Fingerprint Image Quality Classification Based on Feature Extraction

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1 APSIPA ASC X an Fngerprn Image ual Classfcaon Based on Feaure Eracon Xuun Yang, Yang Luo, Shangd Zhang College of Informaon and Communcaon Engneerng, Harbn Engneerng Unvers, Harbn, 5 E-mal: anguun@hrbeu.edu.cn E-mal: luoang@hrbeu.edu.cn E-mal: zhangshangd@hrbeu.edu.cn Absrac Fngerprn recognon echnolog has been del used n crmnal nvesgaon, aendance ssem, secur esng and oher felds and has become one of he mos maure bomerc echnologes. Snce fngerprn mage qual affecs heavl he performance of fngerprn recognon ssem, accurae evaluaon of fngerprn mage qual has grea value n mprovng he performance of auomac fngerprn denfcaon ssem and applcabl of fngerprn recognon algorhms. In hs paper, e manl nvesgae fngerprn mage qual classfcaon approaches based on feaure eracon. We erac s groups of qual feaures ncludng frequenc doman feaures and spaal doman feaures, and respecvel use mehods such as ndvdual qual feaure parameer, lnear eghed sum, avele doman energ, K- means cluserng, Suppor Vecor Machne and BP neural neor o classf fngerprn mages no hree pes of hgh qual, medum qual and lo qual mages. Epermenal resuls ndcae ha classfcaon accurac of he mehod combnng s groups of qual feaure vecor h BP neural neor s hgher han oher mehods. I. ITRODUCTIO Fngerprn recognon echnolog hch s del used n crmnal nvesgaon, aendance ssem, secur esng and oher felds s one of he mos maure bomerc echnologes[]. Fngerprn mage qual drecl affecs he performance of auomac fngerprn denfcaon ssem, and here are man facors hch cause declne n fngerprn mage qual, such as sans, fngerprns damage, degrees of drness or eness. Some of hese facors are rressble, so s mporan o evaluae he capured fngerprns qual for mprovng he performance of auomac fngerprn denfcaon ssem. A presen, researches on fngerprn mage qual are progressng ver qucl, and esng approaches for fngerprn mage qual esmaon can be summarzed no he follong hree caegores.. Mehods based on frequenc doman feaures usuall depend on frequenc doman feaures eraced o analze mage qual. Za[] evaluaed he overall qual of fngerprn mage b compung and analzng Fourer specrum; Hong[3] and Shen[4] used Gabor fler o oban he clar of rdges hch s defned as qual bloc mar;. Mehods based on spaal doman feaures compue a measure of mage qual based on he feaures eraced on he spaal doman. Xe[5] eraced he local feaures from fngerprn mages based on Opmzed Orenaon Ceran Level as qual measuremens; 3. Classfer mehod consders fngerprn mage qual as a classfcaon problem o deal h. Tabass[6] evaluaed fngerprn mage qual based on classfer b compung he qual and separang degree of each mnuae pon eraced from fngerprn mages, and used neural neor for learnng and classfcaon. In hs paper, e eraced s groups of feaures ncludng frequenc doman feaures and spaal doman feaures, and respecvel used mehods such as ndvdual qual feaure, lnear eghed sum, avele doman energ, K-means cluserng, Suppor Vecor Machne and BP neural neor o classf fngerprn mages no hree pes of hgh qual, medum qual and lo qual. II. UALITY FEATURES EXTRACTIO In hs research, a vare of qual feaures are eraced o evaluae fngerprn mage qual. Snce dfferen feaure parameers can reflec fngerprn mage qual n varous aspecs, e respecvel erac o frequenc doman feaures ncludng energ concenraon and Gabor feaure, as ell as four spaal doman feaures ncludng global orenaon change, effecve area, spaal coherence and dreconal conras o evaluae fngerprn mage qual n hs paper. A. Energ Concenraon The o-dmensonal Dscree Fourer Transformaon ( DFT ) of a fngerprn mage I (, j) of sze M s gven b M lj ι π ( + ) I j e M j F(, l) (, ) M Alhough DFT produces a comple-valued oupu, onl he poer specrum P(, l) F(, l) s ofen used as conans mos of he geomerc srucure of an mage[7]. The poer specrum s defned as an annular band h radus from rdges mnmum frequenc o rdges mamum frequenc. The beer fngerprn mage qual s, he more concenraed s energ dsrbuon s. ()

2 We use a se of Buerorh lo-pass flers o erac he energ from poer specrum of fngerprn mages. The Buerorh fler funcon s defned as follo.5.6 m.6 +,,, KT () T H (, l m, n) (3) a l b + (( ) + ( ) ) n n m M (, l) s he pel nde n he poer specrum and ( a, b ) s he locaon of he cener of he poer specrum. The Buerorh funcon generaes a se of lo-pass flers h he cuoff frequenc gven b m and he fler order gven b n. We can ge a oal of T equall spaced band pass flers b ang o consecuve Buerorh funcons, and energ concenraon n he h band s compued b M l R E R (, l) P(, l) (4) Energ concenraon n he h band s normalzed as follos E P T (5) E The enrop of energ concenraon s defned as T E P log P (6) Fnall, energ concenraon score s compued b B. Gabor Feaure log T E (7) A fngerprn mage I (, j ) s dvded no a se of nonoverlappng blocs of sze6 6 and e consruc m Gabor flers hch s descrbed as follos h(,, θ, σ, σ ) θ cosθ + sn θ (8) θ sn θ + cosθ (9) θ θ σ ep[ ( + )] ep( π f θ ) σ, Km here f s sne ave frequenc, θ s he drecon of he () h Gabor fler, σ and σ are sandard devaon of Gaussan envelope along -as and -as. We use hese m Gabor flers o fler each sub-bloc, and he Gabor feaure of samplng pon ( X, Y ) s gven b π ( ) θ () m g( X, Y, θ, f, σ, σ ) I ( X +, Y + ) h( X, Y, θ, f, σ, σ ) (), Km So he sandard devaon of each sub-bloc Gabor feaure s defned as follos G m ( g g θ ) m θ, g m θ g θ (3) m If he sandard devaon of one sub-bloc Gabor feaure s loer han he hreshold Tq hch s obaned from he epermens, hs sub-bloc s mared as lo qual bloc, oherse s mared as hgh qual bloc. So e can ge he number of hgh qual blocs good and he number of lo qual blocs poor, and he score of a fngerprn mage based on Gabor feaure s gven b good (4) + good C. Global Orenaon Change poor I s obvous ha here are smooh changes n he orenaon of hgh qual fngerprn mage and abrup changes n he orenaon of lo qual fngerprn mage[8]. Accordng o hs characersc, e use global orenaon change o evaluae fngerprn mage qual. Frsl, a fngerprn mage I s dvded no a oal of nonoverlappng blocs of sze 6 6, and hen e calcuae he drecon of each sub-bloc as follos + j+ V (. j) ( u, v) ( u, v) (5) u v j + j+ u v j V (, j) ( ( u, v) ( u, v)) (6) V (, j) θ (, j) an V (, j) (7) ( u, v ) and ( u, v) are graden of pel hn sub-bloc (, j ) along he horzonal and vercal drecon respecvel and θ (, j) s he drecon of sub-bloc (, j ). Usng hs mehod, e can oban he drecon of each sub-bloc. We can compue orenaon change of neghborhood of sze 3 3 cenered a sub-bloc (, j ) as follos θ (, j) θ (, j) θ ( + m, j+ n) (8) m n So orenaon change of he hole fngerprn mage s defned b

3 D. Effecve Area 3 θ (9) A fngerprn mage I (, j ) s dvded no non-overlappng blocs of sze 9 9, and hen e can calculae average gra value Mean of he hole mage and average gra value Mean( u, v ) of sub-bloc ( u, v ) as follos j M Mean I(,j) M* () ( u ) + ( v ) + Mean( u, v) I (, j) ( u ) j ( v ) () We can deermne heher one sub-bloc s bloc or no accordng o he follong rules B( u, v), f Mean( u, v) Mean () B( u, v), f Mean( u, v) < Mean B( u, v ) suggess sub-bloc ( u, v ) s a bloc, oherse suggess sub-bloc ( u, v ) s a bacground bloc. In order o reduce he probabl of msclassfcaon, e use he follong correcve mehod[9]. u+ v+ B( u, v) f B( m, n) 6 m u n v (3) u+ v+ B( u, v) f B( m, n) < 6 m u n v B above mehod, e could deermne accurael heher one sub-bloc s or no, and compue he number of blocs bacground blocs defned b 4 bacground E. Spaal Coherence and he number of. Fnall, effecve area s + bacground (4) A fngerprn mage I (, j ) s dvded no a se of nonoverlappng blocs of sze 9 9. The graden of he gra level T s s s nens a he pel n bloc B s defned as g ( g, g ). The covarance mar of he graden vecors for all pels n hs bloc s gven b T j j J gsgs s B j j (5) The egenvalues of he above mar are gven b λ ( race ( J ) race ( J ) 4de( J )) (6) λ ( race ( J ) race ( J ) 4de( J )) (7) race( J ) j + j, de( J ) j j j. The normalzed coherence measure s gven b λ j j + j + λ j + j ( ) ( ) 4 % λ ( λ ) ( ) (8) Once e oban he spaal coherence of each bloc n hs a, he spaal coherence score can be defned as follos 5 r r %, l l ω ep q r s he number of blocs, and egh for he h bloc. c (9) s he relave F. Dreconal Conras The rdges of hgh qual fngerprn mage are clear and have hgher conras. Whereas, he rdges of lo qual fngerprn mage are ndsnc and have loer conras[]. Based on hs characersc, dreconal conras s appled o evaluae fngerprn mage qual. A fngerprn mage I (, j) s dvded no a oal of nonoverlappng blocs of sze 8 8. We process all pels n each bloc h egh drecons fler.usng egh drecons fler, e can compue he gra-scale sum value n each drecon of he 5 5 neghborhood cenered a each pel n bloc B hch s descrbed as follos S (, ) I ( P ),,, L8 (3) j afer sldng he fler over bloc B, he egh drecons grascale value sum of bloc B can be obaned b 8 8 j θ S (, ),,, L 8 (3) and he dreconal conras of bloc B s compued b here θma ' D θma θ,,l (3) s he mamum gra-scale value sum n egh drecons of bloc B and he drecon of ' θ s n a drecon perpendcular o he drecon of θ ma. So e can compue he drecon conras of each bloc n hs a and he drecon conras score s defned as 6 D C (33) These s eraced qual feaure parameers are hen normalzed o values beeen zero and one so ha s easer o deal h n subsequen procedures. III. THE SIMULATIO RESULTS AD AALYSIS We chose hree hundred and fve capured fngerprn mages o se up fngerprn daabase, hch consss of one hundred hgh qual mages, one hundred and ffeen medum qual mages and nne lo qual mages. The qual caegor of hese fngerprn mages are manl depended on he human s subjecve judgmen. Dfferen qual fngerprn mages are shon n Fg..

4 (a) hgh qual mage (b) medum qual mage (c) lo qual mage Fg. Dfferen qual fngerprn mages. The epermenal seps are nroduced as follos. We eraced s groups of qual feaures ncludng energ concenraon, Gabor feaure, global orenaon change, effecve area, spaal coherence and dreconal conras from hree hundred and fve fngerprn mages, from hch e could oban feaure mar conssng of hese s groups of qual feaures;. For each group of qual feaures, e calculaed classfcaon accurac on fngerprn mages of dfferen qual caegores and oal classfcaon accurac based on mnmum error creron; 3. A se of lnear eghed sum from hese s groups of qual feaures can be descrbed as follos sum 6 (34) and hen calculae classfcaon accurac of dfferen qual fngerprn mages and oal classfcaon accurac based on mnmum error creron; 4. We se up gaherng cener of s qual feaures and used he feaure mar as npu of K-means cluserng mehod. Then e could oban classfcaon accurac of dfferen qual fngerprn mages and oal classfcaon accurac; 5. We seleced ff hgh qual fngerprn mages, s medum qual fngerprn mages and for and fve lo qual fngerprn mages as svm ranng se, and remanng mages ere used as svm esng se. Besdes, e used s groups of feaures eraced from hese mages as svm npu and used qual caegores of hese mages as sample labels, and hen raned svm classfer h he ranng se. Fnall, esng se as used on he raned svm n order o oban classfcaon accurac of dfferen qual fngerprn mages and oal classfcaon accurac; 6. We seleced ff hgh qual fngerprn mages, s medum qual fngerprn mages and for and fve lo qual fngerprn mages as BP neural neor ranng se, and remanng mages ere used as BP neural neor esng se. Besdes, e used s groups of feaures eraced from hese mages and qual caegor as BP neural neor npu, and hen raned BP neural neor h he ranng se. Fnall, esng se as used on he raned BP neural neor n order o oban classfcaon accurac of dfferen qual fngerprn mages and oal classfcaon accurac. 7. Each fngerprn mage as decomposed no one lo frequenc subband and egheen hgh frequenc subbands usng avele ransform. We compue he cumulave oal energ n he subbands -8 as follos CTE energ (35) And e could ge he overall qual of he mage as follos CTE8 CTE4 ual (36) CTE We could respecvel ge he qual dsrbuon maps of energ concenraon, Gabor feaure, global orenaon change, effecve area, spaal coherence, dreconal conras, lnear eghed sum and avele doman energ as shon n Fg.. (a) (c) (e) (g) Fg. The qual dsrbuon maps of s ndvdual qual feaures, lnear eghed sum and avele doman energ: (a) he qual dsrbuon map of energ concenraon; (b) he qual dsrbuon map of Gabor feaure; (c) he qual dsrbuon map of global orenaon change; (d) he qual dsrbuon map of effecve area; (e) he qual dsrbuon map of spaal coherence; (e) he qual dsrbuon map of dreconal conras; (g) he qual dsrbuon map of lnear eghed sum; (h) he qual dsrbuon map of avele doman energ. 8 (b) (d) (f) (h)

5 The resuls n able I are classfcaon accurac on fngerprn mages of dfferen qual caegores and oal classfcaon accurac h dfferen mehods, such as ndvdual qual feaure parameer, lnear eghed sum, avele doman energ(wdg), K-means cluserng, Suppor Vecor Machne and BP neural neor. TABLE I CLASSIFICATIO ACCURACY O FIGERPRIT IMAGES OF DIFFERET UALITY CATEGORIES AD TOTAL CLASSIFICATIO ACCURACY WITH DIFFERET METHODS Mehod Hgh Medum Lo Toal sum WDG K-means SVM BP 85.86% 86.87% % 77.53% 9.9% 9.93% 76.77% 9.% 94.7% 93.88% 59.65% 8.46% 6.8% 87.7% 6.4% 57.% 86.84% 55.6% 85.% 7.8% 96.49% 4.% 8.% 6.% 8.89% 56.67% 6.67% 85.56% 4.% 97.96% 95.36% 97.78% 6.7% 83.7% 67.33% 56.44% 65.68% 59.4% 88.45% 58.% 9.9% 9.6% 96.3% Comparng h ndvdual qual feaure classfcaon mehod, qual classfcaon accurac of mehods based on mul-feaures s hgher. Ths s because he facors causng dfferences of fngerprn mage qual are so man, e ndvdual qual feaure could onl reflec fngerprn qual n one a and mul-feaures could reflce qual n a de range. Wavele doman energ s rough for assessng he qual of fngerprn mage, and he mehod maes some msclassfcaons on fngerprn qual caegores comparng h classfcaon hreshold hch s deermned based on mnmum error decson rule. Comparng h lnear eghed sum mehod, qual classfcaon accurac of K-means cluserng, Suppor Vecor Machne and BP neural neor mehod s hgher. I s because he nheren relaonshp beeen mul-feaures and fngerprn mage qual s no a lnear correlaon, and hese hree mehods could buld a beer nonlnear relaonshp h fngerprn qual. ual classfcaon accurac of he mehod combnng mulfeaures and BP neural neor reaches 96.3%, hch s he hghes among hese hree mehods IV. COCLUSIOS In hs paper, e manl nvesgae fngerprn mage qual classfcaon mehods based on feaure eracon algorhms. We erac s groups of qual feaures ncludng a vare of frequenc doman feaures and spaal doman feaures, and respecvel use mehods such as ndvdual qual feaure, lnear eghed sum, avele doman energ, K-means cluserng, Suppor Vecor Machne and BP neural neor o classf fngerprn mages no hree pes of hgh qual, medum qual and lo qual. Epermenal resuls demonsrae ha qual classfcaon accurac of he approaches based on mul-feaures s hgher han ndvdual qual feaure and avele doman energ classfcaon mehod, and he qual classfcaon accurac of K-means cluserng, Suppor Vecor Machne and BP neural neor s hgher n comparson h lnear eghed sum mehod. Furher epermenal nvesgaons are needed o esablsh drec correlaons beeen he effecveness of fngerprn mage qual esmaon algorhm and he performance of fngerprn denfcaon ssem. ACKOWLEDGMET Ths paper s funded b he aonal Scence Foundaon of Helongjang Provnce under Gran REFERECES [] A. Jan, Ross, and S. Panan, Bomercs: A ool for nformaon secur, IEEE Trans. Inf. Forenscs Secur, vol., pp.5-43, Jun 6. [] Saqub. Za, Son, and Sanosh Kumar, Herarchcal fngerprn qual esmaon scheme, Inernaonal Conference on Compuer Desgn and Applcaons, vol., pp [3] Hong L, Wan. Y, and A. Jan, Fngerprn enhancemen algorhms and performance evaluaon, IEEE Trans. Paern Anal. Mach. Inell, vol., pp , Augus 998. [4] L. Shen, A. Ko, and W. Koo, ual measures of fngerprn mages, n Proc. Audo Vdeo-Based Person Auhencaon, pp. 66-7,. [5] Xe Shan Juan,Yoon Soo, and Yang Ju Cheng, Rule-based fngerprn qual esmaon ssem usng he opmal orenaon ceran level approach, Proceedngs of he 9 nd Inernaonal Conference on Bomedcal Engneerng and Informacs, pp [6] E. Tabass, and C. Wlson, A ovel Approach o Fngerprn Image ual, Inl. Conf. Image Processng, vol., pp. 37-4, 5. [7] Chen Y, Dass Sara, and C. Jan Anl, Fngerprn qual ndces for predcng auhencaon performance, Lecure oes n Compuer Scence, vol. 35, pp. 6-7, 5. [8] E. Lm, X. Jang, and W. Yau, Fngerprn qual and vald analss, n Proc. In. Conf. Images Process, pp ,. [9] Ca ume, Fan julun, and Gao nbo. Improved algorhm for fngerprn segmenaon based on varance and s graden, Compuer Engneerng and Applcaons, vol. 46, pp ,. [] Ballan Melem, Saara and F.Ahan, Fngerprn classfcaon echnque usng dreconal mages, Conference Record of he Aslomar Conference on Sgnals, Ssems and Compuers, vol., pp. -4, 998.