sweat pores-based (level 3) novel fingerprint quality estimation

8/2/2019 Sweat Pores-Based (Level 3) Novel Fingerprint Quality Estimation

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Sweat Pores-based (Level 3) Novel Fingerprint Quality Estimation

Z Squb, Stsh Kum S

Biometics Division,C-DC MumaiMumai, Inia

[email protected], [email protected]

Poor-quality images mostly result in spurious ormissing features, which further degrade the overallperformance of the recognition systems. This work augmentsthe ngerprint quality with respect to one of the level 3 micro

features, i.e., sweat pores. The paper proposes sweat poresbased an eective ngerprint quality estimation scheme, whichis a multi-factor quality assessment model comprises vequality measures estimating their respective quality scores.The scores are then fused into a single quality score by a fusionengine based on the 'Weighted Sum rule. The proposedscheme is experimented with publicly available ngerprintdatasets, which were scanned with Cross Match Verier 300scanner at 500 dpi (pores are quite visible in these samples, asof now, no 1000ppi datasets are publicly available). Theexperimental results show that this arrangement couldcorrectly estimate and assign grades (good/acceptable/poor) tonearly 93.33% images in the dataset. This paper also presents anovel technique for identifying the strong pores for highreproducibility. The corresponding result is shown for the

same set of images and is found as 91.93%. This method is alsobeing tried with higher resolution images.

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I. NRODUCION

ltoug te peroance o ngerprint recotnsstems as greatl improve, it is still inuence manactors. mong tese, ngerint image qualit ic is ameasre o te caracteristics o rige-valle tetre, as

a te greatest impact on matcing peroance. oorqualit images mostl result in sprious or missing eatres,

ic rter egrae te overall peroance o te

recognition sstems [1].or man application sstems, it is muc preerre to

replace lo-qualit images it teir net etter qualitimages or iger sstem perormance. Knoing tengerint qualit in avance proves usel toarsimproving te perormance o ngerprint recognitionsstems.

Tere ave een man researces to evelop appropriatemeasures to estimate te ngerint image qualit at Level 1macro an Level 2 eates, ut not muc on level 3 microeatures. Level 3 eatures seat pores, rige contors,egeoscopic eatres e prove to car signicant

8----//$ IEEE

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Rkh Vg

Department o Electronics & TelecoicationsMTME, IM Mumai,

Mumai, Iniarea. [email protected]

iscriminator inormation [12][3]. o, tis or primarilocuses on qualit assessment o ngerints om Level 3micro eates seat pores vie-point, ic can ten ecomine it teir Level an Level 2 scemes or a

roust qualit estimation. Tis novel sceme proposes vequalit inices, ere te or in tanem to assess anclassi te ngerint images into one o tree classesGoo qualit 2, cceptale qualit 1 an oor qualit0.

Rest o te paper comprises or sections. In section 2,e propose sstem is eplaine in etail along it teirqualit measures, novel tecnique or ientiing song

pores or etter reprouciilit an matcing accrac. Tesection conclues it te sion tecnique to erive a singlenal qualit ine to eclare te crrent sample asgooacceptalepoor. In ection 3, e iscuss teeperimental results. Conclusion is an in section 4.

II. ROPOSD FINGRPRIN QUIY SIMIONSYSM

Te unerling pilosop is sstems multiimensionalapproac o analing an estimating qualit inices aseon seat pores availale on te ngerint images. Tevarious metos in tis aangement inspect te sujectom ierent angles, ile complementing eac oter anminimiing teir iniviual eanesses, tus reinorcing tesstems strengt.

Te process starts it te eective area an region ointerest ROI etermination at macro level. I te estimatescore remains elo te specie tresol, te processens an te image is eclare oor ue to insucientoregro area, or else, control is alloe to enter temicro level or qualit analsis it respect to seat pores.Tis level ouses a set o our algoritms it pre-assignemanuall eigts as per teir iniviual peroancesagainst te atasets scanne it Cross Matc Verier 300scanner at 500 pi [11] pores are quite visile in tesesamples, as o no, no 1000ppi atasets are puliclavailale, ere te or on level 3 micro eatres seatpores. t te en, all te elemental qualit scores are setogeter as per te eigts assigne to eac su-sstem asper teir peroances using te sion engine, reer to ig.


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14, ic is ten tresole an te nal ecision is taen te sstem as eiter Goo, cceptale or oor.

In a ngerint Image, te seat pores eist on te riges.Beore escriing te pore etaction meto, it is necessar to no at a tpical pore loos lie. tpical averagesie pore in 500ppi ngerint ma ave gra levels asson in ig. 1. It as ig ite to ligt gra intensitvalue at te cente an lo gra to lac intensit aroun

te cente piel. en te image is inverte, te pores alsoget inverte, ic can e ten anale using te poremoel as presente in [5], as son in ig. 3.

A pical ore An nveed oe

Fige . typical Pore (in 3x3 neighborhood) and its inersion

Te majorit o pores itin a 500 pi image can eapproimate using a sligtl moie 2-imensionalGaussian nction as son in equation 1 [5]

1

Te plot o te ngerint pore moel an te resulting3 3 lter are son in ige 2 an ige 3.

07569 06321 07569

0632 0 06321

07569 06321 07569

Figure 2. he 3 x 3 pore mask

r :[i 1 O 1: 1

Figure 3. 2-D Gaussian keel as a pore model

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error map is generate ic is given equation 2[5]

. !) =.brl: - ((j) !fi % 1, } + : 2 ere, i, j is te normalie value o te gra piel

etween 0 an 1. Here, te loer te value o E, inequation 2, te stonger te pore remains. In oter ors,

tere remains a ig proailit o 33 region itminimm value to e a pore. Hence a lo alue o errormeans more cances o eistence o pore.

A. Eective area oreground) estimation and region ofinterest OI) deteination

oregroun area is ver important to evaluate esiesnoise, ness, eess, scarsruises etc. te qualit o tengerprint images. It is icult to pic up te etails o teimages i te oregroun aea is too small. goo qualit

image soul ave enoug oregroun area. o, e initiate te process it te oregroun aea calculation, ic isene as te percentage o te oregro locs.

Te eective oregroun area is te ratio ooregroun aea as percentage o total area

(3)

smaller value o oul mean a smaller area ongerint as een capte. or a given optimum tresolvalue T, i < T te ngerprint image qualit iseclare "not goo enoug, ic nees to e recapte,

reer to ig. 20.e propose a ROI-ase qualit estimation sceme,

ic comprises to pases ROI etermination an qualitestimation o te area itin ROI.

The analysis and evaluation process begins with thedivision of image under I into a set of disjoint blocks (w xw). The standard deviation ofthe gray scale values ofthe pixelsin the k block (stdIJ is then calculated using the equation (4[2]. or a given optimum tresol value T, i stI > T,

te loc is eclare "oregroun loc or else"acgroun loc, reer to ig. 5 onl ROI portion.

. Quadrant analysis (QA) and quali index (QI)

estimation

4

Tis novel approac is toars te projection ananalsis o pores istriution in o quaants. or angoo qualit print, te pores are generall o evenlistiute in all quaants, ic is not te case oterwise.Empiricall etermine optimum tresols in terms o

percentage o pores are specie an qualit ine isestimate ecling te image as gooacceptalepoor, reer

to ig. 4, 5 & 6.


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Figure 4. Sample ngerprint: 012_3_l.tif[l(Cross Math Verier 300 sanner at 500 dpi)

ores are ientie an mare it re ules, as sonig. 5. ot all te pores coul e mare, as e nee tocange te value o 'r, which is the sae from theee othe pore model to he edge as well as the masksize (from 3x3 to 55 for r=2 a for r=3), butotherwise most of them ae oae.

) g llEil d Inst !oo

EHj . :\ ! D . Irs dttd t 1 rd

Figure 5. Pores Detetion: 012_3_l.tif

To compute an plot te o quarants or quarantanalsis, te pore locations itin te ROI are ientiean plotte using square eor meto as given in equation2. Te centoi o te oregroun region itin te ROIis compute an ten te quarants ae accuatel anaaptivel gure out. s can e evient rom ig. 16, 17,21 an 22, or egrae qualit prints, te pores istriutionis selom uniorm in our logical regions.

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) g [r fd : Ier Qkp W 1

t Q ( \ / rs strutn n u udrnts

5 1 15 2 25 3 35 4 45 5

Pores Count

Figure 6. Quaant nalysis: 012_3_l.tif(slightly enlarged for a ler view)

Tis is a simple et eective a o assessing te imagequalit om pores vie-point, reers to te total ot oientie pores in a given image, as so in ig. 7.

To N o Po [

Figure 7. Pores Count: 012_3_l.tif

Empiricall etermine optimum tresols in terms onumer o pores are specie an qualit ine is estimateecling te image as gooacceptalepoor.

Pores Contrast (Michelson-based

This approach is all towards computing the pore-ridgecontrast which is based on Michelson contrast method [9].

Local conast is computed using equation (5) for all the pores(and only the pores) in their local neighborhoods (here, 3x3),and a pore contrast image is generated which is used rter toclassi pores into Strong/Averageeak classes. Song pores

have been fod exhibiting better reproducibility as discussedin experimental results.

Te image contrast is ene as relative local ierencesin piel intensities. Te enition is

= 5

where L is the maximum gray level and L is theminim gray level in the image.


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ile computing, deed uo her reseve eses ad reshod vaues, ores are aeorzed as:'So, 'Averae ad 'Weak Pores, ad are sored searaey.S ores are haraerzed by hh esy vaues, averaead weak oes wh esser vaues, refer o F. ad 9. Asevde o he es, we have ve brh xes (whe,

brh yeow e.) refeed o as So ores (wh her owoaos/des). Sary, averae d weak ores aredeed by reavey dker shades, reer to ig. 16, 17, 18 n19 for addtona snashots.

oooO d ! : / . [

Figure 8. Pores ontast image: 012_3_.tif

I O tHI 0 : '_; 0 [

Figure 9. Magnied view of the pores ontrast image': 012_3_.tif

E. Pores Strength

As dsussed se. II (reabe) ad show F. ad 2. te loer te vlue o E,, te stronger te pore is. In

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oter ors, tere is ig proilit o tt 33 region toe pore. Hence lo vlue o error mens more cnceso eistence o pore.

ince inormtion is relte to proilit in similrmnner i.e. lo proilit mens more inormtion, erroralue cn gie us inormtion on eistence o pore. Toclculte te eistence n strengt o pore e rst

normlie te eor value it its mimum possile lue.Its maimum lue ill e generte en te pore salues as son in ig. 1O

1 1 1

Figure 10. The 3 x 3 pore mask

Tis genertes a E, = 3.88979208.

EN , = E, EJ, 6

ere, EN , is te normlie eor mp it te aluesin te rnge 0 an 1. o, te sengt o pores cn eclculte s

= EN ,*logEN , umer o ores 7

over te entire image. iger vlue means tere is morepossiilit o eistence o pores, n te pores strengt ismore.

' 96Figure . Pores Strength: 012_3_. tif

Tis is o te imge is nle rom pores ililit point o ie. lso, troug little strict tresoling, em ense constelltion o pores eing relatiel morestrong pores, ic m ensure etter reprouciilit n

tus a iger pores mtcing ccc. s r s qulitine is concee, epening upon te pores strengt lue

n te corresponing tresols, current imge cn elele s goo 2cceptle poor 0.

Fusion Engine

Te sion engine comines te iniiul qulit scoresom ierent su-sstems n genertes te nl qulitscore, ic re ten use to ajuge te qulit o teimge, reer to ig. 14 n 15.

Fusion rule Te sion rules opte ere or cominingte ille t are te eigte um Rule [7][8].

Weightedsum rle In eigte sum rule Ong et .,2003; ga et ., 2006, a eigte ctor is multiplie


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it te score an en sum is calculate as gien inequation 8.

8

Eac sumoule is assigne some eigt, W suc atTe sumoule eigts are calculate trougempirical calculations, ere eigts are irectl

proportional to teir respectie su-sstems accac.

EXPRMNTAL SULTS

ie qualit meases ae een selecte animplemente in MTLB. Te are teste on imagesscane it Cross Matc Verier 300 scner at 500 pipores are quite isile in tese samples, as o no, no1000ppi atasets are pulicl aailale.

e ae teste tis moule eigte um oer 15images. e ae presente eperimental results toemonstrate te perormance o te propose multialgoritm sceme, reer to ig. 12 an 13. rom te ataset,

e ae manuall classie te images in tree classes i.e.goo, acceptale an poor ase on teir properties. Te

result ig. 12 sos tat o meto c istinguis teimages as per teir qualit atiutes it respect to leel 3seat pores. Te accac rate osere is 9 3. 3 3%.

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sweat pores-based (level 3) novel fingerprint quality estimation

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