dr. Noman Jafri an the whole team of Vision an Pattern Recognition Group (VisPRS) for their kin support an guiance. I woul also like to thank National ICT R&D fun [63], Ministr of IT an Telecom, Pakistan for funing part of this research. It is also acknowlege that the fingerprint atabases from fingerprint verification competition [53], [55] are use in this research along with the open source implementation of Hong s [30] an Chikkerur s [3], [4] algorithm. I am thankful to m parents an famil for their praers an continuous support. v

5 ABSTRACT Automate personal authentication has become increasingl important in our information riven societ an in this regar fingerprint base personal ientification is consiere to be the most effective tool. In orer to ensure reliable fingerprint ientification an improve fingerprint rige structure, novel fingerprint enhancement approaches are propose base on local aaptive contetual filtering. This issertation presents the aims an objectives of the research along with the motivation an propose research approaches for fingerprint enhancement mentione below. In the first part of this research, a twofol enhancement technique is propose that involves processing both in frequenc an spatial omain. The fingerprint image is first filtere in frequenc omain an then local irectional filtering in spatial omain is applie to obtain enhance fingerprint. Two major avantages of the propose enhancement technique are; avoiing the hazars of frequenc estimation errors an making spatial omain filtering quite simple b using a global D Gaussian smoothing filter. In orer to etermine the performance evaluation of the propose enhancement, etensive evaluation of the propose metho against well-known enhancement approaches has been carrie out on publicl available stanar atabases. Eperimental results emonstrate that propose enhancement performs better as compare to other well-known enhancement techniques. In the secon part of this work, a fingerprint image with non-uniform rige frequencies is consiere as a -D namic signal. A non-uniform stress on the sensor s sensing area applie uring fingerprint acquisition ma result in a nonlinear istortion that isturbs the local frequenc of riges, aversel affecting the matching performance. This stu presents a new approach base on short time Fourier transform (STFT) analsis an local aaptive contetual filtering for frequenc istortion removal an enhancement. In the propose approach, the whole image is ivie into sub-images an local ominant frequenc ban an orientation are estimate. Gaussian Directional ban pass filtering is then aaptivel applie in frequenc omain. These filtere sub-images are then combine using our propose technique to obtain the enhance fingerprint image of high rige qualit an uniform inter-rige istance. Eperimental results show efficac of the propose enhancement technique. vii

6 LIST OF PUBLICATIONS [] Mubeen Ghafoor, Imtiaz A. Taj, Waqas Ahma, M Noman Jafri, Efficient -fol Contetual Filtering Approach for Fingerprint Enhancement, IET Image Processing, Volume 8, Issue 7, p , Jul 04. [] Mubeen Ghafoor, Imtiaz A. Taj, M Noman Jafri, Fingerprint Frequenc Normalization an Enhancement using -D STFT Analsis, submitte to IET Image processing, (uner review). viii

7 TABLE OF CONTENTS ACKNOWLEDGMENT... v DECLARATION... vi ABSTRACT... vii LIST OF PUBLICATIONS... viii TABLE OF CONTENTS... i LIST OF FIGURES... ii LIST OF TABLES... iv LIST OF ACRONYMS... v Chapter... INTRODUCTION.... Overview.... Biometrics Traits use for Authentication..... Fingerprint as Biometric..... Face as Biometric Iris as Biometric Palmprint as Biometric Motivation: Fingerprint as Biometric Main Moules of Fingerprint Authentication Sstem Statement of Problem Purpose of the research Theoretical bases an Organization Summar... Chapter... LITERATURE SURVEY.... Fingerprint Acquisition Pre-processing an Enhancement ROI Segmentation Initial Enhancement...6 i

8 ..3 Orientation Estimation Rige Frequenc Estimation Contetual Filtering base Enhancement Binarization an Thinning Feature Etraction Minutiae Etraction False Minutiae Removal Feature Encoing an Matching General Methoolog of Local Minutiae Encoing an Matching Eisting Techniques of Minutiae Encoing an Matching Summar Chapter FOLD CONTEXTUAL FILTERING FOR ENHANCEMENT Propose Enhancement Scheme Frequenc Domain Filtering Spatial Domain Filtering Frequenc Response of Propose Filters Computational cost analsis an comparison Eperimental Results Databases an Test Protocol Matching Algorithm Performance Comparison with other enhancement techniques Summar Chapter ENHANCEMENT AND FREQUENCY DISTORTION REMOVAL67 4. Non-Uniform Inter-Rige Separation Distortion Problem Formulation Fingerprint as a non-stationar -D signal Filtering In STFT Frequenc Normalization Propose Algorithm STFT Computation Frequenc an Orientation Map Construction Block-wise Filtering in Frequenc Domain Frequenc normalization Enhance Image Construction Results Summar... 83

9 Chapter CONCLUSION AND FUTURE WORK Conclusion Future Work REFERENCES APPENDICES Appeni A Bi-linear interpolation for non-regular D gri Appeni B Comparison of computational compleit for contetual filtering base enhancement techniques i

10 LIST OF FIGURES Figure.: Different Biometrics use for human authentication... Figure.: Fingerprint Image Structure... 3 Figure.3 Common Minutia tpes Figure.4 Iris Structure... 4 Figure.5: Creases, Riges an Minutiae points in palmprint... 5 Figure.6: Human authentication base on ifferent biometrics: Pros an Cons... 6 Figure.7: Block iagram of Fingerprint Authentication Sstem... 7 Figure.8: An eample of ifferent fingerprint images with varing qualit... 9 Figure.: Block Diagram of Fingerprint Authentication... 3 Figure.: Block iagram of pre-processing an enhancement... 4 Figure.3: Images segmente base on intensit variance base metho... 6 Figure.4: Normalize images... 7 Figure.5: The angle of orientation... 8 Figure.6: Vector fiels epicting calculate orientations... 9 Figure.7: Frequenc estimation using -signature... 0 Figure.8: Contetual Filter esigne b O Gorman an Nickerson... Figure.9: Algorithmic flow iagram for filter-bank base image enhancement propose b Sherlock et al Figure.0: Some intermeiar results of Sherlock et al. propose enhancement scheme... 4 Figure.: Real part of Gabor filter... 5 Figure.: 3-D representation of real part of Gabor filter... 6 Figure.3: Block iagram of enhancement base on Gabor filtering... 6 Figure.4: Frequenc response of Gabor Filter... 7 Figure.5: STFT Analsis propose Chikkerur et al Figure.6: STFT filtering propose Chikkerur et al Figure.7: Application of STFT enhancement on fingerprint image... 9 Figure.8: Frequenc response of Log-Gabor filter Figure.9: Block iagram of enhancement base on Log-Gabor filtering base enhancement technique propose b Wang et al Figure.0: Binarization of images... 3 Figure.: Results of Thinning Process Figure.: Seven most common minutiae tpes Figure.3: Minutia Structure Figure.4: Eamples of false minutiae Figure.5: Results of Minutia Etraction after False Minutiae Removal Figure.6: Fingerprint encoing base on neighboring Minutia Figure.7: Fingerprint matching base on neighboring Minutia Figure.8: Fingerprint encoing base on Minutiae triplet structure propose b Jiang an Yau... 4 Figure.9: Left figure highlights the k-plet local structure for minutia 3 an right figure shows the graph representation for the fingerprint base on k-plet representation... 4 Figure 3.: Algorithm flow Diagram for the propose fingerprint enhancement technique Figure 3.: Algorithmic steps of frequenc omain filtering ii

11 Figure 3.3: Application of Fourier transform on fingerprint image Figure 3.4 : Frequenc response of the propose Ban-pass filter Figure 3.5: Enhance fingerprint images after propose irectional smoothing metho Figure 3.6: Algorithmic steps of spatial omain enhancement... 5 Figure 3.7: Enhance fingerprint images after propose irectional smoothing metho Figure 3.8: Propose Gaussian filter Figure 3.9 : Frequenc response of the propose Gaussian filter an its comparison with other contetual filters Figure 3.0: An eample of FMR(t) an FNMR(t) curves, EER is highlighte at the threshol t where the FMR(t) an FNMR(t) are equal Figure 3.: Eample: application of propose two-fol enhancement on fingerprint image an its comparison with other enhancement techniques Figure 3.: Eample : application of propose two-fol enhancement on fingerprint image an its comparison with other enhancement techniques Figure 3.3: DET graph of ifferent enhancement schemes Figure 4.: Fingerprint images showing the problem of non-uniform inter-rige separation Figure 4.: Fingerprint image epicting non-stationar nature Figure 4.3: Enhancing a sub-image of fingerprint in frequenc omain Figure 4.4 Block Diagram of propose enhancement technique... 7 Figure 4.5 Gaussian winow function Figure 4.6 Problem in normalize image construction Figure 4.7 Normalize gri construction map Figure 4.8 Interpolation on non-rigi gri Figure 4.9: Eample : application of the propose frequenc istortion removal enhancement technique on fingerprint image an its comparison with Gabor Enhancement... 8 Figure 4.0: Eample : application of the propose frequenc istortion removal enhancement technique on fingerprint image an its comparison with Gabor Enhancement... 8 Figure A.: Bilinear Interpolation on -D regular gri Figure A.: Bilinear Interpolation on non-regular -D gri iii

12 LIST OF TABLES Table : An analsis of computational compleit for ifferent well-known contetual filtering base enhancement techniques Table : An analsis of computational compleit for ifferent values of image sizes (M) an block sizes (m) Table 3: Summar of comparative analsis of the propose two-fol enhancement with other well-known techniques base on EER Table 4: Comparative analsis of the propose frequenc istortion removal enhancement technique base on EER iv

13 LIST OF ACRONYMS AFIS ROI FFT IFFT STFT FT BFS EER DET ROC FMR FNMR DNA Automate Fingerprint Ientification Sstem Region of Interest Fast Fourier Transform Inverse Fast Fourier Transform Short Time Fourier Transform Fourier Transform Breath First Search Equal Error Rate Detection Error Traeoff Receiver Operating Characteristic False Matching Rate False Non-Matching Rate Deoribonucleic Aci v

14 Chapter. Overview INTRODUCTION Biometrics are the measurable biological an behavioral characteristics of human beings an emploe to authentication techniques for their ientification. The use of biometrics in ientifing humans goes back to prehistoric times when humans use to aorn the caves with their biometrics like fingerprints an hanprints as their signatures. There are eviences that Bablonian use cla tablets with fingerprints as signatures in their business transactions as earl as 500 BC. The Chinese merchants use fingerprints as signatures to settle their business eals in the Fourteenth centur. In the recent ears, ue to the importance of securit applications an increase epenenc on network base e-business an e-banking transactions, research in securit relate fiels especiall secure ientification an ientit verification has attracte a lot of interest. For robust authentication, human biometrics base approaches are becoming increasingl common as compare to passwors or car base sstems, because the passwor or the car is likel to be hacke or stolen. Therefore, biometrics provie the most accurate, safe an, acceptable authentication metho []. Biometrics market has grown consierabl in the last a few ears an market trens suggest that its growth is still on the rise in the coming ears ue to rising securit concerns worl-wie. In a recent report, publishe b USA Homelan Securit News Wire [], the current biometrics market is estimate to be aroun $5 billion, an it is forecast to reach aroun $ billion b the ear 05.. Biometrics Traits use for Authentication Different tpes of biological an behavioral biometrics are use for human authentication. Some of the eamples of human biometrics, like fingerprints, Deoribonucleic Aci (DNA), ee retinas an irises, voice patterns, signatures, gait patterns, facial patterns an palmprints are shown in Figure.. The reliabilit of biological biometrics is far higher than that of behavioral biometrics. That is wh,

15 biological biometrics are more commonl use in securit applications. A brief escription of some of the biological biometrics is given in following subsections. Figure.: Different Biometrics use for human authentication.. Fingerprint as Biometric A fingerprint image can be consiere as a sinusoial wave pattern of oriente riges having almost same frequenc throughout the fingerprint. The crests an troughs of these alternating wave patterns are commonl known as riges an valles as shown in Figure. (a). Figure. (b) shows a zoome 3-D plot of small portion taken from fingerprint image of Figure. (a). The riges on the fingerprint image possess high egree of uniqueness because of certain lanmarks commonl known as Minutiae.

16 The term minutia means small etails, an in fingerprint contet, it is an information that can be erive from various tpes of iscontinuities of riges. (a) (b) Figure.: Fingerprint Image Structure (a) Fingerprint image, (b) Zoome 3-D plot of small portion taken from (a) Although there are several tpes of rige iscontinuities, but rige enings (en point of a rige) an rige bifurcations (a rige ivie into two riges) are consiere more important because of their reliabilit an uniqueness. A pictorial representation of rige ening an bifurcation is shown in Figure.3 [3]. (a) Figure.3 Common Minutia tpes (a) Minutia Ening. (b) Minutia Bifurcation. (b) 3

17 .. Face as Biometric Face is the most common biometric that human uses for authentication. The nee for face base automate authentication sstems is high because it can be acquire nonintrusivel an then can be use for authentication in wie range of commercial an law enforcement applications. Even though face recognition algorithms reache a certain level of maturit, et there remain major challenges that limit the use of face in real worl authentication sstems. These challenges inclue, change of illumination conitions, pose variations, facial isguise, aging affects, epressions variations, etc [4]...3 Iris as Biometric Human iris is a comple structure that eist between the cornea an the lens of the ee in the form of circular iaphragm. It is consiere as one of the most accurate biometric because of its unique patterns like furrows, freckles, crpts, an coronas an other features as shown in Figure.4 [5]. These features are highl consistent over the person s life span making iris a reliable choice in authentication applications. Figure.4 Iris Structure Although, iris base authentication sstems give high accurac rates, et acquiring the comple iris patterns eman goo qualit images that require epensive sensors. Aitionall, iris image acquisition is a bit complicate an tiresome. Also user 4

18 cooperation is require to ensure that the iris is capture properl from a preetermine istance from the focal plane of the camera...4 Palmprint as Biometric Palmprint image is acquire from the inner part of human han. It starts from wrist an ens at roots of fingers as shown in Figure.5 [6]. It provies much larger area as compare to fingerprint an also possesses aitional features like principal lines, wrinkles, minutia, riges an teture etc. The principal lines consist of life line, heart line an hea line. Figure.5: Creases, Riges an Minutiae points in palmprint.3 Motivation: Fingerprint as Biometric The assessment of biological biometrics like fingerprint, iris, face, palmprint, etc. are base on three important characteristics; uniqueness of features, stabilit over time an acceptabilit among masses. Base on these characteristics, ever biometric has its own strengths an weaknesses as shown an escribe in Figure.6. For eample, Face biometric is the most commonl use for authentication, but its automate authentication sstems are not ver reliable as face is a changeable feature epenent on epressions, viewing angle, pose, illumination conitions an aging effects [4]. Similarl, iris authentication sstems are consiere to be the most accurate among all the biometrics [7], et ue to their teious acquisition process, the acceptabilit of 5

19 these sstems is ver low. Palmprint is a relativel new biometric being use in human authentication sstems [8] an provies much larger area as well as aitional features as compare to fingerprint. However, owing to the large size of palmprint image, matching is not possible in real-time for large-scale atabases [9]. For eample, a well-known fingerprint matcher VeriFinger [0], that performs more than 5,000 fingerprint matches per secon; gives onl three matches per secon for palmprint images. Fingerprint authentication, on the other han, is reliable, fast an more acceptable among common masses. Therefore, it remains the most suitable biometric for large scale national ID, civil ientification an securit programs [3]. Accoring to a surve [], automate fingerprint authentication sstem (AFIS) ominates other biometrics having shares of aroun 33% of overall biometrics market. This provies an incentive of research an evelopment in fingerprint base authentication sstems in the coming ears. Figure.6: Human authentication base on ifferent biometrics: Pros an Cons 6

20 .4 Main Moules of Fingerprint Authentication Sstem Like other biometrics, fingerprint can be use as a metric for personal verification an /or ientification. Fingerprint verification involves - match because a quer (probe) fingerprint image with a given ientit is matche against a template store in the atabase against the same ientit. Whereas, fingerprint ientification involves -N matches because a quer (probe) fingerprint image is matche against all the template images store in a atabase in orer to fin the ientit of the person. The overall block iagram of fingerprint authentication is shown in Figure.7. It involves image acquisition, pre-processing an enhancement, feature etraction, feature encoing an matching stages. Figure.7: Block iagram of Fingerprint Authentication Sstem During the enrollment process which involves registration of a person in a atabase, all the initial processes up to the template generation stage are performe, an once the template is generate, it is store in the atabase. During authentication all these initial processes are followe for onl quer image an are not require for templates from atabase. Therefore, matching is consiere to be an on-line process an 7

21 enrollment is consiere to be an off-line process. If there is no wear an tear in the fingerprints, it will remain stable throughout the life time. However, ue to wear an tear, the templates are generall consiere stable over 5 to 0 ears an enrollment is performe again after this perio. The fingerprint template is an abstract representation of specific fingerprint features. For fingerprint authentication sstems the most reliable features are consiere to be minutiae points which generall consist of rige ening an bifurcation [], eplaine above. The minutiae base matching methos are ver sensitive to the occurrence an qualit of minutiae, as an missing minutia or eistence of false minutia can egrae the performance an error rate of the matching metho [], [3]..5 Statement of Problem In orer to overcome the problem of unreliable features etraction, a pre-processing an enhancement technique is imperative to fingerprint recognition sstem. The enhancement technique is usuall esigne to achieve the best results b removing ifferent imperfections containe b fingerprint images. These imperfections can eist in the form of non-uniform illumination ue to sweating effects on fingertips or imperfect sensors, scars an broken riges, an non-uniform rige separations, which occur ue to non-uniform contact with the sensor. Figure.8 shows an eample of ifferent fingerprint images with varing qualit. Figure.8 (a) shows a goo qualit fingerprint image with high contrast between the riges an valles. Figure.8 (b) & (c) shows fingerprint images with low contrast an having broken riges an scars. Figure.8 () shows a low qualit fingerprint with insufficient istinction between riges an valles. Figure.8 (e) shows a fingerprint having non-uniform inter-rige separation, which occurs ue to nonuniform contact with sensor [4], [3]. In case, minutiae are irectl etracte from istorte images there will be man spurious minutiae, resulting in false matching as well as increasing the matching time. In orer to overcome this problem, researchers commonl emplo the application of irectional contetual filtering base enhancement on fingerprint image before the feature etraction stage. 8

22 (a) (b) (c) () (e) Figure.8: An eample of ifferent fingerprint images with varing qualit. (a) Goo qualit image, (b) & (c) Fingerprint with low contrast an broken riges, () Low qualit fingerprint image with insufficient istinction between riges an valles, (e) Fingerprint having nonuniform inter-rige separation.6 Purpose of the research Although fingerprint recognition is a mature fiel of research an the algorithms for image enhancement, feature point etraction an matching are well establishe, in case of severel istorte images still there is a lot of room of improvement. This research focuses on robust enhancement techniques which can overcome problems in severel istorte images. The main tasks involve in this research are as follows: 9

23 To review the ifferent stages i.e. preprocessing, enhancement, feature etraction an matching techniques involve in fingerprint authentication sstems an then review how the contribute in the overall matching results of the authentication sstem. To review the ifferent istortions in the fingerprint image an also review the ifferent enhancement algorithms to overcome these istortions. To perform the istortion removal from fingerprint images. Two stuies are presente in this thesis. First stu overcomes the problems like Non-uniform illumination Broken riges an scars Distorte rige valle structures (low contrast rige valle structure) In this stu, a novel two-fol contetual filtering base enhancement scheme is propose [5]. In orer to etermine the performance of the propose algorithms, a comparative analsis is performe with major recognition schemes b calculating the equal error rates on stanar fingerprint atabases. Secon stu aresses the problem of non-uniform inter-rige separation along with other imperfection [6]. Although contetual filtering base approaches work well to enhance the fingerprints, but the o not rectif the nonuniform inter-rige separation. In this research a novel enhancement technique is presente that simultaneousl normalize non-uniform inter-rige frequencies an also remove other istortions from fingerprint image..7 Theoretical bases an Organization This issertation is organize into five chapters whose contents are escribe below. Chapter gives the problem statement an the organization of issertation. 0

24 Chapter presents the literature surve on the ifferent algorithm steps involve in fingerprint enhancement, encoing an matching. A etaile literature surve on some of the most prominent contetual filtering base enhancement techniques is also presente. Chapter 3 escribes a novel fingerprint enhancement technique base on local aaptive contetual filtering. Propose technique utilizes processing both in frequenc an spatial omains to enhance a fingerprint image. This chapter also inclues the frequenc response of the propose filters with other contetual filters propose in literature, followe b the comparative analsis of propose enhancement scheme with other major enhancement schemes base on contetual filtering. Chapter 4 escribes a novel contetual filtering base enhancement technique to simultaneousl aress the problems of non-uniform inter-rige istortion along with other imperfection in the fingerprint image. Chapter 5 gives the conclusion of the research work an suggest future research..5 Summar This chapter gives overall introuction of this issertation. An overview of ifferent biometrics use worlwie is presente an then motivation of fingerprint as a most reliable an acceptable biometric is presente, because of its uniqueness an stabilit over time. In the en, a brief overview of the purpose an objectives to be achieve in this research are presente.

25 Chapter LITERATURE SURVEY Fingerprint is the most mature biometric to be use for human authentication with applications ranging from securit to commerce an banking. Although, fingerprint base authentication is an establishe fiel of research, et research is still going on ue to stringent time an reliabilit requirements for large scale applications. This issertation focuses on the importance of fingerprint enhancement an its significance in making the fingerprint base authentication sstem more robust. This chapter iscusses the overall fingerprint base authentication sstem an the etails regaring ifferent algorithmic for blocks in the sstem. The overall fingerprint authentication sstem can be broal ivie into two phases; fingerprint encoing an matching. In encoing, a fingerprint image is initiall acquire from a sensor, followe b a feature etraction stage. In feature etraction phase, features like minutiae are etracte from fingerprint image. Various techniques have been propose for automatic etraction of minutiae features from a fingerprint image. Maio an Maltoni etracte minutiae points irectl from gra images base on a rige line following algorithm [7]. This stu shows acceptable results, however, owing to high computational cost this technique is not reail use for minutiae etraction [3]. Watson et al. [8], working at National Institute of Stanars an Technolog (NIST), evelope MINDTCT, an open source software for minutiae etraction. MINDTCT binarizes a fingerprint image b assigning a binar value to ever piel base on its neighborhoo in irection of riges an minutiae points are then etracte base on minutia searching pattern. The minutiae etection using this metho is ver simple an fast. However, these irect methos usuall fail because fingerprint images contain some imperfections like non-uniform gra levels along the riges, scars, creases an broken riges. These imperfections ma cause false an inconsistent etection of features especiall minutiae points, which ma lea to false matching. The most consistent an reliable techniques for minutiae feature etraction are base primaril on incorporating a pre-processing an enhancement stage prior to feature

26 etraction step. The pre-processing an enhancement stage is usuall esigne to achieve best results b removing ifferent imperfections containe in fingerprint images. Feature etraction step is followe b feature encoing phase, where a biometric coe of the fingerprint is calculate an store in atabase as a reference template for that fingerprint. In fingerprint matching stage, the templates (biometric coes) of fingerprint images are loae from the atabase an the features from these templates are then matche. Figure. shows an overall block iagram of steps involve in fingerprint authentication. A etaile review of these steps involve in fingerprint authentication e.g. fingerprint acquisition, preprocessing, encoing an matching techniques are presente in following subsections. Fingerprint Acquisition Pre-processing an Enhancement Feature Etraction Feature Encoing Matching Database Figure.: Block Diagram of Fingerprint Authentication. Fingerprint Acquisition For forensic an criminal investigation, where fingerprint image can be partial (latent), eperts rel on minutiae an rige features, as these features are even accepte in the court of law. Also, in case of automate fingerprint matching, minutia base matching algorithms are consiere to be the most accurate an robust. In orer to reliabl etract minutiae points, fingerprint images must be acquire at a minimum resolution of 400 pi. Most of the fingerprint sensors reail available in the market are of 500 pi an can be easil integrate as off the shelf evices. For research purpose Fingerprint Verification Competition (FVC) On-Going [9], conucts evaluation of latent fingerprint algorithms. The use ifferent sensor technolog for atabase collection an the atabases are publicall available for research purpose. 3

27 . Pre-processing an Enhancement The performance of feature etraction an matching algorithms epens heavil on qualit of input fingerprint images. In orer to ensure reliable feature etraction, fingerprint images are passe through a series of pre-processing an enhancement steps as shown in Figure.. The main moules inclue in pre-processing an enhancement are; ROI (Region of Interest) mask segmentation, enhancement followe b binarizing an thinning. In ROI segmentation, the fingerprint region is etracte from backgroun base on local variance an irectional coherence of fingerprint image. After segmentation, fingerprint image is enhance using ifferent general purpose image processing techniques [0], [], []. These enhancement techniques are emploe to enhance the overall contrast of the fingerprint image. However, these techniques can not improve the rige structure that is amage mainl because of scars an broken riges an creases. Fingerprint Image ROI segmentation Orientation Estimation Initial Enhancement Frequenc Estimation Contetual Filtering base Enhancement Binarization Thinning Figure.: Block iagram of pre-processing an enhancement 4

28 In orer to improve the rige structure an solve the problem of broken riges, creases an scars, fingerprint images are generall enhance using contetual filtering. In enhancement base on contetual filtering, filters are esigne using local orientation an frequenc of riges as parameters. For this purpose, rige orientation an frequenc is estimate in vali fingerprint region. Rige structure qualit is then enhance b using contetual filtering base enhancement technique. Minutia features are reliabl etracte from thinne (skeletonize) image. That is wh in the last stage of preprocessing an enhancement, the enhance image is first binarize followe b a thinning process... ROI Segmentation Region of interest (ROI) segmentation is performe to etract fingerprint region an remove backgroun region an other irrelevant information from the fingerprint image. This stage is also referre to as foregroun/backgroun etection. ROI etraction is necessar, so that onl vali features are etracte uring feature etraction stage. In ROI segmentation, the non-overlapping block-wise approaches are generall use for fast computation an eas ecision making. Maio an Maltoni [7], propose a graient magnitue base metho for segmentation of low contrast fingerprint images. In this approach, variance of local intensit is first calculate an then compare against a threshol efine b the global intensit variance of the image. If the local variance is less than the global threshol, then block is classifie as a backgroun region, otherwise, a foregroun region. The gra level variance for a block of size TT is efine as: T T k Gi, j M i, j, V (.) T i0 j0 where V(k) is the variance for block k, G(i, j) is the gra level value at piel (i, j) an M(i, j) is the mean gra level value for the block k. The eperimental results obtaine after appling variance base segmentation are shown in Figure.3. In another stu, Bazen et al [3], propose ROI segmentation base on coherence of rige irection. 5

29 6 Figure.3: Images segmente base on intensit variance base metho.. Initial Enhancement Fingerprint images usuall contain some imperfections like non-uniform ink intensit ue to sweating effects, humiit an sensor efects. This results in istorte nonuniform gra levels along the riges an valles patterns. Removal of non-uniform illumination an unwante aitive noise has been tackle in initial preprocessing steps of mean an variance normalization b Kim et al. [0]. Normalization basicall stanarizes the gra levels ajustment, so that the processe image can have esire range of mean an variance values over the whole fingerprint image. Let g(i, j) represents the gra level value at piel (i, j) an I(i, j) is the normalize gra level value at piel (i, j). Mathematicall, otherwise M j i g V V M j i I M j i g if M j i g V V M j i I o o,, ), ( ), (,, ), ( 0 0 (.)

30 where M an V are estimate mean an variance of g(i,j) an M 0 an V 0 are the esire mean an variance values. The normalization is usuall carrie out globall. However, local normalization can be use to remove slow intensit range variations ue to non-uniform contact of finger with the acquisition evice [0]. The eperimental results obtaine after appling normalization processing as iscusse above are shown in Figure.4. Figure.4: Normalize images In another interesting approach, Greenberg et al. [] propose Wiener filtering for fingerprint enhancement. Willis et al [], emploe a simple frequenc omain enhancement technique for removal of illumination changes an unwante scars in fingerprint in one step. In this approach, the enhance image is calculate b multipling Fourier transform of a block b the k th power of power spectrum an then computing its inverse Fourier transform. Chanra et al. [4] propose a computationall efficient enhancement scheme base on meian filtering. 7

31 These general purpose enhancement techniques work well to enhance overall contrast an remove noise from fingerprint image. However, these techniques cannot improve the rige structure that is amage mainl because of scars an broken riges. In orer to improve the rige structure an solve the problem of broken riges, creases an scars, fingerprint images are generall enhance using contetual filtering. For this purpose, rige orientation an frequenc images are estimate before appling filtering...3 Orientation Estimation In orientations estimation, an orientation map or image O(, ) is constructe that provies the ominant irection of unerling local rige structures [3]. This orientation image O(, ) gives ominant rige orientation θ i,j of each piel (, ) with the horizontal ais as shown in Figure.5 [3]. Figure.5: The angle of orientation Reliable orientation estimation is ver important as enhancement is epenent on it. Furthermore, orientation image is also use in feature etraction, an feature encoing. Turroni et al. [9], propose an aaptive orientation estimation algorithm for low qualit fingerprint images containing man creases an scars. The most common methos for orientation estimation are generall base on computing graients in spatial omain [5]. However, simple graient base methos leas to problems ue to the non-linearit an iscontinuit of sines an cosines. For fingerprint images, various methos are propose to overcome this problem. Bazen et al. [8], compute orientation base on principal component analsis of the graient vectors. Kass an Witkin [6] propose orientation estimation base on oubling the angles. Ratha et al. [7], estimate the ominant rige orientations θ i,j b calculating 8

32 9 multiple graient estimates within a 7 7 winow centere at ( i, i ) as shown in following equations [7]., tan 90 ij G G G (.3),,, h j i k j i k h k h G (.4) , h k j i k h G, (.5)., h k j i k h G (.6) Figure.6: Vector fiels epicting calculate orientations

33 The eperimental results of graient base rige orientation calculation are shown in Figure.6. These orientation estimation algorithms work well for fingerprint images an can reliabl etect ominant local rige orientations...4 Rige Frequenc Estimation In frequenc estimation, a rige frequenc image or frequenc map is constructe that provies rige ensit in a local region. Hong et al. [30], estimate frequenc in spatial omain b -signature of riges within an oriente winow as shown in Figure.7. In this approach, the fingerprint image is ivie into small blocks an then for each block frequenc is calculate b counting riges within an oriente winow an then iviing it b the istance of the first an last rige within that oriente winow. This propose approach is straightforwar an efficient. However, for nois fingerprint images the frequenc estimation is not accurate in spatial omain. Chikkerur et al. [3], propose Short Time Fourier Transform (STFT) analsis for simultaneous estimation of frequenc an orientation of fingerprint image. Figure.7: Frequenc estimation using -signature..5 Contetual Filtering base Enhancement In image enhancement base on contetual filtering, the filter parameters are tune aaptivel uring filtering process base on some local characteristic of the image contents. In fingerprint enhancement, since rige flow patterns are to be enhance, the 0

34 filters are, therefore, esigne to enhance rige flow structure b tuning onto local rige frequenc an irection [3]. Although, the techniques of enhancement are ifferent, et their purpose is same. i.e. perform filtering base on rige pattern, so that ifferentiation between rige valle structure can be enhance as compare to noise an other artifacts an also fill the broken rige linkages b appling filtering along rige irection. In this section, a etaile literature surve on current state-of-the-art contetual filtering base fingerprint enhancement techniques both in spatial an frequenc omains is presente. Although, a lot of algorithms can be foun in literature, the most prominent ones are iscusse. In orer to enhance fingerprint image, O Gorman an Nickerson [46] were the first to introuce contetual filters. The propose enhancement technique was base on convolution of each piel in fingerprint image with a filter. The filter is bell shape, elongate along the rige irection an esigne for a constant rige frequenc as shown in Figure.8 [46]. Figure.8: Contetual Filter esigne b O Gorman an Nickerson A set of 6 rotate filters are erive from the propose filter in orer to cater for the ifferent rige orientations in fingerprint image. Convolution of each piel is performe using a filter whose orientation matches the orientation of that piel. Since this filter is esigne for a constant rige frequenc, so it oes not eploit the sinusoial wave pattern of fingerprint. In frequenc omain, the first most notable contetual filtering technique for fingerprint enhancement is propose b Sherlock et al. [47], [48] where enhancement is achieve b esigning irectional filter bank base on the ominant frequenc ban. In propose stu, the fingerprint image is

35 converte into frequenc omain through fast Fourier transform (FFT). The ominant ban in the image is then estimate in the frequenc omain, which is then use to esign irectional filter banks suitable for enhancing the require etails an suppress the unnecessar or nois etails. The irectional filter bank in frequenc omain is efine b multiplication of two filters as given b equation (.7) [47]: H(, ) H raial ( ) H ( ), (.7) angle where H raial is the ban-pass filter an is epenent on the local rige frequenc ρ (equation (.8)) an H angle is irectional filter an is epens on the local rige orientation θ (equation (.9)). Mathematicall, n BW H raial n n BW 0, (.8) where ρ BW an ρ o are the esire banwith an center frequenc. H angle c cos if BW 0 otherwise BW, (.9) where φ BW is angular banwith of the filter an φ c is the orientation at which value of filter is maimum. Each irectional filter H is then multiplie b the Fourier transform of fingerprint image F to obtain a bank of irectionall filtere images PF i. After filtering in frequenc omain all the filtere images are brought to spatial omain PI i through inverse Fourier transform (IFFT) of each image. The final enhance image I enh is constructe b selecting piel values from the bank of irectionall filtere images PI i base on the local orientation of each piel. The algorithmic flow iagram for filter-bank base image enhancement is shown in Figure

36 .9. Some of the intermeiar results prouce b Sherlocks s algorithm [48] are shown in Figure.0. However, this scheme works well onl if frequenc (inter-rige separation) is constant throughout fingerprint image, as filter bank is esigne base on ominant frequenc ban. Figure.9: Algorithmic flow iagram for filter-bank base image enhancement propose b Sherlock et al. [48] 3

37 (a) (b) (c) () (e) (f) (g) Figure.0: Some intermeiar results of Sherlock et al. [48] propose enhancement scheme 4

38 Hong et al. [30], propose spatial omain base fingerprint enhancement technique using Gabor filters because of their frequenc an orientation selective properties. The enhancement proceure using Gabor filter is straightforwar an is implemente b convolving each piel of the fingerprint image with a Gabor filter properl tune base on local frequenc an orientation estimates. A Gabor filter can be represente as a sinusoial wave of specific frequenc an orientation, moulate b a Gaussian envelope. However, for fingerprint enhancement, the real part of -D Gabor filter having frequenc f an oriente at an angle θ is use given b equation (.0). g, :, f ) ep.cos(f ), (.0) ( θ = cos θ + sin θ, (.) θ = - sin θ + cos θ, (.) where σ an σ are the stanar eviations of the Gaussian envelope along the -aes an -aes, respectivel. Figure. (a) shows a cosine wave with frequenc f. Figure. (b) shows a Gaussian envelope. Figure. (c) shows real part of -D Gabor filter. A 3-D representation of real part of Gabor filter is shown in Figure.. (a) (b) (c) Figure.: Real part of Gabor filter, (a) cosine wave, (b) Gaussian envelope (c) real part of Gabor filter obtaine b multipling (a) an (b) 5

39 Figure.: 3-D representation of real part of Gabor filter The block iagram of enhancement base on Gabor filtering presente b Hong et al. [30] is shown in Figure.3. Figure.3: Block iagram of enhancement base on Gabor filtering 6

40 Since this enhancement technique involves -D Gabor filtering for each piel, base on its own frequenc an orientation. Therefore, the methoolog is effective for enhancing fingerprint images with varing inter-rige frequencies. The frequenc estimates can be erroneous ue to presence of scars an broken riges in a localize region, thus the qualit is greatl epenent on the eactness of frequenc estimates. Another rawback of Gabor base enhancement is that the frequenc response of Gabor filters has a DC component. This DC component can be observe from the frequenc plot of the Gabor filter as shown in Figure.4. Proper enhancement using these Gabor filters is not possible because the DC component ecrease the istinction between the alternating rige an valle structures. The technique is also computationall intensive, because for ever location a separate -D Gabor filter is use for enhancement. as iscusse in section 3.3. (a) (b) Figure.4: Frequenc response of Gabor Filter, (a) Fourier Spectrum of real part of Gabor Filter, (b) -D Frequenc response of traitional Gabor Filter Another variation of contetual filtering base on short time Fourier transform (STFT) is propose b Chikkerur et al. [3], that applies irectional filters in frequenc omain locall. The overall algorithm is ivie into two stages; STFT analsis followe b a filtering stage. The block iagram of STFT analsis is shown in Figure.5. This stu moels a non-stationar fingerprint image as a locall stationar signal. Therefore, enhancement scheme ecomposes image into small 7

41 overlapping blocks such that inter-rige istance is almost uniform within a block. Then Fourier transform of each block is compute. The orientation map, frequenc map an mask region of the fingerprint image are all simultaneousl estimate uring STFT analsis. Figure.5: STFT Analsis propose Chikkerur et al. [3] The net stage is STFT filtering; the overlapping blocks are separatel processe in frequenc omain to combine the avantage of frequenc omain filtering an local aaptation. The enhance blocks are then obtaine b taking inverse Fourier transform. The final enhance image is obtaine b tiling together all the enhance blocks. The block iagram of STFT base filtering is shown in Figure.6. However, ue to block b block processing, iscontinuities along block bounaries appear, which ma also affect the rige continuit as shown in Figure.7. 8

42 Figure.6: STFT filtering propose Chikkerur et al. [3] (a) (b) Figure.7: Application of STFT enhancement on fingerprint image (a) Fingerprint image 44_.tif, (b) Enhance image after appling STFT metho 9

43 Wang et al. [3], propose fingerprint enhancement in frequenc omain, base on Log-Gabor filters as an improvement to traitional Gabor filters propose b Hong et al. [30]. A -D Log-Gabor filter is given b equation (.3) an is shown in Figure.8. H r ( u, v) H ( u, v) H ( u, v) (.3) H r ( u, v) log( r / f ep Wr 0 ) (.4) H ( u, v) ( ep 0) W (.5) (a) (b) (c) () Figure.8: Frequenc response of Log-Gabor filter (a) -D log Ban-pass filter, (b) -D log fan-filter, (c) -D Log-Gabor filter, () -D Frequenc response of Log-Gabor 30

44 Unlike traitional Gabor filters, Log-Gabor filters have no DC component an have an etene tail at higher frequencies as shown in Figure.8 (). This scheme uses Hong s metho to compute the frequenc an orientation map. Whereas, the enhancement proceure is similar to that of Chikkerur et al. [3], that inclues application of localize frequenc omain filtering. The overall flow iagram of Log- Gabor base enhancement technique is shown in Figure.9. Figure.9: Block iagram of enhancement base on Log-Gabor filtering base enhancement technique propose b Wang et al. [3] Carsten [49], recentl, introuce curve Gabor filters for enhancing the curv rige valle structure in fingerprint image. C. Tang et al [50] applie secon-orer oriente partial ifferential equations (SOOPDE) to enhance fingerprint images. The propose technique is effective in joining broken fingerprint riges as well as it can smooth irregular riges in fingerprint images, however, it also isturbs the minutia information that is vital for fingerprint matching. F. Turroni et al. [5], propose an enhancement algorithm where contetual filtering is applie using an iterative metho. The algorithm starts enhancing fingerprint image b appling contetual filtering on regions of high-qualit an then epaning towars low-qualit regions. P. Sutthiwichaiporn et al. [5] propose an iterative fingerprint enhancement algorithm b using Gaussian-matche filters. These Gaussian-matche filters o not use the local contetual information e.g. local rige orientation an local rige 3

45 frequenc. However, the computational compleit of these algorithms is high ue to their iterative nature...6 Binarization an Thinning After fingerprint enhancement, the enhance images are generall binarize before features are etracte from them. Binarization is the process that converts a gra level image into a binar image, where white piels represent the foregroun riges, an the black piels represent the backgroun valles. Figure.0: Binarization of images Binarization is performe b choosing a threshol value an then classifing all piels with values above this threshol as white, an all other piels as black. The problem is how to select a correct threshol. This problem is resolve b image enhancement 3

46 using the filter banks, because the reuce the illumination variations an have zero mean ue to rejection of all lower frequencies. Thus, the DC level of the filtere image i.e. zero can be use as a global threshol. The results of binarization process are shown in Figure.0. The final enhancement step tpicall performe prior to feature etraction is thinning. Thinning is a morphological process that reuces the with of riges to a single piel [33], [34]. The application of the thinning algorithm to a fingerprint image preserves the connectivit of the rige structures while forming a skeletonize version of the binar image. This thinne image is then use in the net step of feature etraction. The results of thinning process are shown in Figure.. Figure.: Results of Thinning Process.3 Feature Etraction Fingerprint images contain man unique features that are use b researchers for fingerprint matching. For eample, riges, singular points (core point) an minutiae. Core point is the north most point of the inner most rige in fingerprint image [6]. 33

47 Accoring to the ANSI/NIST-ITL stanar working raft for ata format of fingerprint features, a core is locate at the focus of the innermost re-curving rigeline of a rige pattern: if the rige is viewe as a section of a circle, the core is the center of that circle; if the rige is viewe as an ellipse or parabola, the core is the focal point of that curve. Core point is consiere as reference point in fingerprint an can be effectivel use for fingerprint classification an matching [35], [36], [37]. However, in case of latent or partial fingerprint images, presence of core point is oubtful. Furthermore, core point etection an classification algorithms are error prone an ma increase the overall error of the sstem. The most common an reliable features use for fingerprint matching techniques are minutiae. These minutiae features were first observe b Sir Francis Galton base on the iscontinuities of local rige structure []. Therefore, these minutiae points are also known as Galton etails. Ever minutia point has its own position coorinates an angle, an a pattern of such points can be use for reliable iscrimination of fingerprints. Although, there are several ifferent tpes of minutiae, seven most common minutiae tpes are shown in Figure. [3]. Figure.: Seven most common minutiae tpes Among these minutiae tpes, rige ening an rige bifurcation are most commonl use for fingerprint authentication. This is because rige ening an rige bifurcation are ver stable an the algorithms to automaticall etect these features are ver accurate in comparison to the rest. Moreover, all other minutiae features can be realize as combinations of rige enings an rige bifurcations. Therefore, there is no nee to etect other features separatel. 34

48 These minutiae features are generall etracte from thinne fingerprint image. Thinning algorithms also introuce some irregularities an noise in rige structure, which leas to etection of spurious minutiae. Since the matching results heavil epen on qualit of etracte minutiae. Therefore, to ensure the etection of genuine minutiae onl, researchers propose certain heuristics to reject the false minutiae as a post-processing step for minutiae valiation [38], [39], [40]. The algorithm of minutiae etection an false minutiae removal is iscusse below..3. Minutiae Etraction The caniate minutiae are etracte b scanning the local neighborhoo of each rige piel (having value 0 in thin image) in a winow of 3 3. A rige piel is classifie as a caniate minutia base on the number of 0 to transitions occurre while scanning its 8 neighboring piels in clockwise irection. If there is onl one 0 to transition then the piel is classifie as rige ening an if the number of 0 to transitions is 3 then the piel is classifie as rige bifurcation. Rige ening an rige bifurcation scenario is represente in Figure.3 [3]. (a) (b) Figure.3: Minutia Structure (a) Rige Ening, (b) Rige Bifurcation.3. False Minutiae Removal False minutiae ma be introuce in the fingerprint image ue to the factors such as nois image, an image artifacts create b the thinning process. Minutiae postprocessing technique is use to filter out the false minutiae from the fingerprint image. Figure.4 illustrates some eamples of false minutiae, which inclues spur, hole, triangle, an spike structure Maltoni an Maio [3]. 35

49 Spike Hole Triangle Spur Figure.4: Eamples of false minutiae There are numerous propose approaches for image post-processing base on certain heuristics to eliminate false minutiae. In this stu, the technique propose b Marius Tico an Pauli Kuosmanen [39], has been followe for minutiae post-processing. The propose technique authenticates each caniate minutia b analzing the thin image in a winow of size W W aroun each caniate minutia. An eample of final results of minutiae etraction after false minutiae removal is shown in Figure.5. Figure.5: Results of Minutia Etraction after False Minutiae Removal 36

50 As a result of minutiae etraction an false minutiae removal proceure, a set of minutiae will be obtaine, where each minutia point is represente b its location along with its reference irection. Minutia tpe (rige ening or bifurcation) can also be mae part of this feature set an can be use in matching. However, minutia tpe etection is erroneous that is wh it is not generall mae part of minutia feature set..4 Feature Encoing an Matching Once the features are etracte from a fingerprint image, the net important step is to represent these features into a compact representation where all the information of etracte features remains intact an reunant information is remove. This transformation is calle feature encoing process. An important consieration for the encoing scheme is to ensure the suitabilit of the transforme ata to the matching algorithm. Fingerprint encoing an matching algorithm are inter-epenent on each other. These algorithms can be broal characterize into three groups, i.e. matching base on correlation, matching base on teture escriptors an matching base on minutiae features. In correlation base algorithms, fingerprint images are superimpose over each other for ifferent alignments an a similarit score is calculate base on their correlation. The rawback of these techniques is that the o not properl hanle rotational an scaling misalignments. Moreover, these algorithms have high computational compleit an storage costs which make them unsuitable for fingerprint matching. In teture base encoing an matching techniques, a reference point is etecte initiall an fingerprint area aroun this point is then encoe base on teture patterns [35] or b mapping minutiae in fingerprint image onto that reference point [36], [37].. The reference point in these schemes is normall the core point (singular point) of fingerprint [3]. These schemes are not onl computationall efficient, but also require less storage space. However, these techniques are not use in commercial large scale applications, because core-point etection algorithms are not reliable. Therefore, core-point base mapping schemes ma increase the overall error rates of matching sstem. The most wiel encoing an matching techniques rel on minutiae features base point pattern encoing an matching techniques. Apart from being ver accurate, these techniques support partial fingerprint matching. The general methoolog of 37

51 fingerprint encoing an matching base on neighboring minutiae is iscusse in following subsection..4. General Methoolog of Local Minutiae Encoing an Matching Encoing Consier a template fingerprint with M minutiae ( i, i, θ i ) T,(i=,,3,, M). Each minutia point in the template fingerprint M i is represente b,, θ. Where an are the location coorinates, an θ is the orientation or angle along -ais of each minutia point in the fingerprint image. In orer to encoe a template fingerprint image, each minutia is selecte as reference an its istance an relative angle is measure from its neighboring N minutia as shown in Figure.6. Figure.6: Fingerprint encoing base on neighboring Minutia Let P={(,, θ ) T,..( N, N, θ N ) T }, where P enotes the set of N neighboring minutiae use in encoing of a reference minutia R. The final biometriccoe of the fingerprint is a combination of all such iniviual minutia encoings R i, where i=,,3 M. This biometric-coe is store in the atabase as a template of that fingerprint an use for future matching. 38

52 Matching In orer to match a caniate fingerprint with template fingerprints store in the atabase, the fingerprint is acquire from the fingerprint sensor. Preprocessing, enhancement an minutiae etraction an encoing steps for the caniate fingerprint image are same as the template fingerprint. For matching two fingerprints base on point pattern matching technique, an ehaustive search algorithm is emploe, where ever minutia coe from template will be matche with all minutia coes in caniate fingerprint to fin the best minutia pair matching of template an caniate fingerprint as shown in Figure.7. Figure.7: Fingerprint matching base on neighboring Minutia 39

53 Matching one minutia coe of template to one caniate minutia coe requires matching of ever component (neighboring minutia) of template minutia coe with caniate minutia coe. This is because the orer of occurrence of neighboring minutia is not certain especiall in case of an missing or spurious neighboring minutia. Therefore, for fining correct corresponence ever component (neighboring minutia) of minutia coe of template coe must be matche with ever component of minutia coe of caniate coe. In orer to match one template minutia coe, consisting of n neighbors with one caniate minutia coe, n component matching s are require. The accurac of matching algorithm generall increases as the number of neighboring minutiae for encoing increases. However, b increasing number of neighbors for encoing not onl the size of biometric-coe increases, but also the number of computations involve in matching. The overall computational compleit of minutia neighbor base point pattern matching algorithms become M N n, where n is number of neighbors use for encoing an M an N are number of minutiae in template an caniate fingerprints respectivel. Base on minutiae pair matching, a score is calculate that escribes the similarit between two fingerprint images..4. Eisting Techniques of Minutiae Encoing an Matching Although, the encoing an matching techniques are ifferent, et the basic methoolog remains the same an that is, each minutia is encoe base on the relative istances an angles with neighboring minutiae [4], [4], [43]. Jiang an Yau [], propose a rotation an translation invariant encoing an matching technique base on minutia-triplet structure. In minutia-triplet base encoing, each minutia is taken as center an a feature vector is forme b taking its istance an orientation ifference with its nearest two neighboring minutiae in polar coorinates. An eample of encoing a minutia m i ( i, i, θ i ) with respect to its two nearest minutiae n 0 ( n0, n0, θ n0 ) an n ( n, n, θ n ) is shown in Figure.8 [94]. The feature vector for minutia m i can be represente as: F i = (r i0, r i, θ i0, θ i, φ i0, φ i, n i0, n i, t i, t 0, t ) T (.6) 40

54 Figure.8: Fingerprint encoing base on Minutiae triplet structure propose b Jiang an Yau [] In equation (.6), r i0 an r i represents the istance between the minutia m i an its nearest neighboring minutiae n 0 an n in polar coorinates. θ i0 an θ i represents the orientation ifferences between minutia m i an its corresponing neighboring minutiae. φ i0 an φ i are the ifferences of orientations between θ i an the orientation of the line segment connecting m i an corresponing neighboring minutia n 0 an n. n i0 an n i represents the number of riges between minutia m i an n 0 an n i. Finall, t i, t 0, t correspons to the tpe of each minutia in the minutia-triplet structure, e.g. ening or bifurcation. Jiang an Yau [], then propose a local minutiae matching scheme b computing similarit between all possible combinations of minutiae pairs base on weighte Eucliean istance between their corresponing feature vector. The overall matching score between caniate an template fingerprints can be estimate b counting the number of matche minutiae pairs. In orer to improve the matching score estimates, a consoliation stage is applie to valiate whether the local matching results hols at the global level too. This is performe b aligning all the locall matche minutiae pairs to a minutiae pair that best efine the transformation between the caniate an template minutiae pairs. For matching partial fingerprints, Jea et al. [43] emploe neural networks to calculate normalize matching score within the overlapping region of caniate an template fingerprints. Ratha et al. [3], propose fie raius base local encoing technique to overcome the problems of spurious or missing minutiae. In this scheme, a feature vector is 4

55 forme for ever minutiae b encoing all the neighboring minutiae that eists within some fie raius from each minutia. This leas to variable coe length for each minutia an can tolerate spurious or missing minutiae at the cost of increasing compleit of local matching algorithm. Tico et al. [44], propose another variation of minutiae encoing an matching, where each minutia is encoe base on the orientation fiels in the close proimit of iniviual minutia. Feng [45], presente, a hbri technique that uses fie-raius local minutiae as well as orientation information for fingerprint matching. Figure.9: Left figure highlights the k-plet local structure for minutia 3 an right figure shows the graph representation for the fingerprint base on k-plet representation Chikkerur et al. [4], propose a graph representation to encoe fingerprint base on local structure calle k-plet. In k-plet representation, each minutia is encoe b neighboring k minutiae such that the k nearest neighbors falls in all the four quarants aroun the central minutia. The encoing proceure will be less erroneous in case of presence of spurious minutiae aroun the central minutia an also preserve more connectivit between minutiae. An eample of k-plet local structure for minutia 4

56 number 3 an the graph representation for minutiae points in fingerprint is shown in Figure.9 [3]. For the given eample k is chosen to be 4. For fingerprint matching, Chikkerur et al. [4], propose local matching base on of k-plet structure using namic programming followe b a novel Couple Breath First Search (CBFS) algorithm for global matching. In CBFS the matching between two graphs is carrie out b initializing the matching from an arbitrar pair of noe (minutia) of caniate an template fingerprints. In graph traversal all the neighboring noes that are matche locall are visite an matche in Breath First Search (BFS) manner. The matching score for that pair of noe is then obtaine b the number of noes matche at the en of BFS algorithm. As the optimal minutia pair corresponence is not known before the start of BFS algorithm, so the complete graph traversal algorithm is repeate for ever noe (minutia) pair in graphs. The noe pair that gives maimum number of matches is consiere as the best minutia pair an the transformation between caniate an template fingerprints can be foun b aligning the fingerprints base on this minutia pair. The number of matches for the corresponing noe pair is taken as the consoliate matching score for the fingerprints. The implementation of matching algorithm propose b Chikkerur et al. [4] is online available at: ( This coe is use for comparison of the propose enhancement algorithm with other enhancement techniques in Chapter 3 an Chapter 4..5 Summar This chapter presents the literature surve on fingerprint base authentication sstem. A brief surve of eisting algorithms for feature etraction an matching is presente along with emphasis on the ifferent state-of-the-art contetual filtering base enhancement techniques. These contetual filtering base enhancement techniques eist either in spatial or in frequenc omains with their pros an cons. In net chapter, a novel two-fol contetual filtering base enhancement technique is presente that process the fingerprint image both in spatial an frequenc omains to overcome the rawbacks of eisting techniques. 43

57 Chapter 3 -FOLD CONTEXTUAL FILTERING FOR ENHANCEMENT 3. Propose Enhancement Scheme As iscusse section..5, fingerprint enhancement schemes base on contetual filtering are generall carrie out either in frequenc omain or in spatial omain. However, in the propose stu a novel technique is emploe to combine the avantages of both frequenc an spatial omains for enhancing fingerprint images. As rige patterns approimatel follow a sinusoial wave pattern, therefore, the rige frequenc estimation can be hanle efficientl in frequenc omain rather than spatial omain. In the propose enhancement scheme, frequenc omain filtering caters for filtering out non-rige nois information an backgroun region from vali fingerprint information. The egraation in fingerprint image because of presence of broken riges an scars can be resolve b joining riges in the orientation of riges. Algorithms for estimation of orientation in spatial omain are usuall ver accurate, because the are base on local graients an a smoothing process takes care of noise an iscontinuities of riges. In orer to resolve the problem of broken riges an scars, filtering is carrie out in spatial omain. The propose enhancement technique is base on the following important observations note uring etensive testing of previous algorithms.. Algorithms for frequenc calculation in spatial or frequenc omain are prone to errors because of the presence of scars, broken an creases running across riges in fingerprint images. In particular, at high curvature regions an localities consisting of rige enings an rige bifurcations, the estimates are usuall not ver accurate. Therefore, image is not perfectl enhance at the location of minutiae, thus affecting the minutiae etraction an matching afterwars. 44

58 . Moreover, in previousl propose algorithms, a ifferent filter is applie at ever localit tune base on local frequenc an orientation, thus increasing the overall compleit of the algorithm. Figure 3.: Algorithm flow Diagram for the propose fingerprint enhancement technique The propose two-fol enhancement technique avois epenence on estimation of frequenc. Therefore, it is not prone to errors in the frequenc estimation process an is effective for enhancing low qualit fingerprint images [5]. It also emplos same filter for the whole image instea of esigning separate filter for each local region in an image. The avantage of this methoolog is that the computational compleit remains low, comparable to the most well-known an efficient techniques [3, 3] available in literature as iscusse in section 3.3. The overall algorithm flow of the propose two-fol enhancement technique is shown in Figure 3.. A etaile eplanation of each functional block is given in the following subsections. 45

59 3.. Frequenc Domain Filtering Figure 3., presents the main algorithmic steps of frequenc omain filtering. Figure 3.: Algorithmic steps of frequenc omain filtering Which are: Conversion of fingerprint image into frequenc omain through Fast Fourier Transform FFT. Here F represents the Fourier transform of the fingerprint image. The transforme image is then filtere with ifferent frequenc ban-pass filters an reconstructe in spatial omain using Inverse Fast Fourier Transform IFFT. A Unifie ban-pass filtere image is constructe from iniviual filtere banpass images (f, f, f n ) b selecting sub-blocks from the set of iniviual filtere images b choosing a corresponing sub-block having the maimum variance Ban-Pass Filtering In fingerprint images the riges can be moele as a combination of ifferent sinusoial wave forms having ifferent irections [30], an generate a localize spectrum in the form of a ban in frequenc omain as shown in Figure 3.3 (b). 46

60 (a) (b) Figure 3.3: Application of Fourier transform on fingerprint image, (a) A fingerprint image, (b) Fourier transform of (a) The ominant ban in frequenc omain is epenent on the inter-rige istance that varies from min to ma piels in spatial omain, where min is generall 5 an ma is 5 for 500-pi fingerprint images. In orer to pass rige patterns having inter-rige istances from min to ma piels, fingerprint image can be converte into frequenc omain, an a ban-pass filter can then be applie to filter onl the rige patterns. The ominant ban in frequenc omain will be thinner if all the riges have approimatel same inter-rige istances an ban will be wier if riges have varie inter-rige istances, which is mostl the case in fingerprint images. Therefore, a single ban-pass filter will not be beneficial because in orer to pass all vali frequencies, a wier ban-pass filter is require that will also pass frequenc coefficients outsie the ban of local riges. In the propose algorithm, a fingerprint is filtere with a number of thin ban-pass filters an response of each filter is then analze locall. The choice of the number of ban-pass filters n in the filter bank is important as it efines the frequenc resolution for filtering. For filtering in frequenc omain, Fourier Transform of the fingerprint image F(u,v) is compute an a bank of iniviual ban-pass filtere images BP_F i (u,v) are then compute b appling n ban-pass filters on F(u,v) as shown in equation (3.) an (3.). The value of n is empiricall chosen b 47

61 calculating error rates as eplaine in section 3.4 for ifferent values of n. It has been observe that the error rate generall ecreases as n increases. However, it oes not ecrease significantl as n is increase beon n = 5, while the computational compleit increases significantl. Therefore, in this stu, we have set n = 5. FFT f (, ) F( u, v), (3.) BP_ F ( u, v) H ( u, v) F( u, v), where i n, (3.) i i where H i (u,v) is Gaussian ban-pass filter an is epresse in equation (3.3). D ( u, v) D0 ( u, v) i H i ( u, v) ep, where i n, D( u, v) W (3.3) where, D(u,v) is the istance of each piel from the center of the Fourier Transform (FT) image. D 0i (u,v) efines the frequenc of i th ban. W is the with of filter an i has the range < i < n. The raial istance D 0i (u,v) of i th ban-pass filters from center of the FT image is given b equation (3.4). D D ( u, v) ( D D )/ D /( u v ) D /( u 0( i) 0u( i) 0v( i) 0u( i) 0v( i) v M / i, D0v( i) N /, 0u( i) i v u ), (3.4) (3.5) i / ( ) / n ( )( i ( n ) / ), (3.6) ma min ma min Here, M an N are the size of fingerprint image in horizontal an vertical irections respectivel; u an v are the frequenc components in horizontal an vertical irections. D 0i (u,v) will be constant for a square image, but it will be ifferent for ifferent irections in a rectangular image. W is the with of the filter; it efines the Gaussian sprea of the ban-pass filter an is epenent on number of ban-pass filters an size of fingerprint image. With W will become smaller as the number of ban-pass filters increases as shown in equation (3.7). 48

62 ( M N) W. (3.7) ( 0.3( n )) In this case, the maimum with W, is empiricall set to be (/)*(M+N)/, an the with grauall reuces as the number of bans n increases. Since the actual Gaussian sprea in equation (3.3), is proportional to D(u,v), the sprea is not constant on both sies of the ban. The filter has a much smaller sprea at the inner sie of ban an longer sprea at the outer sie of the ban. This is a skewe ban-pass filter which is suitable for retaining significant higher frequenc etails that is important for preserving accurate rige valle structure. The propose ban-pass filter as shown in Figure 3.4 (a) ehibit properties equivalent to Log-Gabor filter [3], as the DC component of this filter is zero. (a) (b) Figure 3.4 : Frequenc response of the propose Ban-pass filter, (a) Ban-pass filter, (b) -D Frequenc response of the propose Ban-pass filter 3... Construction Of Unifie Ban Pass Filtere Image The iniviual ban-pass filtere images BP_F i (u,v), after filtering in frequenc omain, are brought to spatial omain f i (,) through Inverse Fourier Transform IFFT. IFFT BPF ( u, v) f (, ), where i n, (3.8) i i where n is the number of ban-pass filters. These filtere images f i (,) are then combine to form a unifie ban-passe filtere image. Unifie ban-passe filtere 49

63 image is obtaine b selecting sub-blocks from the set of iniviual filtere images b choosing a corresponing sub-block having the maimum variance. This can be mathematicall epresse as: f (, ) f h (, ) L( s / ) L( s / ), L( t / ) L( t / ), (3.9) where s M /( L), t N /( L), (3.0) h ma (var( f (, )), (3.) arg i i,,.. n L( s) L( t) var( f (, )) ( f (, ) f (, )), (3.) i L( s) L( t) i ~ i where L L is the size of block which is being fille. The block size shoul be chosen such that it is not large enough so that the spatial frequenc within the block remains approimatel constant. Moreover, in orer to keep the computational cost low, the block size shoul not be ver small either. For a fingerprint image of resolution 500 pi, a size of 9*9 is foun to be suitable. It shoul be note that the variance of the block is calculate over the size of L L. (a) (b) Figure 3.5: Enhance fingerprint images after propose irectional smoothing metho, (a) Fingerprint image 4_7.tif, (b) Unifie ban-pass enhance image. 50

64 Figure 3.5 shows the results of the propose frequenc omain filtering approach. Figure 3.5 (a) shows the fingerprint image 4_7.tif taken from FVC 00 DB-A [53], an Figure 3.5 (b) shows the unifie ban-pass filtere image of Figure 3.5 (a). 3.. Spatial Domain Filtering Figure 3.6: Algorithmic steps of spatial omain enhancement Following the frequenc omain filtering, the problem of broken riges an scars, it can be resolve in spatial omain b smoothing riges along rige irections. Figure 3.6 shows the basic algorithmic steps of spatial omain enhancement which are as: Compute rige orientation map in spatial omain. Divie unifie ban-pass filtere image into sub-blocks an appl local irectional smoothing on each block. Combine all the irectionall enhance sub-blocks to form an enhance fingerprint image 5

65 3... Directional Smoothing Directional smoothing is performe to enhance the rige flow structure in the irection of riges. For this purpose an orientation map is compute that gives ominant orientation of each piel in the irection of riges. An orientation image O(, ) is efine as an M N image that represents the local rige orientation at piel (, ). Approaches use for estimating local rige orientations in spatial omain are base on computation of graients in the fingerprint image. In the propose stu, the algorithm presente b Ratha et al. [7] is use for estimating rige orientations. The steps involve in irectional smoothing are as follows: Etract sub-blocks of size L L from unifie ban-pass filtere image. f f s, t s, t (, ) f (, ) (, ) 0 ( s / ) L ( s / ) L,( t / ) L otherwise, ( t / ) L, (3.3) where s M /( L), t N /( L). (3.4) Rotate the sub-block b an angle φ+90, where φ is the ominant orientation of sub-block given b the value of O(,) at the center of the sub-block. After rotation the riges in the sub-block become vertical. J s, ) f (, ), (3.5), t ( s, t φ = cos (90-φ) + sin (90-φ), (3.6) φ = - sin (90-φ) + cos (90-φ). (3.7) Appl a -D vertical Gaussian smoothing filter on the sub-block. enhs, t (, ) J s, t (, )* h( ). (3.8) Where h() is the -D Gaussian smoothing filter for vertical riges as iscusse in section 3. an s, t is the center of block in horizontal an vertical irections respectivel. 5

66 Directionall smoothe sub-block is then rotate back. Mathematicall, enh (, ) enhs, (, s, t t ), (3.9) φ = cos (φ -90) + sin (φ -90), (3.0) φ = - sin (φ -90) + cos (φ -90). (3.) Repeat this process on all sub-blocks of unifie ban-pass filtere image. Finall all the processe sub-blocks are tile together to form an enhance image. f (, ) enh, (, ) ( s / ) L ( s / ) L,( t / ) L ( t / ) L. (3.) s t Figure 3.7 shows the results of propose enhancement scheme incluing ban-pass filtering an spatial filtering. Figure 3.7 (a) shows the fingerprint image 4_7.tif taken from FVC 00 DB-A [53], an Figure 3.7 (b) shows the unifie ban-pass filtere image of Figure 3.7 (a). The enhance image after appling irectional smoothing on unifie ban-pass filtere image is shown in Figure 3.7 (c). It can be observe that the propose algorithm effectivel enhances low contrast regions an also improves the rige structure b joining the broken riges. It can be seen in Figure 3.7 (c) that there are some small regions where the riges have been blurre. This is an inherent limitation of Contetual filtering base on fie winow size. This effect can be overcome b using aaptive winow size uring irectional smoothing on unifie ban-pass filtere image. For instance, in high curvature blurre regions, smaller winow size in irectional smoothing stage will give better results. Therefore, aaptive winow size base on variance of local orientations can be use to further improve the results. 53

67 (a) (b) (c) Figure 3.7: Enhance fingerprint images after propose irectional smoothing metho, (a) Fingerprint image 4_7.tif, (b) Unifie ban-pass enhance image, (c) Enhance images after irectional smoothing 3. Frequenc Response of Propose Filters The propose enhancement technique as emonstrate in sections 3.. an 3.., involves first the ban-pass filtering in frequenc omain an then irectional smoothing in spatial omain. It will be interesting to stu the combine filter response an to compare it with variants of Gabor filter. The ban-pass filter that is applie in frequenc omain is efine in equation (3.3). The escription of irectional -D Gaussian filter applie in spatial omain is as follows. A -D Gaussian filter [54], oriente at an angle θ can be epresse as equation (3.3), an is shown in Figure 3.8 (a),., ) ep h (, (3.3) θ = cos θ + sin θ, (3.4) θ = - sin θ + cos θ. (3.5) Where σ is the stanar eviation of Gaussian sprea along θ -ais an σ is the stanar eviation of Gaussian sprea along θ -ais. 54

68 A -D Gaussian filter oriente at an angle θ can be obtaine b taking σ 0, as shown in Figure 3.8 (b). This is because θ -ais epenenc becomes an impulse function for σ 0. To show the response of the filter without losing generalit, it can be assume that the rige irection of a sub-block is verticall upwars. In that case -D vertical Gaussian filter is shown in Figure 3.8 (c) an is given b equation (3.6). ( ) ep h, (for θ =0), (3.6) For θ 0, h() becomes zero. Figure 3.8: Propose Gaussian filter, (a) -D Gaussian filter oriente at angle θ, (b) -D Gaussian Filter oriente at angle θ, (c) -D Gaussian filter oriente at angle 90 0 for vertical riges In Figure 3.9, a comparison of ifferent irectional ban-pass filters for riges with verticall upwar irection is shown. A ban-pass filter with D 0 = 5 an W = 30 is shown in Figure 3.9(a). Frequenc response of the propose -D vertical Gaussian filter shown previousl in Figure 3.9(c), is shown in Figure 3.9 (b). A -D log fanfilter for verticall upwar riges is shown in Figure 3.9 (c). The propose irectional ban-pass filter obtaine after multipling frequenc response of vertical -D Gaussian filter (b) with ban-pass filter (a) is shown in Figure 3.9 (). A -D Log- Gabor filter obtaine after multipling irectional log fan-filter (c) with ban-pass filter (a) as propose b Wang et al [3] is shown in Figure 3.9 (e). A Traitional -D Gabor filter propose b Hong et al [30] for verticall upwar riges is shown in Figure 3.9 (f). 55

69 (a) (b) (c) () (e) (f) (g) (h) (i) Figure 3.9 : Frequenc response of the propose Gaussian filter an its comparison with other contetual filters, (a) Ban-pass filter, (b) Frequenc response of the propose -D Gaussian filter, (c) -D log fan-filter, () propose irectional ban-pass filter, (e) -D Log-Gabor filter, (f) Traitional -D Gabor filter, (g) -D Frequenc response of the propose irectional filter, (h) -D Frequenc response of Log-Gabor, (i) -D Frequenc response of Traitional Gabor Filter 56

70 In orer to compare the response of irectional ban-pass filters shown in Figure 3.9 (), (e) an (f) a -D vector from center of each Figure is etracte an their plots are shown in Figure 3.9 (g), (h) an (i) respectivel. From the plot of Figure 3.9 (i), it is obvious that a DC component is present in the frequenc response of Traitional Gabor filter [30] that will egrae the performance uring enhancement process. Plots in Figure 3.9 (g) an Figure 3.9 (h) shows the -D frequenc response of the propose irectional ban-pass filter an Log-Gabor irectional filter [3], respectivel. From the plots it can be conclue that both the responses are ientical in the sense that the o not have an DC component. Moreover, the propose filter has an etene tail at higher frequencies just like log-gabor filter, which ensures that suen terminations (enings) an bifurcations of the riges are preserve. 3.3 Computational cost analsis an comparison In this section, computational compleit of the propose two-fol enhancement algorithms is compare with other well-known enhancement techniques. The enhancement techniques consiere while analsis are Hong s Gabor filtering [30], Chikkerur s STFT [3] metho an Wang s Log-Gabor [3] base enhancement technique. The analsis is performe b computing the computational cost in terms of total number of multiplications (O ), aitions (O + ) an loa operations (O l ) require to enhance a fingerprint image. The analsis is performe b assuming that the size of the fingerprint image is M M, while the size of each partitione block to be enhance is m m piels for all the enhancement techniques. In this stu, the computational compleit of frequenc estimation an filtering algorithms involve in each enhancement technique is consiere while analsis. The etaile eplanation of computational compleit for each enhancement technique is given in Appeni B, while Table summarizes the computational compleit. Table states the computational compleit in terms of number of multiplications (O ), aitions (O + ) an loa operations (O l ) for ifferent values of M an m that is the size of the image an the size of the block, respectivel. For the propose two-fol enhancement, number of ban-pass filters require in frequenc omain filtering is n=5, while the length of -D Gaussian filter require for spatial omain enhancement is g=7. 57

71 Table : An analsis of computational compleit for ifferent well-known contetual filtering base enhancement techniques Multiplications: Aitions: Loa /Memor (O ) (O + ) operations: (O l ) Hong s Gabor M m + 4 M (+ M m + M (+ / m M base / m(4 m + / log m (3 m + log (m) enhancement (m) + )) +) ) Chikkerur s 4 M (8 + / log 4 M (8 + log 4 M STFT base (m ) ) + M ( + (m ) ) + 4 M log enhancement log (m )) (m ) Wang s Log- 4 M (+ / m(4 m M (3+ / m (3 m 4 M Gabor + / log (m) + + log (m) +) ) + Enhancement ))+ 4 M (+log 8 M log (m ) (m ) ) Propose (M )/ log (M ) (M ) log (M ) + n n * M Two-fol + n (M ) + n (M (M ) log (M ) + Enhancement )/ log (M ) + 4M + M /m ( g ) Technique M /m ( 4 g )+ + M /m (g + m) M /m (g + m) 58

72 Table : An analsis of computational compleit for ifferent values of image sizes (M) an block sizes (m) Hong s Chikkerur s Wang s Log- Propose Gabor base enhancement STFT base enhancement Gabor Enhancement Two-fol Enhancement Technique M=56, m=6 Multiplications Aitions Loa Operations M=5, m=6 Multiplications Aitions Loa Operations Eperimental Results In this section, a etaile performance evaluation has been carrie out b comparing major fingerprint enhancement schemes available in literature [30, 3, 3] with the propose enhancement technique b calculating equal error rates (EER) an comparing etection error traeoff (DET) graph. In fingerprint recognition sstems, the enhancement algorithms are not onl evaluate accoring to their visual perception, but more importantl, b their suitabilit for fingerprint matching. Therefore, the propose technique is teste here b evaluating both visuall an b error rates on known atabases using known matching algorithms Databases an Test Protocol In orer to carr out eperimentations publicall available atabases FVC00 DB- A, FVC00 DB-A [53] an FVC004 DB-A [55] are use. For performance 59

73 evaluation, the FVC00 [53] testing protocol has been aopte. In the above mentione FVC atabases [53], [55], the total number of subjects is hunre (00) an each subject has eight (8) fingerprints. In orer to evaluate performance of enhancement schemes, performance inicators like equal error rates (EER) an etection error traeoff (DET) graph are consiere. EER is measure in terms of False Matching Rate (FMR) an False Non-Matching Rate (FNMR). In orer to obtain genuine matching or False Matching Rate (FMR), each sample of a finger is matche against the remaining samples of that finger. Therefore, the total number of genuine matches for a atabase with hunre (00) fingers an eight (8) samples per finger can be calculate as: ((8*7) /) * 00 =,800. In orer to obtain imposter matching or False Non-Matching Rate (FNMR), the first sample of each finger is matche against the first sample of the remaining fingers in the atabase. Therefore, the total number of imposter or FNMR matches for a atabase with hunre (00) fingers can be calculate as: ((00*99) /) = 4,950. EER is the error rate given b either False Matching Rate (FMR) or False Non- Matching Rate (FNMR), at a threshol level t where both FMR an FNMR are equal as shown in Figure 3.0 [3]. EER is a single error parameter that is accepte worlwie to solel represent the sstem s performance inepenent of threshol selection. Figure 3.0: An eample of FMR(t) an FNMR(t) curves, EER is highlighte at the threshol t where the FMR(t) an FNMR(t) are equal 60

74 3.4. Matching Algorithm The matching algorithms use for performance evaluation is propose b Chikkerur et al. [4]. The source coe of this matching algorithm is available at: This technique emplos a k-plet technique to encoe each minutia base on its local neighborhoo. Matching is then performe base on a ual graph traversal (couple BFS) algorithm Performance Comparison with other enhancement techniques The propose enhancement scheme is compare with enhancement performe b Hong s Gabor filtering [30], Chikkerur s STFT metho [3] an Wang s Log-Gabor Filtering base enhancement technique [3]. The Matlab implementation of Hong s Gabor enhancement [30] is on-line available at: The Matlab implementation of Chikkerur s STFT metho [3] is on-line available at: In Figure 3. an Figure 3., a comparison of enhancement performe b our propose enhancement technique with other enhancement techniques has been presente. Figure 3. (a) is the 4_7.tif fingerprint image taken from FVC 00 DB-A [53]. Figure 3. (b) is the output of enhancement performe b our propose enhancement technique. Figure 3. (c), () an (e) are outputs of enhancement performe b Hong s Gabor [30], Chikkerur s STFT [3] an Wang s Log-Gabor [3] base enhancements respectivel. Similarl, Figure 3. shows output of ifferent enhancement techniques on fingerprint image 33_5.tif taken from FVC 00 DB-A [53]. From the results (Figure 3.0 (c), (e) an Figure 3.(c), (e)), it can be observe that Hong s Gabor [30] an Wang s Log Gabor [3] base enhancement oes not properl enhances fingerprint image in the regions of low contrast an broken riges. This is mainl because of false frequenc estimations in these regions. The enhancement base on Chikkerur s STFT [3] metho, prouces blocking effects as can be seen in Figure 3. () an Figure 3. (). From the results of Figure 3. (b) an Figure 3. (b), it is can be observe, that the propose enhancement is more effective in enhancing istorte images even with regions having low contrast an broken riges. 6

75 (a) (b) (c) () (e) Figure 3.: Eample: application of propose two-fol enhancement on fingerprint image an its comparison with other enhancement techniques, (a) Fingerprint image 4_7.tif from FVC 00 DB-A [53], (b) Enhance image after the propose enhancement technique, (c) Enhance images after appling Hong s Gabor base enhancement [30], () Enhance images after appling Chikkerur s STFT base enhancement [3], (e) Enhance images after appling Wang s Log Gabor base enhancement [3]. 6

76 (a) (b) (c) () (e) Figure 3.: Eample : application of propose two-fol enhancement on fingerprint image an its comparison with other enhancement techniques, (a) Fingerprint image 33_5.tif from FVC 00 DB-A [53], (b) Enhance image after the propose enhancement technique, (c) Enhance images after appling Hong s Gabor base enhancement [30], () Enhance images after appling Chikkerur s STFT base enhancement [3], (e) Enhance images after appling Wang s Log Gabor base enhancement [3]. 63

77 In orer to evaluate performance of enhancement schemes, performance inicators like equal error rates (EER) an etection error traeoff (DET) graph are consiere. EER is measure in terms of False Matching Rate (FMR) an False Non-Matching Rate (FNMR) as iscusse in section As eplaine above, matching algorithm propose b Chikkerur et al. [4] is use for all enhancement schemes. To etract minutiae points, the enhance images are binarize followe b thinning process an minutiae points are then etracte [39], for all enhancement techniques. Table 3, reports the error rates of all enhancement techniques for ifferent FVC atabases [53, 55]. The EER obtaine on FVC 004 DB-A [55] is much higher as compare to EER on the other two atabases consiere. This is because FVC 004 DB-A [55] atabase contains harer cases incluing istorte an partial fingerprint images. However, it is worth mentioning that the propose enhancement technique outperforms other enhancement techniques consistentl for all the three atabases. Table 3: Summar of comparative analsis of the propose two-fol enhancement with other well-known techniques base on EER EER on FVC Databases Hong s Gabor Enhancement [30] Chikkerur s STFT Enhancement [3] Wang s LogGabor Enhancement [3] Propose Enhancement FVC00- DB-A [53] FVC00- DB-A [53] FVC004- DB-A [55].3%.06% 7.09%.95%.5% 8.4%.85%.98% 5.36%.4%.63% 0.84% Another performance inicator consiere here is DET graph. DET graph is a variation of receiver operating characteristic (ROC) curve. In ROC curve, FMR is plotte against True Matching Rate (TMR) or verification rate. On the other han, in DET graph, FMR is plotte against FNMR. Therefore, both the aes of DET graph represents error rates an are generall plotte using logarithmic scales. The DET 64

78 graph for each atabase using ifferent enhancement schemes is shown separatel in Figure 3.3 ((a), (b), an (c)). From these results, it can be observe, that the propose fingerprint enhancement scheme enhances the fingerprint images reliabl an accuratel. Figure 3.3: DET graph of ifferent enhancement schemes, (a) DET graph of ifferent enhancement schemes on FVC 00 DB-A atabase Figure 3.3: (b) DET graph of ifferent enhancement schemes on FVC 00 DB-A atabase 65

79 Figure 3.3: (c) DET graph of ifferent enhancement schemes on FVC 004 DB-A atabase 3.5 Summar An effective fingerprint enhancement technique base on irectional contetual filtering has been propose. The enhancement technique is novel in the sense that it ivies the tasks of ban pass filtering an irectional filtering into frequenc an spatial omain respectivel, eploiting the strengths of both omains. Two major avantages of the propose enhancement technique are; avoiing the hazars of frequenc estimation errors an making spatial omain filtering quite simple b using a global D Gaussian smoothing filter. The enhance images, as shown in the obtaine results, are of goo qualit having smooth rige/valle structure. For performance evaluation of the propose enhancement an etensive testing has been carrie out b using three well-known enhancement approaches an three atabases. Eperimental results emonstrate that propose enhancement outperforms other wellknown enhancement techniques, i.e. Hong s Gabor filtering [30], an Chikkerur s STFT [3] an Wang s Log-Gabor filtering [3]. 66

80 Chapter 4 ENHANCEMENT AND FREQUENCY DISTORTION REMOVAL 4. Non-Uniform Inter-Rige Separation Distortion In practice, a fingerprint image suffers from several ifferent kins of egraations. These egraations inclue non-uniform illumination, scars or broken riges ue to humiit an imperfect sensors, an non-uniform rige separations which occurs ue to non-uniform contact with sensor. Although contetual filtering techniques work well to enhance the fingerprints rige structure, but these techniques o not rectif the non-uniform inter-rige separation which ma result in false fingerprint matching. These istortions result in fault feature etraction thus aversel affecting the recognition performance b complicating the feature matching process an making it time consuming an error prone. The importance of removal of non-uniformit in inter-rige separation is wiel highlighte in the earlier stuies. Most of fingerprint recognition sstems hanle this problem either at image acquisition stage b using aitional harware [56, 57] or b using complicate feature matching algorithms [, 58, 59, 60]. In [4], a canonical form of image was use for inter-rige istance normalization. However, this technique can onl be applie to high qualit thinne binar images. The istorte image is b efinition a -D namic function with varing time frequenc plot ue to non-uniform rige separations. Figure 4., shows two samples of fingerprint images of the same person, but the appear to be ifferent because of non-uniform inter-rige separation. In this stu, a novel algorithm is propose base on local aaptive irectional frequenc enhancement for noise removal an inter-rige istance normalization using local short time Fourier transform (STFT) analsis. In the propose approach, STFT analsis of fingerprint is performe b iviing image into non-overlapping blocks of size m m. Where m is set to 3. Then Fourier transform of each block is compute as was one b Chikkerur et al. [3]. The propose approach is ifferent in 67

81 the sense that the problem of non-uniform inter-rige frequenc is solve b a novel frequenc normalization an nonlinear tiling approach in spatial omain [6]. (a) (b) Figure 4.: Fingerprint images showing the problem of non-uniform inter-rige separation, (a) an (b) Two samples of fingerprint images of same person The contents of the rest of the chapter are organize as follows. Section 4. formulates our approach b first establishing fingerprint as a namic -D signal using STFT analsis. The basic filtering approach an frequenc normalization approach are then eplaine. Section 4.3 eplains propose algorithm in etail an section 4.4 presents results of the propose enhancement technique. 4. Problem Formulation 4.. Fingerprint as a non-stationar -D signal An ieal fingerprint image can be imagine as a D stationar signal compose of multiple sinusoial wave patterns of ifferent orientations with approimatel same frequenc. However, real fingerprint images ten to be non-stationar in nature 68

82 because of non-uniform inter-rige istances, this can be observe from Figure 4., which shows a fingerprint (Figure 4. (a)) with its Fourier transform (Figure 4. (b)) an spatial frequenc plot shown as gra level image (Figure 4. (c)). (a) (b) (c) Figure 4.: Fingerprint image epicting non-stationar nature, a) Fingerprint Image, b) Fourier Transform, c) Frequenc Map From the Fourier transform of Figure 4. (b) it can be seen that a ban of frequencies corresponing to rige frequencies in the fingerprint is clearl ominant an this ban shoul be preserve in an enhancement technique. Frequencies lower than this ominant ban correspon to backgroun intensities an slow intensit variations in the fingerprint, while higher frequencies correspon mostl to noise or abrupt intensit variations. However, from the Fourier transform which is a global representation, it is not clear whether the local frequencies are constant throughout the whole image or these are changing. In the frequenc omain plot shown in Figure 4. (c), the local frequenc of each piel in fingerprint image is calculate along a line orthogonal to rige irection an shown against spatial coorinates. It ma be mentione that time-frequenc plot of a -D signal is a 4-D representation involving frequenc components for each spatial location (,). However, ue to the nature of a fingerprint image that comprises rige patterns, it is logical to show onl the ominant frequenc that is calculate in the irection normal to local rige orientation. Therefore, this 3-D Spatial-frequenc representation of a -D image is analogous to -D time-frequenc representation of a 69

83 -D signal. It is clear from frequenc map of the fingerprint that the local frequencies shown b ifferent gra level in Figure 4. (c) are not constant throughout the fingerprint region. Hence, fingerprint image can be consiere as an orientate D namic or non-stationar signal. Moreover, since the ominant frequencies are not constant throughout, an enhancement approach assuming constant frequenc will not be beneficial. 4.. Filtering In STFT For non-stationar signal analsis, Short time Fourier transforms (STFT) has been use as an effective analsis tool. An eample of filtering using STFT is shown in Figure 4.3. Figure 4.3 (a) shows a scale sub-image of size piels taken from the fingerprint shown in Figure 4. (a), an Figure 4.3 (b) shows its Fourier transform. (a) (b) (c) Figure 4.3: Enhancing a sub-image of fingerprint in frequenc omain (a) A scale block of fingerprint, (b) Fourier Transform of sub-image, (c) Dominant Frequenc Ban, () Enhance sub-image () 70

84 It can be seen in the transform that there is a ominant sinusoial component given b two smmetric peaks on either sie of the center in a region orthogonal to the ominant rige orientation of the block. This ominant frequenc region correspons to the esire rige pattern an the remaining frequencies are unwante frequencies corresponing to noise an average illumination. In orer to enhance the sub-image, this ominant frequenc region shoul be booste compare to the other frequencies. This can be one b filtering out the unesire frequencies using notch-pass filters. A emonstration of such notch-pass filtering is shown in Figure 4.3 (c) where the filtere Fourier transform image is shown b using approimate notches. Figure 4.3 () shows the inverse transforme image in spatial omain which has been enhance as esire Frequenc Normalization For non-stationar signal, inter-rige frequenc varies throughout the signal. In fingerprint image this variation is graual an assumption of slowl varing frequenc is logical. To normalize the frequenc istribution in fingerprint, STFT becomes ver useful as it ivies image into small overlapping blocks an each block is enhance iniviuall assuming that the frequenc within a block is approimatel constant. However, frequenc variation is quite significant throughout the fingerprint an it is esirable to bring the fingerprint to a nearl constant frequenc for favorable fingerprint matching. In orer to clarif the proceure of frequenc normalization, consier two ifferent blocks of fingerprint image having significantl ifferent ominant frequencies. Both blocks are first enhance using the filters corresponing to their iniviual ominant bans an orientations. In orer to bring two blocks on the same frequenc, the frequenc omain image can be scale aroun the center to align the peaks in Fourier transforms of two sub-images. Using the scaling propert of Fourier transform, an alternative simpler proceure for such frequenc normalization can be inverse scaling in spatial omain. For eample, if frequenc of the first block is higher than the other, it means that the inter-rige separation of first block is smaller than the other. In this case normalization can be achieve b either compressing the first block with respect 7

85 to the secon in frequenc omain, or b stretching the first block with respect to secon in spatial omain. 4.3 Propose Algorithm Figure 4.4 Block Diagram of propose enhancement technique The overall algorithm flow for the propose STFT base fingerprint enhancement is shown in Figure 4.4. As shown in the figure, the main components of the propose algorithm inclue firstl the ivision of image into N sub-images. Each sub-image is 7

86 then converte into frequenc omain through FFT an ominant ban an orientation is estimate. Each sub image is enhance b using irectional ban-pass filtering. Each sub-image is then converte back to spatial omain through IFFT an scaling is performe in spatial omain. Finall, sub-images are recombine to obtain full enhance fingerprint image. A etaile eplanation of each component is given in the following subsections STFT Computation Image is first ivie into N overlapping blocks I a,b (,) of m m size, where a an b are the block inices in an irections respectivel. These blocks are larger than the actual blocks, which are to be tile together later for recombination which are of size m m with the same centers an non-overlapping. The larger non-overlapping blocks are more suitable for ominant ban estimation an also for non-linear tiling an interpolation in the later stage of recombination. Each block is then converte to its frequenc omain representation F a,b (u,v) using FFT Frequenc an Orientation Map Construction Normalize frequenc an orientation maps for each block F a,b (u,v) are constructe b using ominant ban estimation technique escribe as follows:. For each block a an b, a. Firstl, i j Gaussian winow functions are compute for i ifferent frequencies an j orientations. Where i ranges from D 0min <i< D 0ma an j ranges from 0<j<π. The Gaussian winow function in frequenc omain resembles a irectional ban-pass filter an is efine b multiplication of two filters. H ( u, v) H ( u, v) H ( u, v), (4.) ij b i H b i j where ( u, v), is the Gaussian ban selection function superimpose b a irectional Gaussian function ( u, v) H j 73

87 The Gaussian ban selection function is efine as, D ( u, v) D 0 i H b ( u, v) ep, (4.) i D( u, v) W where, D(u,v) is the istance of each piel from the center. D 0 is the raial i istance of i th ban center from the center of the FT image W is the with of the function, it efines the Gaussian sprea in both irections. Figure 4.5 (a) shows the -D plot of the ban selection function. H j The irectional winow function is efine b the following equation: ( u, v) ( v D( u, v)cos j ) ( u D( u, v)sin j ) ep D( u, v) W, (4.3) where, W is the total with of function in egrees aroun the irection of the function. θ j is orientation of j th irectional function. Figure 4.5(b) shows the D plot of a irectional function an Figure 4.5 (c) shows the final winow function. b. For a given block F a,b (u,v), all irectional winow functions are multiplie with F a,b (u,v) an absolute energ is compute against each function. E ( i, j) H ij ( u, v) Fa, ( u, a, b b v ). (4.4) c. The ominant frequenc i an orientation j of the block is chosen to be the one for which E a,b (i,j) gives the maimum value, i.e. Dominant i, j a, b arg ma i,j Ea, ( i, j). b (4.5). This process is repeate for ever block an a frequenc map f an orientation map O is constructe b storing ominant frequenc an irection for ever block. 3. Frequenc an irection maps are then smoothe using Gaussian smoothing kernel to obtain the final smoothe frequenc an orientation maps f s an O s. 74

88 Figure 4.5 Gaussian winow function (a) ban selection function, (b) irectional function, (c) final winow function Block-wise Filtering in Frequenc Domain As iscusse in the previous section, Fourier transform F ab (u,v) of each block is enhance b a filter that suppresses all the frequencies other than those in the ominant ban an irections. For this purpose, the notch pass filter efine b the winow function in Figure 4.5 (c) is use. H ij (u,v) will act as the Gaussian irectional ban-pass filter when the i an j are set to values given b the ominant frequenc an orientation in the smoothe maps estimate earlier, i.e. i = f s (a,b), an j = O s (a, b). (4.6) Filtering is performe for each block a, b as follows: F ' a, b ( u, v) Hij ( u, v) Fa, b( u, v ), (4.7) where ' Fa,b is the enhance filtere image in Frequenc omain. Each block is then converte back to spatial omain b computing the Inverse Fourier transform Frequenc normalization The purpose of frequenc normalization is to bring all local frequencies as near as possible to the mean frequenc f m of the fingerprint. To perform the normalization, spatial omain scaling factors S a,b an S a,b for each block a, b are first calculate, using the following equations: 75

89 S a, b f s f m ab, (4.8a) S S S *sin O a, b S *cos O a, b *sin O a, b, a, b a, b s a, b s s S *cos O a, b S *cos O a, b (4.8b) *sin O a, b. a, b a, b s a, b s s (4.8c) Each block is then locall normalize b either stretching or contracting it, base on its scaling factor. A block with frequenc equal to f m will remain as it is, whereas block with frequenc less than f m will be contracte b S a,b an vice versa. In orer to resize a block, D geometric scale transformation is performe base on bi-cubic interpolation. Notice that for the propose approach, blocks are square, however scaling factors are ifferent for both an irections. The block size t ab an t ab for each block is, therefore given as: t m a, b * S a, b t m a, b * S a, b,. (4.9a) (4.9b) After normalizing each block, a globall normalize image is create with uniform inter-rige istance. This image can be constructe b intelligentl blening all normalize an enhance blocks. The problem in constructing normalize image can be better eplaine b the eample shown in Figure 4.6. Consier a small portion of image, ivie into blocks of size m m with block centers A(, ), B(, ), C( 3, 3 ), D( 4, 4 ) etc., as shown in Figure 4.6 (a). Figure 4.6 (b) shows the eample when all these blocks are scale b keeping their centers fie. As a result of block scaling, few blocks ma overlap with each other or some gaps ma be left between blocks as shown in Figure 4.6 (b). Both these phenomenon's are not esirable. 76

90 Figure 4.6 Problem in normalize image construction, (a) Image portion with block size m m, (b) Image portion after block scaling In orer to avoi this situation, the centers of blocks nee to be re-ajuste such that the overlap or gaps between the blocks can be minimize. Figure 4.7 Normalize gri construction map 77

91 In the propose approach, the center of the image is taken as reference an the image is ivie into four quarants, i.e. upper left, upper right, lower left an lower right quarant as shown in Figure 4.7. The proceure for calculating new block centers for normalize gri of lower right quarant of an image is as follows: a. The normalize central block is place in the output image at its original position, i.e. center of the whole image. b. For blocks etening horizontall outwars from center of the image new normalize center location is efine as: C t t a, b a, b a, b a, b C, (4.0a) C C. (4.0b) a, b a, b c. For blocks etening verticall ownwars from center of the image new centers of each block is efine as: C C, (4.a) a, b a, b t a, b t a, b C a, b Ca, b. (4.b). For all the remaining blocks the new central location of each block is efine as: t a, b t a, b C,,,, a b C a b C a b (4.a) t a, b t a, b C,,,. a b C a b C a b (4.b) All the shifte centers for the remaining quarants can be compute in similar wa b just consiering the geometrical structure Enhance Image Construction After computing the normalize gri of block centers, the whole normalize image is constructe. For this purpose, simple tiling approach cannot be use as this approach 78

92 will introuce blocking effect in the image. In orer to avoi blocking effect, a moifie version of inter-block bilinear interpolation is use. Figure 4.8 Interpolation on non-rigi gri For each piel of normalize enhance image, four nearest centers an the normalize blocks associate with them are selecte. Notice that the gri with reefine centers is not regular. Separation between ajacent centers for normalize enhance image is not constant. The value of each piel is calculate b bilinear interpolation of the values at that piel in the four selecte normalize blocks. Consier the case of calculating value of piel X in Figure 4.8. The piel value is to be calculate using four neighboring enhance blocks A, B, C, D with centers (, ), (, ), ( 3, 3 ) an ( 4, 4 ), respectivel. Value of X(, ) is given as: X ( a w w b w c w3 w4 ), (4.3) where a,b,c, are the piel intensit values in four selecte blocks given b a = A{, }, b = B{, }, (4.4a) (4.4b) 79

93 80 c = C{ 4, 4 }, (4.4c) = D{, }, (4.4) Here the istances,, an are given as: =, (4.5a) =, (4.5b) =, (4.5c) =, (4.5) The interpolation weights are given as: w 4 4, (4.6a) w 4 4 ) (, (4.6b) w 4 4 ) (, (4.6c) w 4 3, (4.6) w 4 4 4, (4.6e) where,,, 4, an 4 are istances between the corner points of the block to be rectifie as shown in Figure 4.8. In the above equations, it is assume that enhance blocks are centere at coorinates (0,0). The complete erivation of bilinear interpolation proceure involve in the propose scheme is presente in Appeni A.

94 4.4 Results In orer to show the frequenc normalization effect, a comparison of the propose enhancement technique with Gabor base enhancement [30] is presente in Figure 4.9 an Figure 4.0. Figure 4.9 (a) an 4.0 (a), shows two ifferent fingerprint images of the same person from FVC 004 DB-A [55], but the appear to be ifferent because of non-uniform inter-rige separation. Figure 4.9 (b), 4.0 (b) an 4.9 (c), 4.0 (c) shows results of Gabor base enhancement [30] an the propose enhancement, respectivel. For clarit of the frequenc normalization effect, enhancement results are shown b using thinne images [3]. The enhance images have zero mean because the DC component is suppresse in frequenc omain filtering. Therefore, the DC value i.e. zero can be use as a global threshol for binarizing enhance image. After binarization, thinning is performe using morphological operations [3]. Figure 4.9: Eample : application of the propose frequenc istortion removal enhancement technique on fingerprint image an its comparison with Gabor Enhancement, (a) Fingerprint, (b) Thinne Image base on Gabor Enhancement [30], (c) Thinne Image base on our propose Enhancement technique Figure 4.9 (b) an 4.0 (b) shows thinne images after enhancing fingerprint image using Gabor base enhancement [30]. It can be observe that using Gabor base enhancement [30] onl the rige structure is enhance. However, the inter-rige 8

95 separation is still non-uniform in the output images. The enhancement results (thinne images) of the propose enhancement technique are shown in Figures 4.9 (c) an 4.0 (c). It can be observe that the propose technique not onl improves the rige structure of fingerprint image but it also effectivel reuces non-uniform inter-rige separation. Figure 4.0: Eample : application of the propose frequenc istortion removal enhancement technique on fingerprint image an its comparison with Gabor Enhancement, (a) Fingerprint, (b) Thinne Image base on Gabor Enhancement [30], (c) Thinne Image base on our propose Enhancement technique In orer to evaluate performance of the propose frequenc istortion removal enhancement technique, similar methoolog to the one aopte in section 3.4 is use. Table 4, report the error rates of the propose enhancement technique, in comparison to other state-of-the-art enhancement techniques for FVC 00 DB-A atabase [53]. The results show that the propose technique performs better as compare to other enhancement techniques. 8

96 Table 4: Comparative analsis of the propose frequenc istortion removal Algorithm/metho enhancement technique base on EER EER on FVC00 DB-A Databases Hong s Gabor Enhancement [30].3% Chikkerur s STFT Enhancement [3].95% Wang s LogGabor Enhancement [3].85% Propose Enhancement.64% 4.5 Summar This chapter iscusses a novel contetual filtering base fingerprint enhancement technique to simultaneousl overcome the effects of non-uniform inter-rige frequencies along with broken riges an scars. The propose enhancement technique involves processing both in frequenc an spatial omains. The problem of broken riges an scars is resolve in frequenc omain, where irectional filtering is applie on overlapping sub-blocks of fingerprint image base on the ominant frequenc an orientation of each block. From the ominant frequencies of each block a scaling image is compute. In spatial omain, all the overlapping blocks are scale base on the scaling image such that the inter-rige frequenc of all the blocks become normalize. The scale sub-blocks are then stitche together b using a moifie version of bi-linear interpolation metho to obtain the normalize enhance image. The results shows that the propose enhancement technique not onl improves the problem of broken riges an scars but also reuces the effect of non-uniform interrige frequenc. 83

97 Chapter 5 CONCLUSION AND FUTURE WORK 5. Conclusion In this research two fingerprint enhancement approaches are propose to overcome ifferent istortions incluing non-uniform illumination, scars an broken rige an non-uniform inter-rige frequenc. The first approach iscusse in chapter 3, proposes an effective fingerprint enhancement technique base on irectional contetual filtering. The enhancement technique is novel in the sense that it ivies the tasks of ban-pass filtering an irectional filtering into frequenc an spatial omain respectivel, eploiting the strengths of both these omains. The main avantage of the propose technique is that it avois calculation of frequenc maps before appling contetual filtering thus reucing the chances of errors uring frequenc estimates. The enhance images, as shown in the obtaine results, are of goo qualit having smooth rige/valle structure. In orer to etermine the performance of the propose enhancement technique, a comparative analsis of error rates on stanar fingerprint atabases has also been presente with major contetual filtering base enhancement schemes. Eperimental results emonstrate that propose enhancement outperforms other wellknown enhancement techniques, i.e. Hong s Gabor filtering [30], Chikkerur s STFT [3] an Wang s Log-Gabor filtering [3]. A etaile computational compleit analsis of the propose two-fol enhancement technique is also carrie out in this stu in section 3.3. The analsis shows that the compleit of the propose enhancement is comparable to other well-known enhancement techniques like Hong s Gabor filtering [30], Chikkerur s STFT [3] an Wang s Log-Gabor base enhancement [3]. The secon approach eplaine in chapter 4, focuses on overcoming averse effects of non-linear scaling along with broken riges an scars b normalizing the interrige spacing of fingerprint images. In the propose approach, Short time Fourier 84

98 transform analsis of fingerprint is performe b iviing image into overlapping blocks an then computing Fourier transform of each block as was one b Chikkerur et al. [3]. However, a ifferent an novel approach is propose for the frequenc omain filter esign an block tiling approach in spatial omain. Moreover in the same step, non-uniform rige frequenc problem is also solve b a novel frequenc normalization an nonlinear tiling approach. Earlier frequenc normalization preprocessing technique presente in [4] coul onl work on thinne images an woul fail on severel egrae images. On the other han, contetual filtering base enhancement techniques presente in [30, 3] coul onl be use for rige enhancement an woul not cater for frequenc normalization. The enhance image as shown in the obtaine results overcomes both kin of istortions an enhance images are of goo qualit having uniform frequenc an smooth rige/valle structure. 5. Future Work ) In the propose two-fol contetual enhancement, fie winow size is use uring irectional smoothing on unifie ban-pass filtere image. It has been note in section 3. that ue to this fie winow size some blurring along riges ma appears speciall in regions with high curvature. The propose two-fol contetual enhancement ma be further improve b using aaptive winow sizes uring irectional smoothing. Therefore, in orer to reuce the blurriness an aaptive metho can be evise that use larger winow for regions with low curvature an smaller winow for regions with high curvature. ) Rigorous Evaluation of the Frequenc Normalization Algorithm a) It has been note that the results of severel istorte images improve a lot when frequenc normalization is use. However, for images with nearl same frequenc this metho ma eteriorate the matching results, because of using interpolation. Therefore, a metho for selecte use of frequenc normalization has to be evise that ma check the frequenc variance against an threshol before application of frequenc normalization. 85

99 b) The frequenc normalization proceure ma be further improve b epectation maimization using some iterative proceure that inclues two iterative steps, i.e. locating centres an scaling parameters of blocks accoringl. c) The propose enhancement technique reuces the nonlinearit among riges b making the inter-rige istance nearl equal. This will enable the matcher process to have more strict threshols while matching the features. In this scenario, a simple rigi matcher can be evise for feature matching. 3) Enhancement of Palmprint images High-resolution palmprint images share common features with fingerprint images, like riges structure an minutiae points [6]. Contetual filtering base enhancement techniques can also be use for palmprint. Palmprint enhancement is more challenging problem as compare to fingerprint because of the large size of palmprint image as compare to fingerprint. For palmprint enhancement, Cappelli et al. [9] use traitional Gabor filtering [30] base enhancement that is propose for fingerprint enhancement. The effectiveness of the propose two-fol contetual filtering base enhancement techniques can be further evaluate for enhancement of high-resolution palmprint images. 86

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106 APPENDICES Appeni A Bi-linear interpolation for non-regular D gri Bilinear interpolation is performe to fin the values at ranom positions on a regular -D gri. Bilinear interpolation etens from linear interpolation b first performing linear interpolation in one irection followe b interpolation in the other irection. An eample of fining intensit value at position X(, ) using bilinear interpolation on a regular -D gri is shown in Figure A.. Figure A.: Bilinear Interpolation on -D regular gri The intensit value at position P (p, p ) can be estimate b linearl interpolating between piel vales at A(, ) an C( 3, 3 ) as given in following equation: P p, p ( C( 3, 3)* 4 ) ( A(, )* 4 4 ), (A.) 93

107 Here the istances an 4 are given as: =, (A.) 4 = 3, (A.3) Similarl, the intensit value at P (p, p ) is estimate b linearl interpolating between piel vales B(, ) an D( 4, 4 ) as given in following equation: P p, p ( D( 4, 4 )* ) ( B(, )* ), (A.4) =, = 4, (A.5) (A.6) The intensit value at position X(, ) is finall estimate b linearl interpolating between intensit vales at P (p, p ) an P (p, p ) as given in following equation: X, P ( p, p ( p) ) * P ( p ( p p ), p ( p ) ) * ( p p ) (A.7) In case of regular gri shown in Figure A., the value of p is equal to an 3, similarl p is equal to an 4, so we can write the above equation as: X, X, P ( p, p ( P ( p, p ) * ( ) ) * P ( p ( ), p ( ) * ( ) ( P ( p, p ) * ) ) ) (A.8) (A.9) where the istances an are given as: =, =, (A.0) (A.) Coming back to the propose problem of bilinear interpolation as iscusse in section 4.3.4, it can be note that the gri use for interpolation is non-regular that makes the 94

108 interpolation problem more complicate. A closer look of nor-regular gri that can eist in the propose algorithm can take the form as shown in Figure A.. Figure A.: Bilinear Interpolation on non-regular -D gri In orer to fin bilinear interpolation of intensit value at position X(,) for nonregular -D gri in Figure, equation (7) can use. However, in case of non-regular gri the components of P an P can be misaligne. So it is neee to calculate -components (p, p ) of points P an P before using stanar bilinear interpolation proceure as eplaine above. In orer to estimate the -components of P an P, equation of straight line for two points is use as given in following equations: p ( ) 4 4 (A.) p ( ) (A.3) where istances, an 4 are given in equation (), equation (5) an equation (3) respectivel. Distances 4,, an are given as: 95