Authentication Management for Information System Security Based on Iris Recognition

Transcription

1 Journal of Advanced Managemen Scence, Vol 1, No 1, March 2013 Auhencaon Managemen for Informaon Sysem Secury Based on Irs Recognon Yao-Hong Tsa Deparmen of Informaon Managemen, Hsuan Chung Unversy, Hsnchu Cy, Tawan Emal: Absrac Ths paper presens an negraed secury managemen for auhencaon of users based on weghed rs recognon echnology For he proposed sysem, a moble devce cooperang wh a user calbraon nerface s used o capure he rs mage Wh large varaon n he envronmen, here are wo man mprovemens o develop he sysem For calbraon on lne, we use a hree-pon localzaon scheme for exracng approprae eye regon accordng o he nformaon of eye corners and he cener of eyebrow Furhermore, sasc based llumnaon normalzaon s used n preprocessng o decrease llumnaon nfluence under varan lghng condons The deecon of he rs mage s based on Adaboos algorhm and local bnary paern (LBP) hsogram s hen appled o exure classfcaon Expermen showed ha he proposed sysem provded users a more flexble and feasble way o nerac wh he verfcaon sysem hrough moble devce The onlne auhencaon process for rs recognon can provde more proecon for nformaon sysem secury Index Terms auhencaon, secury conrol, rs recognon, adaboos, local bnary paern I INTRODUCTION For nformaon sysems (IS) ha are used o creae, sore, process or dsrbue nformaon mus be properly managed o proec agans unauhorzed access of classfed nformaon and o ensure he avalably of he sysem As he complexy of a specfc IS ncrease, he need for auhencaon of users and process becomes more and more sgnfcan Idenfcaon and auhencaon conrols are requred o ensure ha users have he approprae clearances and need-o-know for he nformaon on a parcular sysem [1] Sysems wh bomerc secury echnology are becomng preferable o radonal mehods such as ID cards, passwords or PIN (personal denfcaon numbers) ha can be easly dvulged o unauhorzed users or solen by mposors [2] and [3] A relable auomac recognon of users based on bomerc feaures has long been an aracve goal The bomercs can provde a naural and convenen means for ndvdual denfcaon by examnng he physcal or behavoral ras of human bengs [4] As n all paern recognon problems, he key 2012 Manuscrp receved November 13, 2012; revsed December 29, ssue s he relaon beween nerclass and nra-class varably: obecs can be relably classfed only f he varably among dfferen nsances of a gven class s less han he varably beween dfferen classes Among bomerc sysems, rs recognon s he mos sable and relable sysem snce uses he unque and mmuable paerns of human rs such as archng lgamens, conracon furrows, rdges, cryps, coronas, freckles, colors, and rfs Also, unlke fngerprn recognon, rs recognon has he advanage ha mage acquson can be more easly acheved wh nonconac daa acquson [5] All hese facors conrbue o hgh effecveness n he currenly deployed rs-recognon sysems Ther ypcal scenaros are: subecs sop and sare relavely close o he acquson devce whle her eyes are llumnaed by a near-nfrared lgh source, enablng he acquson of hgh-qualy daa In he pas years, grea advances have been made n consraned envronmens where he users are closely cooperave The sae of he ar rs recognon echnques are revewed by [6] However, consrans are a maor obsacle for he mass mplemenaon of rs-based bomerc sysems, especally n he handheld devce lke cell phone or pad Recen research neres n he feld has focused on recognon n less consraned magng condons Several facors make rs mages non-deal, such as a-adsance magery, on-he-move subecs, and hgh dynamc lghng varaons In such crcumsances he rs mages capured may be degraded due o off-axs magng, mage blurrng, llumnaon varaons, occluson, specular hghlghs and nose [7] Robus rs recognon n such degraded mages pose a grand challenge Daugman [8] repored some advances ncludng accurae rs boundares localzaon wh acve conour and mage regsraon hrough Fourer-based rgonomery, among ohers Sun and Tan [9] presened a general framework for rs feaure represenaon based on ordnal measure L and Ma [10] nroduced a robus algorhm based on he random sample consensus for localzaon of non-crcular rs boundares and an mage regsraon mehod In hs paper, an negraed secury managemen for denfcaon and auhencaon of users based on weghed rs recognon s presened A moble devce s used as he user nerface The proposed sysem ncludes a hree-pon localzaon scheme for exracng 2013 Engneerng and Technology Publshng do: /oams

2 Journal of Advanced Managemen Scence, Vol 1, No 1, March 2013 approprae eye regon accordng o he nformaon of eye corners and he cener of eyebrow Furhermore, sasc based llumnaon normalzaon s used n preprocessng o decrease llumnaon nfluence under varan lghng condons The deecon of he rs mage s based on Adaboos algorhm and weghed local bnary paern (LBP) hsogram s hen appled o rs recognon II USER INTERFACE AND PREPROCESSING In hs secon, he user nerface desgn s frs descrbed hen some preprocessng seps are nroduced These preprocessng seps are all performed on he server ncludng he rs deecor, llumnaon normalzaon Afer hese preprocessng, more sable feaures are obaned and hey are used o be he npu of he rs verfcaon sep A User Inerface for Calbraon Onlne and Eye Deecon The local roaons, affne ransformaons and dsorons of he rs mage are common phenomenon Therefore, he calbraon for he camera wh he sysem s used o mprove he nvarably In he scenaro, he moble devce ncludng he bul-n camera s smply hold by he hand of he user I s mpossble o have calbraon for he camera and he sysem n advance There are hree basc camera operaons: camera pan, l, and roll ha wll nfluence he performance of rs verfcaon heavly To reduce he verfcaon errors ha usually caused by he basc camera operaons, a hreepons eye localzaon scheme s developed n he lm of he compung power of he moble devce The corners of eye and eyebrow n he eye mage are se o be he hree pons of he eye localzaon scheme Three pons mus be fed by user manually The dsance beween each par of wo pons solves he problems of camera pan and l The poson of pons solves he problems of camera roll In he desgn, user sees hs eye on he screen of he handheld devce and res o f each cross o s correspondng feaure pons as shown n Fg 1 From he user nerface developed on a cell phone, users should npu user denfcaon number and choose o verfy or enroll The rough rs mage s hen capured from he camera of he cell phone hrough he user nerface for calbraon onlne and eye deecon Three cross pons are drawn on he screen of he handheld devce by wo dfferen colors o represen fng posons for eye corner pars (red) and eyebrow (green) The user wll frs be asked o adus hs eye's poson o f wh he wo red crosses on he screen Afer hen, he user mus ceners hs eyebrow o he green cross for l angle adusmen Alhough he precson of he fng process above may no be well conrolled, does provde a rough regon of he eye mage o be he npu from he moble devce o he conrol server I s noed ha does no need o ransm he whole mage snce he rough rs mage s already obaned B Illumnaon Normalzaon I s well known ha he mage gray level (or mage color) s very sensve o he lghng varaon Consderably dfferen mages may be capured from he same obec under dfferen llumnaons Psychophyscal expermens show ha he human vsual sysem s dffcul o denfy he mages of he same obec ha are due o consderable changes n llumnaon [11] For rs recognon sysem, s also dffcul o produce good classfcaon accuracy f mage samples n he ranng and esng ses are aken from dfferen lghng condons The general purpose of llumnaon normalzaon s o decrease lghng effec when he observed mages are capured n dfferen envronmen A common dea s ryng o adus observed mages o approxmae he one capured under a sandard lghng condon Mos of he pas works red o defne he sandard lghng condon n sascally and modfy he observed mages o mach hese sasc properes In he proposed sysem, we exrac he sascal hsogram feaure of sandard lghng condon from he ranng mages n he daabase Each esng mage wll be adused based on he exraced hsogram nformaon of he sandard lghng condon Fg 2 shows he flow char of he llumnaon normalzaon algorhm Sandard lghng condon Tes mage Sasc analyss Nonlnear lghng adusmen Tes mage Fgure 2 The flow char of he llumnaon normalzaon For example, f R s he observed mage and h s he hsogram feaure of sandard lghng condon we ge, he modfcaon sraegy [12] we used s o ransform he sasc hsogram of regon R o he hsogram h so ha he ransformed resul has smlar sasc hsogram o h The ransform s he one o one funcon T whch can be expressed as follow equaon Fgure 1 The user nerface for calbraon onlne 1 T G H, (1) 92

3 Journal of Advanced Managemen Scence, Vol 1, No 1, March 2013 where G s he emprcal cdf of h and H s he emprcal cdf of regon R f we rea he nensy hsogram a he regon R as probably densy funcon Each pxel n he regon R s normalzed by usng he ransfer funcon T Le R be he normalzed regon, hen R 1 ( T( R( x ) G H( R( ), (2) Ideally, R wll have smlar hsogram dsrbuon o he one under sandard lghng condon has III IRIS DETECTION AND RECOGNITION A Irs Deecon and Boundary Exracon The AdaBoos was frs proposed by Freund and Schapre [13] I can auomacally selec some weak classfers from he weak classfer space o consruc a srong classfer hrough he weghed negraon of seleced weak classfers Afer ha, auhors proposed new AdaBoos us lke Schapre and Snger [14], and J Fredman, T Hase, and R Tbshran [15] They was used exensvely on face deecon [16] and [17] Irs has very fne exures compared wh he oher area of he eye mage such ha he AdaBoos has he ably o deec he rs and exrac he exac boundary The proposed mehod s based on he Adaboos algorhm of Vola and Jones [16] The mehod brngs ogeher new algorhms o consruc a framework for robus and exremely rapd vsual deecon Ths sysem acheves hgh frame raes workng only wh he nformaon presen n a sngle grey scale mage Ths s very useful o deec rs mage on moble devce n he proposed sysem Frs, an negral mage ha allows for very fas feaure evaluaon s used The summed area able (SAT) s shown n Fg 3 The formula s lsed n he followng SAT ( ' ' x y y ' ' I( x, y ), SAT ( SAT ( y 1) SAT ( x 1, I( SAT ( x 1, y 1), SAT ( 1, SAT ( 1) 0, where x and y represen he coordnaes n he npu mage (3) process mus exclude a large maory of he avalable feaures, and focus on a small se of crcal feaures As a resul each sage of he boosng process, whch selecs a new weak classfer, can be vewed as a feaure selecon process AdaBoos provdes an effecve learnng algorhm and srong bounds on generalzaon performance The algorhm s lsed n he followng Algorhm Gven he mage feaures ( x 1, y1),,( x n, yn), each y 0,1 ndcaes he negave and posve paern Inally he weghng s se by w 1 1 1,, 2m 2 l and he amouns of he negave and posve paerns are expressed by m and l for loop 1,, T : Normalzaon he weghng, w, w, n w Calculae he weghng o selec weak classfer mn f, p, w hx, f, p, y Defned h x hx f, p,, are mnmum of he, when he f, p and The way o updae he weghng s w w 1e 1,, when he x s correc, e 0, oherwse s e 1, hen 1 A las, he decson of he srong classfer s obaned T T 1 by 1 h x Cx 0 oherwse where 1 log The deeced resuls are shown n Fg 4 and Fg 5(a),, Fgure 3 The summed area able The second one s a smple and effcen classfer ha s bul by selecng a small number of mporan feaures from a huge lbrary of poenal feaures usng AdaBoos Whn any mage sub-wndow he oal number of Haarlke feaures s very large, far larger han he number of pxels In order o ensure fas classfcaon, he learnng Fgure 4 The npu mage and he ransformed gray scale mage Sharpenng and morphologcal operaons [18] are used o enhance he eyes on a face A 33 mask s used here for mage sharpenng Suppose ( s he cener of he mask, where x and y are coordnaes w 5 denoes he 93

4 Journal of Advanced Managemen Scence, Vol 1, No 1, March 2013 coeffcen of he mask for poson ( and w 1, w 2, w 3, w 4, w 6, w 7, w 8, and w 9 are he correspondng neghborng coeffcen n he mask The resul afer morphologcal operaons resuls are shown n Fg 5(b) T[ y] w f ( x 1, y 1) w f ( x 1, 1 2 w f ( x 1, y 1) w f ( y 1) 3 4 w f ( w f ( y 1) w f ( x 1, y 1) w f ( x 1, w f ( x 1, y 1) 8 9 T[ y] s he pxel value afer he operaon of he mask w 5 s se o 8 and he oher coeffcens are se o -1 n our expermen Afer mage sharpenng, Closng operaon by a srucurng elemen wh sze 33 s performed o fll he nensy valley n mage Apply Dfferen operaon on he orgnal mage and he closng resul and hen he pxels of eye-analogue segmens reman on he resul mage Openng operaon s used o remove nose Dlaon max arg f ( x, y ), Eroson mn arg f ( x, y ), Openng Eroson Dlaon Closng Dlaon Eroson (a) (b) Fgure 5 The mage of rs regon afer rs deecon B Weghed Irs Recognon Afer he rs deecon, he local bnary paerns (LBP) are adoped o represen exure paerns of rs mages The LBP has emerged as a smple ye very effcen exure operaor and become a popular approach n varous applcaons of exure analyss [19, 20] LBP s defned for each pxel by comparng s 3 3 neghborhood pxels wh he cener pxel value, and consderng he resul as a bnary b srng Gven a pxel f( n he mage, an LBP code s compued by comparng wh s neghbours: P 1 p0 p LBP ( s( f ( f ( )2, (4) where s(z) s he hresholdng funcon 1, s(z) 0, 0 0 Here P represens he number of samplng pons, e, 8 surroundng pons Afer he LBP paern of each pxel p (5) s denfed, a hsogram of he mage wh sze IJ s bul o represen he exure mage: I J H( k) w(, ) g( LBP(, ), k), k 0, K (6) 0 0 where g( s he hresholdng funcon: 1, g( 0, x y oherwse The rs mage and s correspondng LBP feaures are shown n Fg 6 (a) Fgure 6 The rs mage and s correspondng LBP feaures Afer LBP, we followed he seup of [18] for nonparamerc exure classfcaon The LL dsance s sued for hsogram ype feaures For hsogram ype feaures, we used he log-lkelhood sasc, assgnng a sample o he class of model mnmzng he LL dsance LL( h S, h M B (b), (7) S M ) h ( b) log h ( b), (8) b1 where h S (b) and h M (b) denoe he bn b of sample and model hsograms, respecvely IV EXPERIMENTATION RESULTS For generang he rs mage under varan envronmen, he frs expermen appled dfferen lghng condons and ransformaons on he esng mage n he daabase [21] The average accuracy of verfcaons on he daabase s abou 925% In second expermen, 10 ndvduals are enrolled n he ranng daabase and 20 mages are used for each person n he esng process The expermenal resuls are shown n Table I, whch s he confuson marx of he expermen Each row (column) represens he acual class (predced class), respecvely TABLE I THE CONFUSION MATRIX OF THE EXPERIMENT A B C D E F G H I J A B C D E F G H I J

5 Journal of Advanced Managemen Scence, Vol 1, No 1, March 2013 V CONCLUSIONS Ths paper presens an negraed secury managemen for denfcaon and auhencaon of users based on weghed rs recognon echnology For calbraon on lne, we developed a hree-pon localzaon scheme for exracng approprae eye regon accordng o he nformaon of eye corners and he cener of eyebrow Furhermore, sasc based llumnaon normalzaon s used n preprocessng o decrease llumnaon nfluence under varan lghng condons The Adaboos and LBP hsogram are appled for rs deecon and recognon The proposed echnology can provde nformaon secury sysem a more flexble and feasble way o do auhencaon hrough moble devce ACKNOWLEDGMENT Ths work was suppored n par by a gran from NSC E REFERENCES [1] D Km and M Solomon, Fundamenals of Informaon Sysems Secury; Jones & Barle Learnng, 2010, ch 1 [2] J G Daugman, Hgh confdence vsual recognon of persons by a es of sascal ndependence, IEEE Trans Paern Anal Machne In, vol 15, no 11, pp , 1993 [3] J G Daugman, How rs recognon works, IEEE Trans Crcus Sysems Vdeo Technol, vol 14, no 1, pp 21 29, 2004 [4] A K Jan, P Flynn, and A Ross, Handbook of Bomercs, Sprnger-Verlag, 2007 [5] P W Hallnan, Recognzng human eyes, Geomerc Mehods n Compuer Vson, vol 1570, pp , 1991 [6] K W Bowyer, K Hollngsworh, and P J Flynn, Image undersandng for rs bomercs: A survey, Compuer Vson and Image Undersandng, vol 110, pp , 2008 [7] H Proença, S Flpe, R Sanos, J Olvera, and L A Alexandre, The UBIRISv2: A daabase of vsble wavelengh rs mages capured on-he-move and a-adsance, IEEE Trans Paern Anal Machne Inell vol 32, pp , 2010 [8] J Daugman, New mehods n rs recognon, IEEE Trans Sysems, Man, Cyberne, Par B, vol 37, pp , 2007 [9] Z Sun and T Tan, Ordnal measures for rs recognon, IEEE Trans Paern Anal Machne Inell vol 31, pp , 2009 [10] P L and H Ma, Irs recognon n non-deal magng condons, Paern Recognon Leers, vol 33, no 8, pp , 2012 [11] Y Moses, S Edelman, and S Ullamn, Generalzaon on novel mages n uprgh and nvered faces, Percepon, vol 25, pp , 1996 [12] P J Phllps and Y Vard, Effcen llumnaon normalzaon of facal mages, Paern Recognon Leers, vol 17, pp , 1996 [13] Y Freund and R E Schapre, Expermens wh a new boosng aalgorhm, n Proc 13h Inernaonal Conference on Machne Learnng, Bar, Ialy, 1996, pp [14] R E Schapre and Y Snger, Improved boosng algorhms usng confdence-raed predcons, Machne Learnng, vol 37, no 3, pp , 1999 [15] J Fredman, T Hase, and R Tbshran, Addve logsc regresson: A sascal vew of boosng, The Annals of Sascs, vol 28, no 2, pp , 2000 [16] P Vola and M J Jonse, Robus real-me face deecon, Inernaonal Journal of Compuer Vson, vol 57, no 2, pp , 2004 [17] L L Huang and A Shmzu, A mul-exper approach for robus face deecon, Paern Recognon, vol 39, pp , 2006 [18] R C Gonzalez and R E Woods, Dgal Image Processng, 3rd edon, Pearson Educaon, Inc, 2007 [19] T Oala, M Pekanen, and T Maenpaa, Mulresoluon grayscale and roaon nvaran exure classfcaon wh local bnary paerns, IEEE Trans on PAMI, vol 24, no 7, pp , 2002 [20] Y H Tsa and S W Chang, Texure Analyss and Recognon Based on LBP, presened a Inernaonal Conference on Informaon Technology, Tawan, 2012 [21] Irs mage daabase for he expermen [Onlne] Avalable: hp://phoenxnfupolcz/rs/ Yao-Hong Tsa receved he MS and PhD degrees n nformaon managemen from he Naonal Tawan Unversy of Scence and Technology (NTUST), Tape, Tawan, ROC, n 1994 and 1999, respecvely He was a Researcher wh he Advanced Technology Cener, Informaon and Communcaons Research Laboraores, Indusral Technology Research Insue (ITRI), Hsnchu He s currenly a Asssan Professor wh he Deparmen of Informaon Managemen, Hsuan Chuang Unversy, Hsnchu Hs curren research neress nclude mage processng, paern recognon, and compuer vson 95