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1 The Internatonal Arab Journal of Informaton Technology, Vol. 13, No. 1A, Fngerprnt Image Qualty Fuzzy System Abdelwahed Motwakel 1 and Adnan Shaout 2 1 Collage of Post Graduate Studes, Sudan Unversty of Scence and Technology 2 The Electrcal and Computer Engneerng Department, the Unversty of Mchgan-Dearborn Abstract: In ths paper, we present a novel technque to analyss fngerprnt mage qualty usng fuzzy logc. The qualty of fngerprnt mage greatly affects the performance of mnutae extracton and the process of matchng n fngerprnt dentfcaton system. The system uses the extracted four features from a fngerprnt mage whch are the Local Clarty Score (LCS), Global Clarty Score (GCS), Rdge_Valley Thckness Rato (RVTR), and the contrast. The proposed fuzzy logc system uses mamdan fuzzy rule model whch can analyss and determnate the fngerprnt mage type (oly, dry or neutral) based on the extracted feature values and fuzzy nference rules. Keywords: Fngerprnt mage qualty, LCS, GCS, RVTR, contrast, fuzzy nference system. Receved September 13, 2015; accepted October 18, 2015; publshed on lne January 28, Introducton Fngerprnt dentfcaton s the most wdely used bometrcs technologes and s used n crmnal nvestgatons, commercal applcatons, and so on. Wth such a wde varety of uses for the technology, the demographcs and envronment condtons that t s used n are just as dverse. However, the dentfcaton performance of such system s very senstve to the qualty of the captured fngerprnt mage. Fngerprnt mage qualty analyss or assessment s useful n mprovng the performance of fngerprnt dentfcaton systems. In many systems t s preferable to substtute low qualty mages for better ones. Therefore, mage qualty analyss takes an mportant part n mage processng. The quantfcaton of mage qualty allows to tune a system and to evaluate measurement accuracy of a gven nput mage [11]. Varous factors can affect the qualty of fngerprnt mages such as dryness/wetness condtons, nonunform and nconsstent contact, permanent cuts and so on. Many of these factors cannot be avoded. Therefore, assessng the qualty and valdty of the captured fngerprnt mage s necessary and meanngful. Many papers n bometrc lterature address the problem of assessng fngerprnt mage qualty. But these methods stll have some problems and can t be sutable for all the condton [11]. Saenko et al. [9] proposed a technque to analyss mage qualty that uses two dfferent methods. The frst s acrsp method that uses matrx norm whch s a scalar that gves some measure of the magntude of the elements of the matrx. The second s a fuzzy logc method whch evaluates the mage qualty by usng IF- THEN-RULES wth the followng parameters: Sharpness, nose, contrast, vgnettng and feld curvature. All of these parameters are lngustc varables and have three possble lngustc values bad, normal, and good. Eun-Kyung and Cho [2] proposed a method to analyss mage qualty by extracted fve features from a fngerprnt as follows: Mean, varance, block drectonal dfference, rdge valley thckness rato, and orentaton change. Accordng to these features they cluster mages by usng ward s clusterng algorthm whch s a herarchcal clusterng method. It ntally assgns an ndependent cluster to each sample, then t seeks the most smlar pars of clusters and merges them nto one cluster. Rahmat et al. [8] proposed a model to determne the standard value used n classfyng the type of dstortons wthn fngerprnt mages based on the mage qualty. They used the rdge-valley clarty score and rdge-valley thckness rato score to descrbe the fngerprnt mage qualty. Rahmat et al. [7] proposed a novel procedure to determne the parameter values of dry fngerprnt mages based on the score of clarty and rdge-valley thckness rato. The parameters are Local Clarty Scores (LCS), Global Clarty Scores (GCS) and rdgevalley thckness rato. L and Xe [5] proposed a method for measurng fngerprnt mage qualty usng Fourer spectrum. Ths method measures fngerprnt mage qualty based on the global characterstcs of the mage and do not relay on local rdge orentaton estmaton. The method frst search the band frequency whch corresponds to the global average perod of rdge. Then the qualty score of the fngerprnt mage s computed by measurng relatve magntude of the band frequency components. Mod and Ellott [6] studed the mpact of fngerprnt mage qualty of two dfferent age groups: 18-25, and 62 and above on overall performance usng two dfferent matchers. The dfference n mage qualty between the two age groups was analysed and the mpact of mage qualty on performance of fngerprnt matchers between the two groups was also analysed. Image qualty analyss was performed usng NFIQ whch s part of NIST Fngerprnt Image

2 172 The Internatonal Arab Journal of Informaton Technology, Vol. 13, No. 1A, 2016 Software (NFIS). NeurotechnologjaVerFnger and bozorth3 NFIS matchers were used to assess the overall performance. The paper s organzed as follows: Secton 2 presents the ratonal of usng fuzzy logc, secton 3 presents a bref ntroducton to fngerprnt mage qualty, secton 4 presents the new proposed fngerprnt mage classfcaton system that classfes the mage nto one of three types; dry, oly or neutral and secton 5 presents the results usng the DB_ITS_2009 database whch s a prvate database collected by the Department of Electrcal Engneerng, Insttute of Technology SepuluhNopember Surabaya. 2. Fuzzy Logc Fuzzy logc s a form of many-valued logc or probablstc logc, t deals wth approxmate (rather than fxed and exact) reasonng. It has been extended to handle the concept of partal truth, where the truth value may range between completely true and completely false [10]. Lngustc varables are used to express fuzzy rules, whch facltate the construct of rule-based fuzzy systems. A lngustc varable can be defned as a varable whose values are words or sentences. For example a lngustc varable such as age may have a value such as young, very young, old, very old rather than 30, 36, 18 etc., However, the advantage of lngustc varables s that they can be changed va hedges (fuzzy unary operators) [10]. Fuzzy Inference System (FIS) s a method that nterprets the values n the nput vectors and based on user defned rules, assgns values to the output vector. Usng a GUI edtors and vewers n the MATLAB fuzzy logc toolbox, we can buld the rules set, defne membershp functons and analyse the behavour of a FIS. The edtors and vewers are used to edt and vew the membershp functons and rules for FIS [1]. 3. Fngerprnt Image Qualty In general, the fngerprnt mage qualty reles on the clearness of separated rdges by valleys and the unformty of the separaton. Although, the change n envronmental condtons such as temperature, humdty and pressure mght nfluence a fngerprnt mage n many ways, but the condton of skn stll domnate the overall qualty of the fngerprnt [9]. Dry skn tends to cause nconsstent contact of the fnger rdges wth the scanner s platen surface, causng broken rdges and many whte pxels replacng rdge structure as shown n Fgure 1-c. To the contrary the valleys on the oly skn tend to fll up wth mosture, causng them to appear black n the mage smlar to rdge structure as shown n Fgure 1-a. Fgure 1 showss the examples of the oly, neutral and dry mages, respectvely [2]. a) Oly mage. b) Neutral mage. Fgure 1. Examples of fngerprnt Images [2]. c) Dry mage. Oly Image: Even though the separaton of rdges and valleys s clear, some parts of valleys are flled up causng them to appear dark or adjacent rdges stand close to each other n many regons. Rdges tend to be very thck [2]. Neutral Image: In general, t has no specal propertes such as oly and dry. It does not have to be fltered [2]. Dry Image: The rdges are scratchy locally and there are many whte pxels n the rdges [2]. In ths paper, the FIS s appled to analyze fngerprnt mage qualty to three types (oly/dry/neutral) based on four features whchh are the LCS, GCS, Rdge_Valley Thckness Rato (RVTR), and the contrast, then use mamdan fuzzy rule model to determnate each type depend on the extracted feature values and the FIS rule base. 4. Proposed Method Fgure 2 shows the flowchart of the proposed method. The method starts by extractng four features from the fngerprnt mage for mage qualty assessment usng FIS to determne the type of the fngerprnt mage qualty. Features Extracton Qualty Analyss Fgure 2. Proposed method flowchart. 4.1.Features Extracton In ths stage we extracted four features whch are the LCS, GCS, RVTR, and the Global Contrast Factor (GCF), to analyss the fngerprnt mage qualty usng a FIS. The followng subsectons wll present few fngerprnt mage scores that wll be used n analysng fngerprnt mages Rdge-Valley Clarty Scores of Fngerprnt Images Rdge and valley clarty analyss ndcates the ablty to dstngush the rdge and valley along the rdge drecton. A method of analyzng the dstrbuton of segmented rdge and valley s ntroduced to descrbe the clarty of the gven fngerprnt pattern [8]. To perform local clarty analyss, the fngerprnt mage s quantzed nto blocks of sze pxels. Insde each block, an orentaton lne, whch s perpendcular to the rdge drecton, s computed. At

3 Fngerprnt Image Qualty Fuzzy System 173 the center of the block along the rdge drecton, a 2D vector V1 (slanted square shown n Fgure 3) wth sze pxels can be extracted and transformed to a vertcal algned 2D Vector V2. Equaton 1 s a 1D Vector V3, whch s the average profle of V2 [8]. m ( ) j= 1V 2 V =, = 1,...,32 3 m Where m s the block heght (13 pxels) and s the horzontal ndex. Once V3 s calculated as n Equaton 1, then lnear regresson can be appled to V3 to fnd the Determne Threshold (DT1). Fgure 4 shows the method of regonal segmentaton. DT1 s the lne postoned at the center of the Vector V3 and s used to classfy the rdge regon and valley regon. Regons lower than DT1 are classfed as the rdges and the others are as valleys. Hence, the regons of rdges and valleys can be separated n the 2D vector V2 by the 1D average profle V3 wth the DT1 shown as the dotted straght lne n Fgure 4. As rdges and valleys are separated, a clarty test can be performed n each segmented 2D rectangular regons. Fgure 5 shows the gray level dstrbuton of the segmented rdges and valleys. The overlappng area s the regon of msclassfcaton, whch s the area of falng to determne rdge or valley accurately by usng DT1. Hence, the area of the overlappng regon can be an ndcator of the clarty of rdge and valley. For the calculaton of the clarty score refer to Equatons 2, 3, and 4 [8]. V α = V B T β R = R Fgure 3. Regon Segmentaton of vector V2 [8]. LCS = B T ( α+ β ) 2 Where V B s the number of bad pxels n the valley that the ntensty s lower than the DT 1, V T s the total number of pxels n the valley regon, R B s the number of bad pxels n the rdge that the ntensty s hgher than the DT 1 and R T s the total number of pxels n the rdge regon. α and β are the porton of bad pxels. Hence, the LCS s the average value of both α and β. For rdges wth good clarty, both dstrbutons should have a very small overlappng area. The followng factors affect the sze of the total overlappng area [8]: 1. Nose on rdge and valley. 2. Scar across the rdge pattern. (1) (2) (3) (4) 3. Water patches on the mage due to wet fnger. 4. Incorrect of orentaton angle due to the effect of drectonal nose. 5. Hghly curved rdge. 6. Mnutae, bfurcaton, delta pont or core. Fgure 4. Extracton of a local regon and transformaton to vertcal algned rdge pattern [8]. Fgure 5. Dstrbuton of rdge and valley [8]. Factors 1 to 4 are physcal nose found n the mage. Factors 5 and 6 are actual physcal characterstcs of the fngerprnt. Therefore, a small wndow wth sze pxels s chosen to mnmze the chance of encounterng too many dstnct features n the same locaton. The GCS can be computed by the expected value of the LCS [8]. Where GCS=E(LCS(, j)) (5) (.) = (.) H V E = 1 j= 1 As seen n Equaton 5, LCS (, j) s the Clarty Scores whch s calculated accordng to Equatons 2, 3 and 4 at locaton (, j), where and j are horzontal and vertcal ndex of the mage block, respectvely. H and V are the maxmum number of horzontal and vertcal blocks, respectvely. The GCS can be used to descrbe the general rdge clarty of a gven fngerprnt mage. 4. The Rato for Rdge Thckness to Valley Thckness s Computed n each Block. Furthermore, the thckness of rdge and valley are obtaned usng gray level values for one mage block n the drecton normal to the flow rdge. Later, the rato of each block s computed and average value of the rato s obtaned over the whole mage [8]. 5. Global Contrast Factor (GCF) (6) Contrast n mage processng s usually defned as a rato between the darkest and the brghtest spots of an mage. The newly ntroduced GCF corresponds closer to the human percepton of contrast. GCF uses contrasts at varous resoluton levels n order to compute overall contrast [3]. The contrast of any (small) part of an mage s called the local contrast. The global contrast s defned

$174 The Internatonal Arab Journal of Informaton Technology, Vol. 13, No. 1A, 2016 as the average local contrast of smaller mage fractons [3].$ whch t gamma corrected lumnance usng a gamma of 2.2 for standard dsplays [3]. Let us denote the orgnal pxel value wth k, k {0, 1,, 254, 255}. The frst step s to apply gamma correcton wth g=2.

whch t gamma corrected lumnance usng a gamma of 2.2 for standard dsplays [3]. Let us denote the orgnal pxel value wth k, k {0, 1,, 254, 255}. The frst step s to apply gamma correcton wth g=2.

4 174 The Internatonal Arab Journal of Informaton Technology, Vol. 13, No. 1A, 2016 as the average local contrast of smaller mage fractons [3]. Frst, compute the local contrast factors at varous resolutons, and then buld a weghted average based on human percepton method whch t can be approxmated wth the square root of the lnear lumnance, whch t gamma corrected lumnance usng a gamma of 2.2 for standard dsplays [3]. Let us denote the orgnal pxel value wth k, k {0, 1,, 254, 255}. The frst step s to apply gamma correcton wth g=2.2, and scale the nput values to the [0, 1] range. We wll denote the scaled and corrected values lnear lumnance wth L [3]. Y K L = 255 The perceptual lumnance L s now: Y K L = 100 * L = 100 * 255 (7) (8) The square root to compute lumnance was used [3]. Once the perceptual lumnance s computed we have to compute local contrast. For each pxel we compute the average dfference of L between the pxel and four neghborng pxels [3]. By assumng the mage s w pxels wde and h pxels hgh, and the mage s organzed as a one dmensonal array of row-wse sorted pxels, the local contrast Lc for pxel s [3]: Lc = L L + L L + L L + L L w + w (9) The average local contrast for current resoluton C s computed now as the average local contrast Lc over the whole mage [3]. Fgure 6. FIS Edtor for mage qualty classfcaton The Membershp Functon Edtor Ths stage shows the membershp functon for each of the four nputs. Fgures 7, 8, 9, and 10 shows the membershp functon for GCF, LCS, GCS, and RVTR respectvely. Fgure 7. FIS membershp functon edtor for GCF. C 1 = * w * h w * h = 1 Now that, we have computed average local contrasts C, we need to fnd the wegh factors w, whch wll be used to compute the GCF. LC N = 1 * GCF = W C (10) (11) Fgure 8. FIS membershp functon edtor for LCS Qualty Analyss- FIS Fngerprnt mage qualty analyss has been developed and mplemented usng FIS. FIS has been developed usng fve stages. The stages are as follow: The FIS Edtor Ths stage shows the FIS edtor whch dsplays the general nformaton about the proposed method as show n Fgure 6 whch shows the four nputs LCS, GCS, RVTR and GCF wth ther membershp functons. It also shows the three dfferent outputs one for oly, dry, and neutral. Fgure 9. FIS membershp functon edtor for GCS.

Fngerprnt Image Qualty Fuzzy System 175 dry the fngertp. Lkewse, n order to get oly fngerprnt mages, we smeared baby-ol on the fngertps before the mage was taken [7].

The Rules Edtor Ths stage shows the constructng rule base for four nputs and three outputs FIS. Fgure 11 shows the rule base edtor where 63 were created. Each output has two lngustc values.

5 Fngerprnt Image Qualty Fuzzy System 175 dry the fngertp. Lkewse, n order to get oly fngerprnt mages, we smeared baby-ol on the fngertps before the mage was taken [7]. Table 1 shows some of the results of feature extractons. Table 1. Expermental data samples for features. Fgure 10. FIS membershp functon edtor for RVTR The Rules Edtor Ths stage shows the constructng rule base for four nputs and three outputs FIS. Fgure 11 shows the rule base edtor where 63 were created. Each output has two lngustc values. For example, the lngustc values for DRY fngerprnts are Strong Dry (S.Dry) and Moderate Dry (M.Dry). NO LCS GCS RVTR GCF e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e We get the best analyss for fngerprnt mage by usng FIS whch uses fve GUI tools for buldng, edtng, and observng FISs. The rules descrbed n Fgure 11 were tested n our FIS wth DB_ITS_2009 database feature values. Tables 2, 3 and 4, as well as Fgures 12, 13, and 14 shows the performance of our FIS, whch t determne the fngerprnt mage qualty accordng to ther features extracted. Table 2. Oly fngerprnt mage. LCS 1.58 GCS 1.21 RVTR 6.95 GCF 9 Oly Table 3. Neutral fngerprnt mage. 5. Result Fgure 11. FIS rule edtor. The mplementaton of our FIS was done usng MATLAB. A code was wrtten to extract the four features LCS, GLS, RVTR, and GCF as shown n Table 1 and then run ths code after savng t as an.m fle n MATLAB workspace. Then Fuzzy f-then rules was developed and mplemented wth 63 rules. Experment was done usng the DB_ITS_2009 database, whch s a prvate database collected by the Department of Electrcal Engneerng, Insttute of Technology Sepuluh Nopember Surabaya. It was taken wth great cauton because the mage qualty consderatons. The DB_ITS_2009 database was taken usng an optcal sensor U.are.U 4000B fngerprnt reader wth the specfcatons: 512 dp, USB 2.0, flat fngerprnt, uncompressed. Ths database has 1704 fngerprnt mages of sze pxels. The detals are as follows: The fngerprnt mages are classfed nto three types the fnger condtons (dry, neutral and oly). Each type of fnger condton conssts of 568 fngerprnt mages sourced from 71 dfferent fngers each of these fngerprnt mages was taken eght tmes for the three condtons above. As a result, we obtaned =1074 fngerprnt mages. To obtan dry fngerprnt mages, har-dryer was used to completely LCS 1.39 GCS 1.17 RVTR 6.66 GCF 11.1 Table 4. Dry fngerprnt mage. LCS 1.41 GCS 1.19 RVTR 7.78 GCF 10 Neutral The rule vewer whch dsplays a roadmap of the whole fuzzy nference process, shows the values of nput varables and ther output, and has ablty to change nputs and see output change. Fgures 12, 13, and 14 shows the rule vewer for neutral, oly and dry fngerprnt mage respectvely. Dry Fgure 12. Rule vewer for oly fngerprnt mage.

176 The Internatonal Arab Journal of Informaton Technology, Vol. 13, No. 1A, 2016 Fgure 13. Rule vewer for neutral fngerprnt mage. Fgure 17. Surface vewer for oly fngerprnt mage. 6.

6 176 The Internatonal Arab Journal of Informaton Technology, Vol. 13, No. 1A, 2016 Fgure 13. Rule vewer for neutral fngerprnt mage. Fgure 17. Surface vewer for oly fngerprnt mage. 6. Conclusons In ths paper, we have developed a FIS method to analyse fngerprnt mage qualty usng four extracted features LCS, GCS, RVTR and GCF. The FIS has four nput varables and three output varables wth sxty three rules. The result obtaned usng the proposed FIS were successful. The FIS has the ablty to determne the fngerprnt mage qualty (oly, neutral, or dry) accordng to ther nput features. Fgure 14. Rule vewer for dry fngerprnt mage. The surface vewer presents three dmensonal curve that represent two nput features and the mage qualty type. Fgures 15, 16, and 17 shows the three surface vewer for dry, neutral and oly fngerprnt mage respectvely, whch presents the two mportant features GCS and RVTR wth mage qualty type. Fgure 15. Surface vewer for Dry fngerprnt mage. Fgure 16. Surface vewer for neutral fngerprnt mage. References [1] Deepak B. and Atulkar M., Fuzzy Inference System for Image Processng, Internatonal Journal of Advanced Research n Computer Engneerng and Technology, vol. 2, no. 3, pp , [2] Eun-Kyung Y. and Cho S., Adaptve Fngerprnt Image Enhancement wth Fngerprnt Image Qualty Analyss, Image and Vson Computng., vol. 24, no. 6, pp , [3] Kresmr M., Neumann L., Neumann A., Psk T., and Purgathofer W., Global Contrast Factor-a New Approach to Image Contrast, n Proceedngs of the Frst Eurographcs conference on Computatonal Aesthetcs n Graphcs, Vsualzaton and Imagng, pp , [4] Kukula E., Ellott S., Km H., and Martn C., The Impact of Fngerprnt Force on Image Qualty and the Detecton of Mnutae, n Proceedngs of IEEE Internatonal Conference on Electro/Informaton Technology, Chcago, pp , [5] L Q. and Xe M., Measurng Fngerprnt Image Qualty Usng the Fourer Spectrum, Journal of Electronc Scence and Technology of Chna, vol. 5, no. 3, pp , [6] Mod S. and Ellott S., Impact of Image Qualty on Performance: Comparson of Young and Elderly Fngerprnts, avalable at: o= &rep=rep1&type=pdf, last vsted 2006.

7 Fngerprnt Image Qualty Fuzzy System 177 [7] Rahmat S, Harad M., and MaurdhHery P., Determnng the Standard Value of Acquston Dstorton of Fngerprnt Images Based on Image Qualty, ITB Journal of Informaton and Communcaton Technology, vol. 4, no. 2, pp , [8] Rahmat S., Harad M., and MaurdhHery P., Determnng the Dry Parameter of Fngerprnt Image Usng Clarty Score and Rdge-valley Thckness Rato, IAENG Internatonal Journal of Computer Scence, vol. 38, no. 4, pp , [9] Saenko A., Polte G., and Musalmov V., Image Enhancement and Image Qualty Analyss Usng Fuzzy Logc Technques, n Proceedngs of the 9th Internatonal Conference on Communcatons, Bucharest, pp , [10] Tarun M. and Asthana A., Image Enhancement usng Fuzzy Technque, Internatonal Journal of Research n Engneerng Scence and Technology, vol. 2, no. 2, pp. 1-4, [11] Zheng L., Zh H., and Bo F., A Novel Method for the Fngerprnt Image Qualty Evaluaton, n Proceedngs of Internatonal Conference on Computatonal Intellgence and Software Engneerng, Wuhan, pp. 1-4, Adnan Shaout s a full Professor and a Fulbrght Scholar n the Electrcal and Computer Engneerng Department at the Unversty of Mchgan- Dearborn. At present, he teaches courses n logc desgn, computer archtecture, cloud computng, fuzzy logc and engneerng applcatons and computer engneerng (hardware and software). Hs current research s n applcatons of software engneerng methods, computer archtecture, embedded systems, fuzzy systems, real tme systems and artfcal ntellgence. He has more than 33 years of experence n teachng and conductng research n the electrcal and computer engneerng felds at Syracuse Unversty and the Unversty of Mchgan- Dearborn. He has publshed over 170 papers n topcs related to electrcal and computer engneerng felds. He has obtaned hs BSc, M.S. and PhD n Computer Engneerng from Syracuse Unversty, Syracuse, NY, n 1982, 1983, 1987, respectvely. Abdelwahed Motwakel receved the BSc degree n Computer Scence from Omdurman Islamc Unversty- Sudan n 2003, and the MEng degree n Electrcal Engneerng from the Insttut Teknolog SepuluhNopember (ITS) Surabaya- Indonesa n In 2003, he joned the Department of Computer Scence, Omdurman Islamc Unversty, as a Teachng Assstant, and n 2010 became a Lecturer. Hs current research nterests nclude mage processng and fuzzy logc.

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