Curvature-Based Matching Approach for Automatic Fingerprint Identification

7/28/2019 Curvature-Based Matching Approach for Automatic Fingerprint Identification

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Curvature-Based M atching Approach for Automatic Fingerprint Identification

Abdelmonem A. SalehPh.D. CandidateDepartment of Electrical EngineeringUniversity of Alabama in HuntsvilleHuntsville,AL 35899USAAbstract - The performance of the automatic fingerpr inti denti fi csti on/veri fi i cati on systems depends on three factors. 1-the enhancement process efficiency, especially for low qualityfingerprint images, 2- how the maximum available features inthe fingerprint can be encoded, and 3- how much precisely thereference frame, to which the features are allgned, can bedetermined. We present a new approach to encode (transform)and match the fingerprint image. I n this approach, thefingerprint image will betransformed into a sequence of pointsin tbe Anglecurvatur e domain. The transformation takes intoconsideration the entir e fingerprint features such as fingerprintpattem, ridge curvature, relative location, numbers, andcharacteristics (r idge ending, bifurcation, island, spur, etc.) Thematching between a current template file and the database fileswill bebased on the least square error of the Euclidean distancebetween corresponding points in the Angle-Curvature domain.The number of points, that a given fingerpr int image will betransformed into, is a trade off between the required accuracyon one hand and the program running tim, and the storagespace required for each template file on the other hand.

I. INTRODUCTIONRecently, the need for automatic person identification isincreased more and more in our daily activities, in general,and in the world of business and industry, in particular.

Automatic person identification systems based on biometricsoffer more reliable means of identification than the classicalknowledge-based such as password, personal identificationnumber, magnetic card, and driving license. Biometricsidentification systems take advantage of an individual'sunique physical characteristics in order to authenticate thatperson's identity. Various forms of biometrics are face, handand finger geometry, eye, voice and speech, Signature, andfingerprint. Clearly, such physical characteristics can not beforgotten, lost, guessed, or stolen. Moreover, they providerich discrimnatory information for identification purpose.Among all the various forms of biometrics, the fingerprint-based identification is the most reliable and popular personalidentification methods.The inside surfaces of the fingers contain mnute ridgesof skin with furrows between each ridge. The ridges andfurrows run in parallel lines and curves to each other formngcomplicated pattems. The basic fingerprint (FP) patterns arewhorl, loop, and arch. These patterns can be intemallydivided into plain whorl, central pocket loop, double loop,and accidental are under. the whorl pattem. Radial left andright loop are under the loop pattern. Plain arch and tented0-7803666I-1/01/$10.002001 IEEE

RezaR. AdhamiProfessor and ChairDepartment of Electrical EngineeringUniversity of Alabama in HuntsvilleHuntsville,AL 35899USA

tented arch are under the arch pattem [I]. Fig. 1shows thedifferent FP patterns. Also, the ridges have many localcharacteristics called mnutiae. The basic mnutiae are ridgeending, bihcation, lake, independent ridge, island, spur, andcrossover [2]. Fig. 2shows the FP mnutiae. The most twopromnent are ridge ending and ridge bihrcation since anyother mnutia can be expressed as a combination of ridgeending and\or ridge bifurcation. The different patterns of thefingerprints and the mnutiae location represent a richdiscrimnatory information making the FP one of the mostreliable means of personal identification. Practically, no onehas ever been found to have a sequence of identical ridgedetail to any other person. Another reason for the FPidentification powerfulness is its immutability, since the FPfeatures are permanent and unchanging.

11. AUTOMATIC FP IDENTIFICATIONAutomatic personal identification systems, based onfingerprint, havetwo levels of identification, coarse and fineidentification levels. At the coarse level of identification, anygiven impression is addressed into one of the FP pattems. Atthe fine level of identification, FP features are extracted andaligned to construct a template file (TF). Fig. 3shows the

basic steps of automatic FP identification system(AFIS).Thefirst step isthe FP image enhancement, which is necessary topurify and detect the ridges and consequently facilitate thefeature extraction process. The second step is the FP imageclassification, which basically speed up the matching process.Next step is determning of reference frame. This frame ofreference is essential to describe the location and orientationof the extracted features. The final step is the FP imageencoding. FP image encoding compresses the FP image whilesaving all the discrimnatory information of the FP.A precise frame of reference is essential for accuraterepresentation of the extracted features and hence, the entiresystem accuracy. In other words, the system should be able todetermne the exact same reference frame whenever anyimage of the same finger is processed. Also, the referencefiame compensates the position dependence property of theFP image. Position dependence refers to captured FP imagevariation from one scan to another. This variation is possiblyduo to skin moisture, oiliness, dryness, dirt, skin cuts, fingerrotation, and the amount of applied pressure to the fingerreader. Determning such precise reference frame is heavilydependent on the quality of the FP image.

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Curvature-Based Matching A pproach forAutomatic Fingerprint IdentificationThe FP encoding is the heart of the identificationalgorithm. Specifically, an encoding algorithm shouldperform the following sequence of actions. It extracts the FPfeatures, it recognizes the feature type, it specifies theirlocations relative to a given reference frame, it aligns thefeatures in a specific format to make the TF, and finally, itmatcheshtores this file to one classes of the FP patterns. FPfeatures include ridge number, ridge cumahre, and ridgelocal characteristics (mnutiae). Currently, the most prevalentmodel for FP encoding and match is based on mnutiae and iscalled mnutiae point match133- 151.

111. MINUTIAE POINT MATCHBased on mnutiae point match, the encoding algorithm

determnes the geomemc and topological informationof theFP mnutiae. This information is mainly the mnutiae type,location, and orientation. Then, based on this information, theencoding algorithm generates the TF that represents the givenFP image. TheTFhas, or is simlar to, the following format:

Where each column in (1 ) represents a certain mnutiaet ype, i.e. the first column represents, the mnutia of ridgeending, the second column represents bifircation . etc. Eachcolumn entry represents one location( )and orientation( 0of an extracted mnutiae type represented by that column.So,the entry (I , o ) ~epresents location( ) and orientation(0 )of the it hextracted mnutia of typej . Fig. 4 shows anexample of measuring the location and orientationof ridgeending and ridge bifurcation. The mnutiae locations ( I )may be measured either relative to a fixed coordinatesystemor relative to each other. The mnutiae orientation( 0is measured relative to a reference direction. Once again, thistemplate file is stored under / or matched to the part of thedatabase that has the same class. In the matching phase, thecorresponding entries of the currentTFand the database fiIesof simlar class are matched.The above approach (mnutiae point match) of FP imageencoding has the following disadvantages:The FP mnutiae represent a small subset of the FPfeatures.0 Feature extraction is not an easy task at mnutiae location.A well known example is ridge bifurcation since the ridgebecomes very thin in width andas a result, many ridge

I (a) Plainwhorl (b)Central pocket (c) DoubleImp (d)Accidental

I (e) Left loop (f )Right loop (g) Plain arch @) Tentedarch~

Fig. 1 Fingerprintpatterns

IndependentRidge,

Fig.2Fingerprint minutiaeBihrcation is detected as ridge ending causing doubleerrors. The first error is a mssed bifurcation and thesecond oneisa false ridge ending.Minutiae relative distances are subject to vary due to skinelasticity under different amounts of pressure applied to thefinger reader.Corrupted regions in the FP image will create many falsemnutiae.Variable sizedTF representation. Although matching overonly 12-20mnutiae is sufficientto identify theFP imag,e,every extracted mnutia must be considered in thecorrespondingTF.Thi s makes theTF variableinsize.I n the next section, a new approachtoencodeandmatchthe FP image is introduced. The approach isbased on ridgecurvature and lets almost all the FP features contributing tomake up theTF.Moreover, it producesaTF of fixed storagesize.

IV. CURVATURE-BASED MATCHBased on ridge topology, the FP image is transformedinto a sequenceof points in the angle-curvature domain. Eachpoint represents an accumulated amount of ridge curvatlireand termnates at an angle derived by the ridge relativelocation of some areaof the FP image. This setof the points

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Curvature-Based Matching Approach forAutomatic Fingerprint Identification . . I Grayscale~ , 4F P i m a g e

is the TF corresponding to the given FP image. The numberof points, that the FP image will be transformed into, isbasically chosen according to the desired applicationaccuracy and the required storage for each TF.Fig. 5 (a) shows two ridges each possessing relativelocation with respect to the other. InFig. 5(b), the outer ridgehas been rotated to have a different relative location withrespect to the inner ridge. To encode these two different ridgepattems, three points (a, b, and c) are choosing andspecifically distributing on the inner ridge of each pattem.Moving from each point in a direction normal to the ridge atthat point until intersecting the other ridge at some point.Suppose that these intersecting points are i T ,b,and i: for thepattem shown in Fig. 5(a), and are g , b, and for thepattem shown in Fig. 5 (b). Now, the set of point pair ((a,a), (b ,b), c , F) ] encodes the ridge relative location inpart (a) and the setof point pair {(a, ), (b,b), (c, C )}encodes the ridge relative location in part (b)ofFig. 5. To include more FP features to the above encodingtechnique, the ridge curvature at each intersecting point isadded to the above set of points. The new sets of point pairare now encoding the pattems of Fig. 5 (a) and (b),respectively. Where c x is the ridge curvature measured atpoint x. The locationof the intersecting point will be replacedby the ridge normal direction at that point, For simplematching between corresponding points of different set. Sincethe FP image is a two-dimensional signal and any pointlocation is specified using two coordinates while the ridgenormal direction at this location is specified using only onecoordinate. Therefore, the pattems shown inFig. 5 (a) and (b)are encoding by the following sets of point pair ( (e ; , c;;,(eh,cg), e,-,c,-)and ( ( e ; , c z ) , (ee,c;), ( e : , c z ) 1,respectively. Where o x is the normal direction to the ridge atthe point x. Encoding somearea of the given pattem into apoint pair ( e x ,c,) s called specifier. Since it specifies thecurvature and the normal direction of each encountered ridgewithin that area of the FP image starting from some initialpoint until ending at the final pointx .In simlar way, this encoding technique transforms anyFP image into a sequence of points (template file) in theangle-curvature. Fig. 6demonstrates the idea of the FP imageencoding based on ridge curvature. Two simple hypotheticalpattems are encoded into eight specifiers. The specifiers'initial locations are the origin. The specifiers' initialdirections are0,d4 , 1[/2,3n/4, . 7d4. Each specifierstartsmoving from the origin in its initial direction untilintersecting the first ridge at some point. The ridge curvatureand the outward normal direction to the ridge at that point are

-

- -

-I (Z. c;;,(b,cb) , ( 2 : , cF)bnd { ( z ,G),(b,ce),(i,G11

FP image

Referencedetermination

and matchingIdentificationresult

Fig.3 Major steps ofautomatic fingerprint dentification

Fig.4 Location and orientation measurementsfor ridge ending and ridge bifurcationcalculated. This normal direction determnes the nextintersection point on the next encountered ridge.Thisprocessis repeated all while accumulating the ridge curvature untilwe reach the outer ridge. Each specifier ends with anaccumulated amount of ridge curvature and pointing in adirection derived by the encountered ridges. The specifiers'final values are expressed in the angle-curvature plan.Two template files match if the root square error of theEuclidean distance between their corresponding points is lessthan certain threshold value. This can be mathematicallyexpressedas:

Where:&E,+, . mnimum accepted error in Euclidean distanceci curvature componentof current specifier, s i

. Square error of Euclidean distance.

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Curvature-Based Matching Approach forAutomatic Fingerprint Identificationcdi curvature component of database specifier, s iei . angle component of current specifier, siedi . angle component of database specifier, s ikc . curvature coefficient for system coordinate stabilityke . angle coefficient for system coordinate stability.Practically, the identification algorithm may not be ableto exactly determne the same reference frame for the sameFP image. In this case, the generated template filewill have,in general, a significant difference that may lead to amsmatch. To account for this, the factorskc, and ke will be

designed to compensateforthe reference frame instability.It is obvious that the above approach of FP imageencoding takes into account the ridge number, ridgecurvature, and the ridge relative location.Fig. 7demonstratesthat the encoding approach also takes into consideration theridge local characteristics. It shows the tracks of twoadjacent specifiers SI and s2. The specifier s2 reflects thepresence of the ridge mnutiae of ridge ending, and ridgebifurcation.V. FP IDENTIFICATION ALGORITHM

In this Section a complete algorithm forFP identificationis introduced. The consists of four basic codes. These codesare FP image enhancement, FP pattern classification,reference frame determnation, andFP image encoding andmatching.The enhancement code is based on the multiscale ridgefilter [6].The ridge filter at three different scales is used topurify and detect the ridges. The classification code classifiesaFP image into oneof the following FP patterns; whorl, plainarch, tented arch, left loop, and right loop. The referenceframe is determned at the mnimum curvature point withincertain area called the pattern area. This point and theoutward normal direction, of the ridge at this point, constitutethe reference frame of the given FP image.The FP image encoding and matching code is based onridge curvature technique and the root square error of theEuclidean distance that was explainedin Section IV . Theridge curvature is measured using curve fitting method to theclosest ellipse [7]. The algorithm encodesa FP image using18 specifiers, while in the matching phaseonl y 16specifiersare matched. Two specifier values of maximum error arediscarded. The reason for this is that theFP image could haveup to 2 delta shapes. Any fluctuation in the initial startingpoint o f the specifier that passed through the delta shape willlead to a large error in thespecifier value.The required storage space for the generated templatefile is 144 bytes, since we have 18 specifier values each valuerequires4 bytesforcurvature and another4 bytes for angle.

Fig.5 Ridgesat different relative position

r s3

SI

s6 s7

.X-AXir

C 1t

Fig. 6 TwohypotheticalFP patternsin (a) and (c ), andtheirtransformation nto the anglecurvaturedomain in (b) and (d).r i

I@)

Fig.7Two ridgelocal characteristicsencoded by the specifier s2a) r idge ending, and b) r idgebihrcation.VI. RESULTS

The algorithm is tested on the NIST-4 special database[8]. The size of one FP image is 512x512 pixels with 256grayscale levels and resolution of 500 pixel per inch (ppi).The results of over 100 samples are summarized in Table 1.Also, an example showing the specifiers track are shown inFig.8.

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Curvature-Based Matching Approach forAutomatic Fingerprint Identification i I

Tentedarch8187/8

TABLE ISUMMARY OF T HE IDENTIFICATION RESULTS

108/109107/109

WhorlAccepted 28/28Properly 27/27Image

-lainarch16/1717/1716/17

---VII. CONCLUSIONS

The FP encoding based on ridge curvature has thefollowing advantages over the mnutiae point matchencoding:Almost all the FP features contribute to encoding theFPimage and making the TF.The ridge curvature approach is robust against skinelasticity. The basic features of ridge relative locationand ridge curvature are invariant to enlargement orshrinkage of the FP image due to different amounts ofpressure applied to the FP reader.The corrupted regions in theFP image will corrupt onlythe specifier that encounters that regions. Thesespecifiers can be easily discardedin the matching phase.

I t has a simple and fast matching phase.It produces TF of fixed size. The number of specifiersdetermnes the TF size. If the algorithm encodes the FPimage using N specifiers, then the TF contains N pointpairs. Each point pair requires at most8bytes and hencethe TF size will be 8xN bytes.

VIII. REFERENCES[I ] Federal Bureau of Investigation, The Science inF ingerprint: Classification and Uses, Washington,C.C.:U.S.Govemment Printing Office, 1984.[2] H. C. Lee and R. E. Gaensslen, eds., Advances inF ingerprint Technology,New York: Elsevier, 199 .[3] A. K. Hrechak and J. A. McHugh, AutomatedFingerprint Recognition Using Structural Matching,Pattern Recognition, Vol. 23,No8,pp. 893-904,1990.[ 4 ] N. K. Ratha, K. Karu, S. Chen, and A. K. Jain, A Real-Time Matching System for Large FingerprintDatabase, IEEE Trans. PAM I , Vol.18, no.8,pp.799-813, 1996.

(a) Original fingerprint image

(b) Specifiers tracks superimposed on enhancedblack and white image.Fig. 8a) Original Image b) Specifiers tracks

[ 5 ] A. K. Jain., L. Hong. andR. Bolle, On-Line FingerprintVerification, IEEE Trans. P A M , Vol. 19, no. 4, pp.302-313, 1997.[6] A. Saleh and R. Adham, Feature Enhancement in LowQuality Fingerprint Image, IEEE 32 SSST,2000.[7] P. G. W. Murray and M. Wright, Numerical Linear

Algebra and Optimization, Vol.1, Addison-Wesley,1991.(81 C.1 Watson and C.L. Wilson, NIST Special Database 4.F ingerprint Database. National institute of Standardand Technology. March1992.

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Curvature-Based Matching Approach for Automatic Fingerprint Identification

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