Block Based Low Quality Fingerprint Image · PDF fileBlock Based Low Quality Fingerprint Image Enhancement ... personal authentication in terms of physical access control and ... 2

Block Based Low Quality Fingerprint Image Enhancement

NEBOJSA MARTINOVICJohn Naisbitt University

Graduate School of Comp. Sci.Bul. umetnosti 29, Belgrade

[email protected]

MARKO BEKOUniv. Lusofona de Humanidades e Tecnologias

Computer Engineering DepartmentCampo Grande 376, 1749 - 024, Lisbon

[email protected]

MILAN TUBA ∗

John Naisbitt UniversityGraduate School of Comp. Sci.

Bul. umetnosti 29, BelgradeSERBIA

[email protected]

Abstract: Quality of the fingerprint images is important for the proper performance of the fingerprint imagematching algorithm and recognition by fingerprint recognition systems. In this work we proposed modifiedfingerprint enhancement image normalization by taking into consideration properties of local region. For testingpurposes, we used fingerprint images from NIST database and images were divided into sub-blocks (A x B).Our results confirmed considerable fingerprint image enhancement when proposed algorithm was applied. Theachieved enhancement was validated by the number of extracted minutiae.

Key–Words: Fingerprint recognition, fingerprint minutiae, recognition system, image processing

1 IntroductionFingerprint recognition is the most widely usedbiometric measure and also the biometric featurewith the largest databases. Personal identificationby fingerprint is used for more than decades andwas shown as method with best long-term widelyexperience. Increasing demands for automatedpersonal authentication in terms of physical accesscontrol and digital signature creation, use offingerprint was particularly emphasized [1]. Thefingerprint is unique pattern of friction ridges on ahuman finger. Fingerprint is individual characteristic,remain unchanged during a lifetime and has generalridge patterns that makes it suitable to be classified[2]. The uniqueness of a fingerprint is determined bythe local ridge characteristics and their positions [3].A ridge is defined as a single curved segment, and avalley is the region between two adjacent ridges.

Also, each ridge contains pores, which areattached to sweat glands under the skin. Fingerprintsare left on smooth surfaces like glass or paper, becauseof this sweat. The ridges of fingerprints have beenclassified to form three patterns called loops, whorlsand arches. A loop begins on one side of the finger,curves around or upward, and exits from the otherside. There are two types of loops: radial loops whichslope toward the thumb and ulnar loops which slopetowards the little finger. Whorls form a circular orspiral pattern. Arches slope upward and then down,

∗This research is supported by the Ministry of Education,Science and Technological Development of Republic of Serbia,Grant No. III-44006.

like very narrow mountains. The minutiae, the localdiscontinuities in the ridge flow pattern, are smalldetails that can determine important local featuresin the fingerprint image. There are two types ofminutiae, ridge endings and ridge bifurcations. Aridge ending indicates the location where a ridgecurve terminates and bifurcations are where a ridgebreaks up into two ridges. Considering that fingerprintanalysis is a effective as a means for identification,many automatic fingerprint recognition approacheshave been suggested. For determination of theaccuracy of automatic fingerprint recognition systema computerized system called automated fingerprintidentification system (AFIS) was developed [4]. Thissystem use complex algorithms and it is able to siftthrough huge number of fingerprints and to comparethem in short period of time. This system andits data base is very effective in cases where thereare no particular suspected matches. It is of theutmost importance that fingerprint images are takenproperly because smudged or not fully rolled fromnail-to-nail fingerprints could cause ”false” minutiaeon the image which could alter the search results.It is critical that each minutiae marker is accuratelyset to increase the chances of a match. Obtainingadequate impressions on fingerprint cards could beachieved through continued practice and use of rightequipment.

Quality of the fingerprint images is importantfor the proper performance of the fingerprint imagematching algorithm. The more high-quality samplesare the fingerprint recognition systems are moreaccurate. A fingerprints with distinguishable patterns

Nebojsa Martinovic et al.International Journal of Signal Processing

http://iaras.org/iaras/journals/ijsp

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Figure 1: a) A good quality fingerprint image; b)Fingerprint with scars and ridge breaks; c) Lowquality fingerprint image with a lot of noise

and features that can be extracted and, subsequently,used for matching are generally considered to bea good quality. Local ridge characteristics arenot evenly distributed and the quality of fingerprintimages can be affected by user’s skin condition,improper finger placement, scanner limitation orimpurities on the scanner surface. In practice, asignificant number of fingerprint images is of poorquality and that result in spurious and missing features(Fig. 1). A good quality fingerprint typically containsabout 40-100 minutiae. For a fingerprint image oflow quality, a large number of false minutiae may beextracted. Therefore, after careful fingerprint imagequality assessment, fingerprint image enhancementis very important and required step after imageacquisition and before feature extraction in thefingerprint recognition system [5].

A biometric system has three phases: biometricdata acquisition, feature extraction and decision-making. The acquisition phase is very important andhighly depends on image quality. If quality of imagesis not appropriate, the feature extraction phase cannotoperate reliably. This rest of this paper is organizedas follows. Section 2 is about the literature review.Details of the our proposed fingerprint enhancementalgorithm are given in Section 3. Experimental resultsare given in Section 4. Section 5 concludes the paperand discusses the perspectives.

2 Literature ReviewBased on whether the filter kernel is orientationsensitive, filtering techniques can be divided intotwo broader categories: isotropic and anisotropicfiltering. The most commonly used isotropic filtersare Gaussian filter [6] and median filter [7]. The maindisadvantage of these filters is the inability to enhancea fingerprint image considering that fingerprint is non-stationary and ridge size, orientation and length variesfrom part to part. Anisotropic filtering can effectivelyremove noises from the image but reliable orientation

must be provided. Therefore, in several studies,investigators have attempted to find the best possibleimage enhancement algorithm.

Traditional Gabor filters optimally capture bothlocal orientation and frequency-selective properties offingerprint image. In order to enhance fingerprintimage, number of researchers had applied Gaborfilters to enhance flow-like patterns. One of theexamples was proposed by Hong and al. in [8].Gabor filter banks were used to enhance fingerprintimages based on the estimated local ridge orientationand frequency and reported solid improvement ofthe clarity. There are five stages of this algorithm:normalization, ridge orientation estimation, ridgefrequency estimation, filtering and thinning. In [9]a algorithm for automatic fingerprint recognition intwo stages was proposed. In the first one, biometriccharacteristics of fingerprints are extracted and inthe second stage, those fingerprints are matched withtemplates belonging to the test database.

In [10] enhance of fingerprint images was done bycombining the Gabor filter and FFT. Proposed methodfor fingerprint image segmentation was based on theimage histogram and density. Enhances was doneby using the better enhancing filter in each part andthe experimental results showed that this combinationprovide good enhancement.

Similiar method as in [10] was proposed in[11]. For fingerprint image enhancement instead ofcombination of Gabor filter and FFT, FFT was appliedon 32 × 32 and Gaussian filter was used in eachinterconnection of the blocks.

In [12] two methods for fingerprint imageenhancement were introduced. One was usinghistogram equalization, Wiener filtering and imagebinarization while the other was using anisotropicfilter for direct gray scale enhancement. In [13] animproved version called modified Gabor filter wasintroduced. It was shown that modified Gabor filtercan significantly reduce the false rejection rate.

Considering that clarity of the images and thedegree of correspondence between the search printand the database print is critical for fingerprintmatching, there are several methods proposed toovercome low quality images of fingerprints thatcome from degraded and corrupted with elementsof noise. In order to remove scars, in [14]. thediffusion matrix was proposed to reconstruct thebroken ridges. An estimation of the flow of ridgesand valleys by line sensor was presented in [15]. Adirectional filter that strengthens the ridge pattern oflocal dominant orientation and suppress scar linesthat are oriented against the dominant direction wasused in [16] to reconstruct the patterns in acquiredimages. In [17] a technique that employs contrast



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stretching approach to improve the clarity betweenforeground and background of the fingerprint imagewas proposed.

In [18] a method for fingerprint enhancementbased on orientation field estimation algorithm wasproposed. Orientation was determined by priorknowledge of fingerprint structure. A dictionaryof reference orientation patches was used forrepresenting the orientation. Loopy belief propagationwas used for estimation of orientation field which wasposed as an energy minimization problem.

Keun et al. [19] proposed the algorithm thatdecomposes a fingerprint image in the analysis stage,processes parts in the processing stage and put themback together as the enhanced image in the synthesisstage. This improved algorithm reduces the influenceof noise on the ridges and valleys, enhances the ridgesmoving shape and preserves the spatial characteristicsat minutiae and singular points.

In [20] an approach for fingerprint enhancementthat estimate all the intrinsic properties of fingerprintswas introduced. In [21] fingerprint image enhan-cement using decimation-free directional filter bankwhich provides output in the form of directionalimages was employed. These algorithms are usefulfor good quality images but in case of low qualityimages their performance decrease due to the noisesuch as smudges, imperfections, crevasses andholes, so these images enhancement is worth ofinvestigation.

In [22] spectral filtering was proposed asalgorithm for fingerprint enhancement. Adaptiveboost spectral filtering algorithm was based on aprogressive enhancement with additional feedbackin a spatial-partitioning and frequency-domainapproach. Gaussian-matched filter was applied andit started from high-quality regions and finished tolower-quality regions by iterating though a goodspectra of enhanced ridges.

3 Fingerprint Enhancement Algo-rithm

Fingerprint enhancement can be conducted on binaryimages or gray level images. A binary ridge imageis an image where all the ridge pixels are assigned avalue one and valley pixels are assigned a value zero.

However, after applying a ridge extractionalgorithm on the original gray level images,information about the true ridge structures isoften lost depending on the performance of the ridgeextraction algorithm. Therefore, enhancement ofbinary ridge images has its inherent limitations. Ina gray level fingerprint image, ridges and valleys

in a local neighborhood form a sinusoidal-shapedplane wave which has a well defined frequency andorientation.

Fingerprint enhancement algorithm includesseveral techniques. All these steps in figureenhancement are very important before fingerprintimage is introduced to fingerprint recognitionsystems. These techniques are:

• Normalization

• Segmentation

• Orientation estimation

• Ridge frequency estimation

• Gabor filtering

• Binarization and thinning

In order to standardize pixel intensity,normalization should be the first process step inimage enhancement process. This can be done byadjusting the range of gray level to a previouslydetermined mean value and variance value. Everyimage has finite number of rows and columns, rowsare represented as i, and columns are representedas j in a (i, j) matrices. We can state that intensityI(i, j) represent the grey-level value at pixel (i, j),and N(i, j) represent the normalized grey level value[7]. The normalized image can be defined as:

G(i, j) =

Mo+

√(V ARo(I(i, j)−M)2/V ARif I(i, j) > M,

Mo−√

(V ARo(I(i, j)−M)2/V ARotherwise

(1)

(2)

where M and V are the estimated mean value andvariance value of I(i, j), and M0 and V0 are thedesired mean and variance values. Ridge structuresin fingerprint are not changed during normalizationprocess. Dynamic levels of variation are standardized.

There are foreground and background regions inan fingerprint image and when segmentation processis applied those regions are clearly separated. Imagesegmentation is necessary step in many differentapplications and one of the common task is todetermine threshold values [23], [24]. Foreground isclear area of the fingerprint and it contains ridges andvalleys. Background is the region outside of cleararea and usually does not contain valid information.Foreground region generally has wider gray scale



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comparing it to background area where the scale isvery low. Foreground area is the point of interestwith ridges and valleys and background has no validinformation for further processing.

The next step is orientation information. Ridgeorientation is normally used for detecting, describingand matching minutiae and singular points (uppercore, lower core and delta). It defines local orientationof ridges in a fingerprint. There are several algorithmswhich use this feature. Gradient vectors are one of themost important concepts in image processing and itdefines orientation of the ridges locally. Orientationvector is orthogonal to the gradient. Gradientvectoring are used for edge detection. Local ridgefrequency is important parameter. Image is dividedinto blocks of nxn values and this is first stepin frequency estimation stage. Next step is toproject all the values of the block along a directionorthogonal to ridge orientation previously calculated.Ridge orientation and ridge frequency are used toconstruct Gabor filter. This filter is used becauseit has frequency selective and orientation selectiveproperties. Those characteristics allow the filterto be fine tuned for maximum ridge detection atspecific orientation and frequency in image. For lowquality image, ridge orientation estimation is veryimportant step for automatic fingerprint recognitionso, new improvements for orientation estimation andcorrection are should be proposed and investigated.

Binarization is process where certain thresholdvalue is generated and compared with each pixel inimage and changed its value to black or white. Fordetermination of optimal threshold values differentapproaches can be found in literature [25], [26].

The main purpose is to improve contrastbetween black pixels (ridges) and white pixels inimage (valleys). This contrast leads to emergingminutiae. Contrast improving is common algorithmof image processing used in different applications[27]. Thinning is used on foreground area and maingoal is to thin ridges in order to get ridges only onepixel wide. This is done by multiple sub-iterationsof thinning algorithm. Usually, shape is similar toskeleton and the technique is called skeletonization.The final result shows if the connections of variousridges are well preserved or not. In [28] enhancedalgorithm that was done by adaptive normalizationwas proposed. Image was divided into sub blocks(A × B). For each sub block, estimated meanand variance are calculated. Those parameters aredetermined according to those statistical parameters.We have tested this algorithms on 24 × 24 sequence.Fingerprint image consists of rows (i) and columns(j) and every pixel has its own intensity level namedI(i, j). Normalization processing formula is derived

form [8]. M0 and V AR0 are mean and variancevalues which are desired and M and V AR value aremean and variance of the image. M0 and V AR0

should be set according to characteristics of image.One of the problems that may occur is that theimage can have blurry regions and those regions canhave very low quality information. New parameters(adaptive values) can be used in order to deal with thisproblem. Normalization algorithm consists of threesteps: histogram equalization, ROI (region of interest)section selection and adaptive normalization.

3.1 Histogram Matching

Histogram matching is a process in which contrast isadjusted to the desired values and intensity of everypixel is better distributed. The improving of the globalcontrast is accomplished by adjusting the intensitydistribution on a histogram [29]. This techniqueallows areas of lower local contrast to reach a highercontrast without affecting the global contrast andspreading out the most frequent intensity values. ROIwith a lower contrast gain higher contrast after thisprocess.

3.2 ROI Selection

The next step is fingerprint image segmentation. Itshould be noted that only a region of interest (ROI)is useful area to be recognized for each fingerprintimage. Fingerprint image can be taken with highor low fingerprint information depending of variousfactors. Almost every image has some noise and it isundesirable. That is the reason why area with highinformation should be selected in advance before anyfurther processing. This can be done by using sub-blocks which are non overlapping with each other in24 × 24 sequence. Blocks with a high variance valueare selected for the following steps. If parameter Vi isvariance of gray for its block, block is classified in:

BROI = BORIGIN , if Vi > Vt (3)

where parameter BROI is region of interest,BORIGIN is the original image data values and Ois block with all zeros. Two step process is advisedbecause fingerprint image can have both homogenousand non-homogenous regions inside image.

3.3 Local Normalization

In the first step, values M0 and V AR0 are calculatedfrom statistics of the ROI. Normalization is calculatedfor ROI of that block. Parameters M0 and V AR0 arefixed values.



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algorithm 1: Normalization is calculated for ROIof that block. Parameters M0 and V AR0 has fixedvaluesalgorithm 2: In the first algorithm, initial parametersM0 and V AR0 have fixed values. We changed M0

and V AR0 parameters in order to enhance image.This is done by adopting new desired parameters, Mi

and V ARi, for ith block of the ROI, in followingequation:

MI =Mo − α1 ∗ (MI −Mo) (4)V ARI = V ARo − α2 ∗ (V ARI − V ARo) (5)

where α1 and α1 are the weighting factors which arecontributing to factor of variation. ParametersMI andV ARI are previously calculated mean and varianceof the ith block. Desired parameters of the aboveequations are changed according to block propertiesbeing calculated.

4 Experimental ResultsFor testing purposes, we used fingerprint images fromNIST database. All images are in 8-bit gray scale andhave 512×512 pixels. For this work, partitioned blockis 24× 24 pixels. On Fig. 2 is original image.

Figure 2: Original fingerprint image from NISTdatabase

During enhancement process various values ofparameters are used. Parameters for α1 and α2 canbe dynamically changed according to image qualityand parameters for MI and VARI also can be changedaccording to image quality.

In Fig. 3, we have shown image normalization.When Algorithm 1 is applied, image quality isacceptable. In given image, results are notwell enhanced because local properties were notconsidered [8, 9]. Fingerprint images contain welldefined regions, corrupted regions but with enough

Figure 3: Enhanced image

Figure 4: Enhanced image after block processing

information which can be extracted and corruptedregions which is unrecoverable. The purpose ofenhancement algorithm is to improve clarity of firsttwo regions and to remove unrecoverable regions.In algorithm 2 local properties were taken intoconsiderations. Number of extracted minutiae wassignificantly lower when algorithm 2 was applied. InFig. 3, low quality image was normalized and numberof extracted minutiae was over 150. Also, a largenumber of spurious minutiae was detected. Afterblock processing was applied, number of extractedminutiae was decreased below 120 which is not idealbut acceptable (Fig. 4).

5 ConclusionConsidering that fingerprint images originating fromdifferent sources could be different in terms of quality,often some enhancement algorithms are required. Themain task of enhancement algorithms is to createan fingerprint image that does not contain artificiallygenerated ridge structures that might later lead to thedetection of false minutiae features. Enhancementprocess should significantly improve the matching



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performance over a large number of fingerprints.In this work we proposed modification of

fingerprint enhancement normalization for image bytaking into consideration properties of local region.Selection of ROI method is suggested by usingstatistics of the block. Block based fingerprint imageenhancement could be used for number of extractedminutiae decrease when images are poor quality.

Acknowledgements: This research is supported bythe Ministry of Education, Science and TechnologicalDevelopment of Republic of Serbia, Grant No. III-44006.

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Block Based Low Quality Fingerprint Image · PDF fileBlock Based Low Quality Fingerprint Image Enhancement ... personal authentication in terms of physical access control and ... 2