A Novel Approach for Fingerprint Matching using Minutiae

Ashwini R. Patil1, Mukesh A. Zaveri21,2Computer Engineering Department

Sardar Vallabhbhai National Institute of TechnologySurat, India

Email: 1ashwinipatil29@gmail.com, 2mazaveri@coed.svnit.ac.in

Abstract—Fingerprint recognition is one of the most impor-tant challenging tasks of the biometrics identification due to thefact that they are easy to obtain and most importantly they arereliable. A number of pattern recognition methods have beenused to perform fingerprint matching. Among such methodsfingerprint matching based on minutia features is one ofthe researched problems. So we proposed effective fingerprintmatching algorithm based on feature extraction. Also we havepresented a novel fingerprint recognition technique for minutiaextraction and a minutia matching. For minutia marking weconsidered one special false minutiae removal method and forminutiae matching we considered alignment based method.Experimental results show that this method achieves a muchbetter matching performance.

Keywords-biometrics; pattern recognition; minutiae feature

I. INTRODUCTION

Due to high probability of uniqueness [1] and a com-paratively good acceptance of users shows why fingerprintrecognition is currently the most commonly used biometricsmethod. Comparison of fingerprints is a fundamental methodfor the identification of people. Two main factors on whichfingerprint identification is based on are immutability andthe individuality of fingerprints [2]. The pattern on eachfinger is everlasting and unchanging from before birth untildecomposition after death which is referred as immutabilityand individuality refers to the uniqueness of ridge detailsacross individuals. No two persons [3], not even identicaltwins have identical fingerprints [4]. The probability offinding two fingerprints alike is 1 in 1.9 x 1015[5]. That’swhy fingerprints are extremely effective when high degreeof security is required.

A fingerprint pattern is composed of a sequence of ridgesand valleys [6] which generally run parallel to each otherin fingerprint. The ridges are dark lines while the valleysare the light areas between the ridges. The underlying ridgestructure pattern can be analyzed on a global and local level.These are the features of fingerprint. The global featuresmainly give an overall characteristic of the finger. A singlerepresentation of fingerprint image using global featuresis valid for the entire fingerprint. The global classificationschema of fingerprints is provided in [7]. Major conceptused in local representations of fingerprints is finger ridges.Minutiae are minute details of the fingerprint [8] and theyare shown in Fig.1. Minutiae are the locations where a

Figure 1: Minutiae Types

ridge becomes discontinuous. It is widely believed that theminutiae are the most discriminating and reliable features [9]present in the fingerprints. There are more than 100 differenttypes of minutiae have been identified, among which ridgebifurcation i.e. a point where a ridge breaks up into tworidges and ridge endings i.e. a point where a ridge endssuddenly are the most widely used [10][7]. Minutia-basedrepresentation of fingerprints is an ANSI-NIST standard[11]. The computation of local ridge orientation and the localridge frequency play a crucial role in all consecutive stagesof a fingerprint verification system [12].

Lin in [13] directly aligns one fingerprint image to an-other by transforming it to the polar co-ordinate system.Correlation-based matching [14] and ridge feature-basedmatching [15] do not avoid pre-alignment stage to recoverthe geometric transformation between the two fingerprintimpressions in order to ensure an overlap of common regionbefore the minutiae pairing. This fingerprint pre-alignmentis certainly primordial in order to maximize the number ofmatching minutiae. Also due to noise, various false minutiaeare present in the fingerprint. These shortcomings makeit a desirable goal to develop new strategies that allowfingerprint recognition gain a much wider use. Our approachovercomes all above drawbacks by using alignment basedmethods which has less computation overhead.

The paper is organized as follows. In Section 2 we discussour proposed methodology. In Section 3, we present ourexperimental results which justify the method which we have

2010 Fourth Asia International Conference on Mathematical/Analytical Modelling and Computer Simulation

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DOI 10.1109/AMS.2010.70

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Figure 2: System Flow

implemented.

II. PROPOSED METHODOLOGY

Our approach is mainly based on the minutiae-basedrepresentation of a fingerprint. In which the fingerprint isrepresented by two minutiae feature parameters: 1) x andy coordinate of the minutia point 2) θ i.e. the minutiaorientation. Our approach gives the number of matchedminutiae on the basis of these features by considering bothquery and reference fingerprints and uses it to generatesimilarity scores. According to forensic guidelines, whentwo fingerprints have a minimum of 12 matched minutiaethey are considered to have come from the same finger [16].So more matched minutiae yield higher similarity scores.That is when the number of minutiae on both fingerprints islarge; we can confidently distinguish the legitimate and fakefingerprint using the number of matched minutiae.

The proposed methodology of our fingerprint recognitionsystem has four main stages. They are 1) Fingerprint acqui-sition 2) Fingerprint Preprocessing 3) Minutiae extraction 4)Minutiae Matching. The flow of our approach is as shown inFig.2. For image acquisition a fingerprint to be recognizedis captured and processed in a particular moment with onefingerprint stored in the database in real time. The next stagei.e. preprocessing is an important step prior to fingerprintfeature extraction. The generic process of the preprocessingencompasses enhancement, binarization and segmentation.

A. Image Pre processing

The performance of a fingerprint image-matching algo-rithm depends heavily on the quality of the input fingerprintimages [17]. It is very important to acquire good qualityimages but in practice a significant percentage of acquiredimages is of poor quality due to some environmental factorsor user’s body condition. The poor quality images causetwo problems: (1) many spurious minutiae may be createdand (2) many genuine minutiae may be ignored. Therefore,an image preprocessing is necessary to increase the perfor-mance of the minutiae extraction algorithm. The steps to do

Figure 3: Image Preprocessing

preprocessing on fingerprint are as explained below:1) Image Enhancement : Since the fingerprint images

acquired from sensors are not ensured with perfect quality,so to make the image clearer for easy further operationssome image enhancement techniques are applied on image.According to [18], about 10 percent of all fingerprint imagescaptured are of poor quality. Method adopted in our systemfor enhancing the fingerprint is Histogram Equalization.Histogram Equalization (HE) is one of the most commonlyused algorithms to perform contrast enhancement due toits simplicity and effectiveness [19]. Also it should bementioned that histogram based techniques is much lessexpensive comparing to the other methods.

2) Image Binarization : Fingerprint Image binarization isto transform the 8-bit gray fingerprint image to a 1-bit imagein which 0-value is for ridges and 1-value is for non ridgeareas also called as furrows. A locally adaptive binarizationmethod is used to binarize the fingerprint image. Such anamed method comes from the mechanism of transforminga pixel value to 1 if the value is larger than the meanintensity value of the current block (16x16) to which thepixel belongs. After binarization, ridges in the fingerprintare highlighted with black color while furrows are white[20]. The fingerprint obtained after binarization is as shownin Fig.3(c).

3) Image Segmentation : In general, for doing any furtheroperation only a Region of Interest (ROI) of fingerprintimage is considered. For this, fingerprint segmentation isnecessary to eliminate the undesired noisy background andreduce the size of the input data [21]. The image area withoutridges and furrows is first discarded since it only holdsbackground information. Then the bound of the remainingeffective area i.e. ROI is sketched out. To extract the ROI,following two-step method is used. The first step is blockdirection computation while the second is intrigued fromsome morphological methods.

Block direction computation :1) Determine the block direction for each block of the

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fingerprint image with WxW in size (W is 16 pixelsby default). The algorithm for it is:

• For each pixel of the block we have estimatedthe gradient values along x-direction (gx) and y-direction (gy). To fulfill this task we used hori-zontal Sobel operator and vertical Sobel operatorfor calculating horizontal and vertical gradientsfor each pixel, respectively.

⎛⎝

1 0 −12 0 −21 0 −1

⎞⎠

⎛⎝

1 2 10 0 0−1 −2 −1

⎞⎠

Horizontal VerticalSobel Operator Sobel Operator

• For each block, the least square approximation ofthe block direction is calculated using followingformula. For all the pixels in each block,

lsa = 2(gx∗gy)((gx)2−(gy)2)

2) Then some blocks which have no significant informa-tion on ridges and furrows are discarded. It is done bythe following formula:

E = 2(gx∗gy)+((gx)2−(gy)2)(W∗W ((gx)2+(gy)2))

The output after the block direction computation is shownin Fig.3(d).

ROI extraction by Morphological operations : The bi-nary morphological operators OPEN and CLOSE are appliedon the binarized fingerprint image. The primary functionof the morphological operators is the elimination of anyobstacles and noise from the image [16]. OPEN operationexpand images and remove peaks introduced by backgroundnoise while CLOSE operation which shrink images andeliminate small cavities. The ROI of the fingerprint is shownin Fig.3(e).

B. Minutiae Extraction

A critical step in studying the statistics of fingerprintminutiae is to reliably extract minutiae from the fingerprintimages. For this we used two step procedures. Firstly wethinned the binary image of the fingerprint and then weextract the minutiae from it.

1) Fingerprint Ridge Thinning: The main purpose ofridge thinning also called as skeletonization is to eliminatethe redundant pixels of ridges [21]. The pattern shapesobtained after thinning can be represented by 1 pixel widelines. This pattern has some unnecessary spurs, bridges andline breaks. So we used clean, hbreak, spur and thinningmorphological operators. Fingeprint obtained after thinningis as shown in Fig.4(a)

2) Minutia Marking: After the fingerprint ridge thinning,marking minutia points is relatively easy. We only needthat how can we found out the bifurcation and terminationin thinned image. In general, for each 3x3 window, if the

Figure 4: Minutiae Extraction

Figure 5: Bifurcation and Termination

central pixel is 1 and has exactly 3 one-value neighbors,then the central pixel is a ridge branch (Fig.5 (a)). If thecentral pixel is 1 and has only 1 one-value neighbor, thenthe central pixel is a ridge ending (Fig.5 (b)).

Two stages procedure was presented in [22] for extractingminutiae. We modified that procedure by including one newconcept average inter-ridge width (D) which refers to theaverage distance between two neighboring ridges. Firstlyscan a row of the thinned ridge image and sum up allpixels in the row whose value is one. Then after dividingthe row length with the above summation we got D. Formore accuracy we had done such kind of row scan uponseveral other rows and column. Finally all the inter-ridgewidths are averaged to get the final D. Together with theminutia marking; all thinned ridges in the fingerprint imageare labeled with a unique ID for further operation. Theminutiae marked in fingerprint by using our approach areshown in Fig.4(b).

C. Minutiae Post Processing

Due to different types of noise present in the fingerprintimages like under-inking, over inking, scars or excessivelyworn prints also due to the segmentation and thinningprocess, a large number of false minutiae are discoveredamong the extracted minutiae obtained from thinned image.These false minutiae will significantly affect the accuracyof matching if they are simply regarded as genuine minutia.Therefore in post processing stage removal of false minutiaeis important to obtained final minutiae which we can use formatching purpose, so that we can get accurate results.

The false minutiae are of various types like spikes,bridges, holes, spurs, ladder. They are as shown in Fig.6.Ratha et al. [23] use three heuristic rules in order to eliminate

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Figure 6: False Minutiae: (a) spike piercing into a valley.(b) case of spike which falsely connects two ridges. (c) twonear bifurcations located in the same ridge. (d) two ridges arebroken and have nearly the same orientation but have shortdistance. (e) same as the case d with the exception that onepart of the broken ridge is so short that another terminationis generated. (f) extension of case d but third ridge is foundin the middle of the two parts of the broken ridge. (g) onlyone short ridge present in the threshold window.

ridge breaks, spikes and boundry effect. Hung [24] proposesa set of algorithms for detecting and removing spurs, holesand bridges. A combined statistical and structural approachis proposed by Xiao and Raafat [25] in order to removeridge breaks and false bifurcations. We proposed our postprocessing algorithm which is able to detect and removenearly all false minutiae structures mentioned above.

The procedure to remove false minutiae explained in [23]only handles the case (a), (d), (e) and (f). D. Maio andD. Maltoni in [26] have not considered any false minutiaremoval procedure. They simply assumed the image qualityof fingerprint obtained by sensor is fairly good. Jain et al.[27] have not assumed any systematic healing method toremove spurious minutia although it lists all types of falseminutia shown in Fig.6 except case (c).

We appled one special method containing four stages toremove all false minutiae. It is as follows.

1) For case (a), if the distance between one bifurcationand one termination is less than D (which we havecalculated in the minutiae marking stage) and the twominutiae are in the same ridge then remove both ofthem.

2) For case (b) and (c), if the distance between twobifurcations which are on the same ridge, is less thanD then remove these two bifurcations.

3) For (d), (e), (f) case if two terminations

• are within a distance D• don’t have any other termination located between

them.• have their directions coincident with a small angle

variation.

Figure 7: A bifurcation having three terminations (a) Threeneighbors become terminations (b) Each termination hastheir own orientation

Then these two terminations are derived from brokenridge and regarded as false minutia. So they areremoved.

4) For case (g) if two terminations are found to be locatedon a short ridge with length less than D then removethese two terminations.

The above proposed procedures for removing false minutiahave two advantages. One is that the ridge ID (labeled inminutiae marking stage) is used to distinguish minutia andthe seven types of false minutia are strictly defined compar-ing with those loosely defined by other methods. The secondadvantage is that to reduce the computational complexitythe order of removal procedures is well considered. Alsoseven types of false minutia are strictly defined comparingwith those loosely defined by other methods. The relationamong the false minutiae types doesn’t utilized by [27] butour procedure overcomes this way. For example, procedure3solves the (d), (e), and (f) cases in a single check routine.

In our approach each minutia is completely character-ized by the following parameters: 1) x-coordinate, 2) y-coordinate, and 3) orientation. For direction, firstly weconsider the case for bifurcation. From bifurcation point wegot three ridges which have their own directions. So theyrepresent their own directions using technique proposed in[28]. Lin [13] simply chooses the minimum angle amongthe three anticlockwise orientations starting from the x-axis. Both methods drop the other two directions away,so some information loses which can affect the matchingperformance. So here we propose one novel method for it.We break a bifurcation into three terminations. To identifythese three new terminations is very easy; they are simplythree neighbor pixels of the bifurcation. Before these threeridges are connected to the bifurcation, but now they areassociated with a terminations as shown in Fig.7.

Find a ridge segment that’s starting point is the termina-tion and length is D. Sum up all x-coordinates of pixels inthat ridge segment. Then sx is obtained by dividing abovesummation with D. Similarly by summing all y-coordinatesand dividing it by D we obtained sy. Finally we got thedirection by using atan((sy − ty)/(sx − tx))

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D. Minutiae Matching

Now if we have two set of minutia of two fingerprintimages, the minutia match algorithm determines whether thetwo minutia sets are from the same finger or not. We used analignment-based match algorithm which is partially derivedfrom the [13].

1) Alignment Stage: Suppose we have two fingerprint im-ages to be matched then choose any one minutia from eachimage, calculate the similarity of the two ridges associatedwith those two referenced minutia points. If the similarityis larger than a threshold, transform each set of minutia toa new coordinate system whose origin is at the referencedpoint and whose x-axis is coincident with the direction ofthe referenced point. The procedure to alignment is as givenbelow.

1) We calculate the average inter-ridge length L andstored the ridge associated with each minutia as aseries of x-coordinates (x1, x2, . . . xn) of the pointson the ridge. Then a point is sampled per ridge lengthL starting from the minutia point unless the totalridge length is less than 10*L. So the similarity S ofcorrelating the two ridges is derived from the formula,

S =∑ m

i=0(Xixi)√∑ mi=0(Xi

2(xi)2)

where (xi, . . . xn) and (Xi, . . . XN ) are the set ofminutia for each fingerprint image, m is minimal oneof the n and N value. If the similarity score is largerthan 0.8, then go to step 2, otherwise continue to matchthe next pair of ridges.

2) For each fingerprint, translate and rotate all otherminutia with respect to their reference minutia accord-ing to the following formula:

⎛⎝

xinew

yinew

θinew

⎞⎠ = TM∗

⎛⎝

xi − xyi − yθi − θ

⎞⎠

where (x, y, θ) is the parameters of the referenceminutia, and TM is⎛

⎝cosθ −sinθ 0sinθ cosθ 0

0 0 1

⎞⎠

In [13] Lin’s directly align one fingerprint image to anotheraccording to the discrepancy of the reference minutia pair.But it still requires a transform to the polar coordinatesystem for each image at the next minutia match stage.Our approach is to transform each minutia according to itsown reference minutia and then do match in a unified x-ycoordinate. Therefore, less computation overhead is requiredthrough our method.

2) Matching Stage: After we got two set of transformedminutia points from alignment stage, we use the elasticmatch algorithm to count the matched minutia pairs. Becausethe strict match algorithm requires all parameters (x, y, θ)

Figure 8: FAR and FRR curve (a)Blue dot line: FRR curve(b)Red dot line: FAR curve

must be same for two identical minutiae, but it is not possibledue to the slight deformations and inexact quantization ofminutia. So, we are assuming here that two minutiae havenearly the same position and directions are identical.

To achieve elastically match minutia we placed a bound-ing box around each template minutia. If the minutia tobe matched is within the rectangle box and the directiondeviation between them is very small, then the two minutiaeare regarded as a matched minutia pair. Each minutia in thetemplate image either has no matched minutia or has onlyone corresponding minutiae. For the final match ratio for twofingerprints we first calculate the number of total matchedpair and the number of minutia of the template fingerprint.Then the score is 100*ratio. If the score is larger than a pre-specified threshold, the two fingerprints are from the samefinger.

III. EXPERIMENTAL RESULTS

We have collected 10 fingerprints of each person with dif-ferent situations and with different rotation. So our databasecontains near around 110 fingerprints. After applying ouralgorithm on each fingerprint we got the bifurcation andterminations as features. For matching we consider twofingerprint images. One is input image and other is queryimage. We have to see that by considering these featureswhether query image matches with the images in thedatabases or not. If not matching then it shows percentageof matching of these two images.

Two indexes are well accepted to determine the perfor-mance of a fingerprint recognition system:

• FAR (False Rejection Rate) - It measures the percentof invalid inputs which are incorrectly accepted.

• FRR (False Acceptance Rate) - It measures the percentof valid inputs which are incorrectly rejected.

The graph showing FAR and FRR calculated by our ap-proach are shown in Fig. 8.

IV. CONCLUSION

The reliability of any fingerprint matching system stronglyrelies on the precision obtained in the minutia extraction

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process. A number of factors are deleterious to the correctlocation of minutia. Among them, the main factor is poorimage quality. In our approach, we build a minutia extractorand a minutia matcher by using following concepts- segmen-tation using morphological operations, minutia marking byspecially considering the average inter-ridge width, minutiaunification by decomposing a branch into three terminationsand matching in the unified x-y coordinate system after a2-step transformation in order to increase the precision ofthe minutia localization process and elimination of spuriousminutia with higher accuracy. The proposed alignment-based elastic matching algorithm is capable of findingthe correspondences between minutiae without resorting toexhaustive research. In addition, since the minutia-basedfingerprint representation is standard and widely used, ouralgorithm has the advantage of applicability to most existingfingerprint databases.

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