Hybridization of Bimodal Biometrics for Access Control Authentication

Transcription

1 Internatonal Journal of Future Computer and Communcaton, Vol. 3, No. 6, December 4 Hybrdzaton of Bmodal Bometrcs for Access Control Authentcaton T. Zuva, O. A. Esan, and S.. Ngwra Abstract Sngle bometrc trat for authentcaton s wdely used n some applcaton areas where securty s of hgh mportance. However, bometrc systems are susceptble to nose, ntra-class varaton, non-unversalty and spoof attacks. Thus, there s need to use algorthms that overcome all these lmtatons found n bometrc systems. The use of multmodal bometrcs can mprove the performance of authentcaton system. Ths study proposed usng both fngerprnt and face for authentcaton n access system. The study ntegrated fngerprnt and face bometrc to mprove the performance n access control system. Fngerprnt bometrc, ths paper consdered restoraton of dstorted and msalgned fngerprnts caused by envronmental nose such as ol, wrnkles, dry skn, drt, dsplacement etc. The nosy, dstorted and/or msalgned fngerprnt produced as a -D on -y mage, s enhanced and optmzed usng a hybrd odfed Gabor Flter-Herarchal Structure Check (GF-HSC) system model. In face bometrc Fast Prncpal Component Analyss (FPCA) algorthm was used n whch dfferent face condtons (face dstortons) such as lghtng, blurrness, pose, head orentaton and other condtons are addressed. The algorthms used mproved the qualty of dstorted and msalgned fngerprnt mage. They also mproved the recognton accuracy of dstorted face durng authentcaton. The results obtaned showed that the combnaton of both fngerprnt and face mprove the overall performance of bometrc authentcaton system n access control. Inde Terms Bometrcs, multmodal, authentcaton, fast prncpal component analyss. I. INTRODUCTION Bometrc system s an automated technque of recognzng a person based on physologcal and behavoral trats. The physologcal trats nclude the face, fngerprnt, palm prnt and rs, whch reman permanent throughout an ndvdual s lfetme. The behavoral trats are sgnature, gat, speech and keystroke, etc., whch change over tme []. The advantages of a fngerprnt authentcaton system make the system the most wdely used bometrc system for varous applcatons for securty and access control n arports, at borders, mmgraton offces, houses, offces, banks and other places where securty needs to be enhanced []. However, face dentfcaton s also one of the acceptable bometrc systems wdely used n publc securty systems, attendance systems etc. because of ts convenence and hgh effcency []. In ths regards, the problem of securng anuscrpt receved ay 5, 4; revsed August 3, 4. Ths work was supported n part by the Department of Computer System Engneerng, Tshwane Unversty of Technology, South Afrca. Paper ttle: Hybrdzaton of Bmodal Bometrc for Access Control Authentcaton. The authors are wth Tshwane Unversty of Technology, Soshanguve Campus, South Afrca (e-mal:zuvat@tut.ac.za, esanomobayo@tut.ac.za, ngwras@tut.ac.za). nformaton emerged, snce nformaton needs to be managed. Thus, as the method of mantanng securty ncreases, the threat of securty breachng also ncreases. However, Fg. n [] shows the record of securty breaches at fnancal nsttutons from 8 to n Unted State. From the Fg. n [], t can be observed that there s hgher number of hacker, malware and msuse. Ths s not good enough, partcularly for the fnancal nsttutons. Ths record has proved that tradtonal authentcaton system cannot handle the growng daly fnancal customer s transactons across the globe []. However, wth the advances n bometrcs system, t has replaced the use of tradtonal technque of authentcaton snce bometrcs cannot be lost and forgotten []. Bometrc system s an automatc technque of dentfyng person based on bologcal and physologcal trats. Bometrc technology has presented several advantages over classc securty methods, as there s no need for the user to remember dffcult PIN codes that could easly be forgotten or carry a key that could be lost or stolen [3]. However, n spte of these advantages, fngerprnt authentcaton systems stll present a number of drawbacks, ncludng fngerprnt dstortons, msalgnment and lack of secrecy (e.g., hackers can easly steal someone s fngerprnts at any tme). It s thus of specal relevance to address these drawbacks for the beneft of fngerprnt users n access control area such as fnancal nsttutons. A. Fngerprnt Algnment Fngerprnt algnment s a crucal stage n a fngerprnt authentcaton system. salgnment s caused by dsplacement n the query fngerprnt mage, mostly durng the authentcaton phase. The dsplacement ncludes the translaton and rotaton of a fngerprnt mage [3]. Fg. (a) shows an eample of a normal fngerprnt mage captured n database as template, Fg. (b) shows msalgned postons of the same fngerprnt at query stage. However, durng authentcaton phase, ths msalgnment often affects the matchng accuracy when compared the query fngerprnt wth the template n database durng authentcaton [3]. B. Fngerprnt Dstortons Dstortons n fngerprnts are caused by poor qualty nput, whch mght be due to varatons n skn condton caused by accdents, cuts or bruses. The rdge structure n such fngerprnt mages s consequently not well-defned and correctly detected. The red rectangular bo n Fg. ndcates areas of dstorton, whch may lead to the creaton of a sgnfcant number of spurous mnutae, causng a large percentage of genune mnuta to be gnored and large error n localzaton [4], [5]. DOI:.7763/IJFCC.4.V

2 Internatonal Journal of Future Computer and Communcaton, Vol. 3, No. 6, December 4 (a). Normal fngerprnt (b). salgned query fngerprnt Fg.. Fngerprnt wth normal and msalgnment. Fg.. Fngerprnt wth dstorton. C. Face Bometrc In face recognton, there are some features that are mportant for system recognton. These features nclude nose, eyes, eyebrows, mouth and nostrl [6]. Each of these features has some values or weghts to recognze the face. Fg. 3 shows some of the features used n face recognton. The accurate estmaton and etracton of human face features are very challengng. Fg. 3. Some of the features used n face recognton. D. Face Dstortons The etracton of the human eye at grey level s obtaned from valley features. The relatonshp between human face and other facal feature s that the sze of the human face s equvalent to the dstance between the eyes that contans the regon of eyebrows, eyes, nose and mouth [6], [7]. Consequently, etracton of these features can be affected by several varables such as glasses, facal har; face mpressons etc., these makes etractng and postonng of facal features not to be accurately estmated[6], [7]. oreover, human face s a 3-D obect whch s vulnerable to dstorton and uneven llumnaton that can make detectng of true face to be dffcult [7] As dscussed, most fngerprnt authentcaton systems address these two challenges separately. Ths research therefore provdes a new bmodal bometrc authentcaton system whch has the potental of mtgatng the challenges of fngerprnt and face whch are dstorton, msalgnment, face feature etracton and llumnaton for users access control. Ths paper advances the estng bmodal authentcaton system by addressng problem of dstorton and msalgnment n fngerprnt to mprove the securty of access control area such fnancal nsttutons. The maor contrbutons of ths paper are as follows: Proposal of a bmodal bometrcs consttutng a hybrd odfed Gabor flter-herarchcal Structural Check (GF-HSC) matchng and a Fast Prncpal Component Analyss (FPCA) as a two-level securty authentcaton. Epermental evaluatons of the bmodal bometrcs wth applcaton to fnancal systems usng real-lfe fngerprnt mages and benchmarkng wth publcly avalable datasets usng FVC a methods and facal mages. The rest of ths paper s organzed as follows: Secton II presents the theoretcal background, whch ncludes related work on the GF algorthm and HSC algorthm; Secton III presents the fngerprnt authentcaton system model; secton IV crtcally presents vsual nspecton and quanttatve epermental evaluatons of the approach usng lghtly and heavly dstorted fngerprnt mages. Our GF-HSC s also benchmarked wth the Gabor flterng method, and Secton V compares the proposed method wth related methods. We conclude the paper n Secton VI. II. THEORETICAL BACKGROUND A. Related Research Several fngerprnt approaches have been proposed n lterature. These nclude methods based on pont pattern matchng, transform features and structural matchng. A new method of personal authentcaton usng face and palm prnt mages n [8]. The proposed bmodal system authentcaton utlzed a neural network for obtanng the matchng score between the two bometrc trats before performng fuson scores. At the fuson level, the sum a and product rule were used n fusng the two trats together and the result obtaned shows a sgnfcant mprovement of bmodal bometrcs matchng when compared wth that of drect matchng score. A robust bmodal bometrc authentcaton system based on speech and sgnature bometrc trats was developed [9]. The etracton of speech bometrc was done by tranng the cl Frequency Cepstral Coeffcent (FCC) and Wavelet Octave Coeffcent of Resdual (WOCOR) as a feature vector. The FCCs and WOCORs from the traned data are modelled usng vector Quantzaton (VQ) and Gaussan ture odellng (G) technques. The sgnature based bometrc system s developed by usng Vertcal Proecton Profle (VPP), Horzontal Proectle Profle (HPP) and Dscrete Cosne Transform (DCT) as feature. From the eperment conducted the result shows that bmodal person authentcaton gves hgher performance compared wth a sngle bometrc system. Sngle modal bometrc trats are affected by nosy sensor data, non-unversalty and thus are susceptble to spoof attack are ntroduced n []. However [], addressed these ssues [] ntegrated rs and sgnature trats, the two bmodal trats were then fused together usng user-specfc weghtng technque whch also ncreases the accuracy of the proposed bmodal bometrc system. Although the effcacy of the approach was only tested on rs and sgnature and not on other bometrc modaltes. B. odfed Gabor Flterng Algorthm The advantage of spatal and frequency propertes ehbted by Gabor makes t an mportant tool n computer vson and mage processng. The -D Gabor functon have 445

3 Internatonal Journal of Future Computer and Communcaton, Vol. 3, No. 6, December 4 harmonc oscllator conssts of a snusodal plane wave of specfc frequency and orentaton n a Gaussan envelope []. A rdges and valleys structure has a defned frequency and orentaton estmaton whch helps n removng undesred nose. However t s approprate to use Gabor flters as a band pass flters to remove the nose and preserve true rdge or valley n the area of the fngerprnt where there s no appearance of mnutae []. Equaton () represents an even-symmetrc real component of a D Gabor flter n the spatal doman that can be used n removng nose and preservng the true rdge/valley structure n fngerprnt mages. Thus, the local structure matchng for query fngerprnt mnutae and template mnuta s represented n trplet form, as shown n equaton (4): y θ θ G(, y, f, θ) = ep + cos σ σ y ( π f ) () where θ s the rdge wth respect to the vertcal as f, s the frequency of the snusodal plane wave n the σ are standard devaton of θ drecton, σ and y Gaussan functon along the θ and yθ aes respectvely. However, n the GF approach a pel-wse scheme s used to estmate the orentaton feld of the dstorted fngerprnt mage correctly, as n equaton (). θ (, ) = v + v = w + w w = u = w w + w + w w u = w s the mage block sze, each (, y) w G w G ( u, v ) G y ( u, v ) ( u, v ) G y ( u, v ) () G and G y are the gradent at n each block, u and v are the dstance along and y respectvely, derved from equaton (), the areas wth dstorton are epressed n equaton (4) as T n harmonc oscllaton and also the frequency doman n equaton () s represented wth a cosne functon n equaton (3). odulatng the perodc functon F ( X, X, T) to obtan; (,,, ) = (,, ). (, ) g y T φ h T φ hy y φ φ πφ y φ = ep cos ep σ T σ y The mert of odfed Gabor flter approach s t parameter selecton s mage-ndependent. C. Herarchcal Structural Check atchng Algorthm atchng two fngerprnts n mnutae-based representaton presents a problem wth algnng the two pars of correspondng fngerprnts mnuta ponts []. A mnuta-matchng algorthm based on composte features can be used to address the ssue of rotaton and geometrcal transformaton. Fg. 4 shows a typcal eample of composte feature representaton. (3) Fg. 4. Composte feature representaton []. d ϕ (4) _, _,θ_ d_ s the length of l _ connectng.athematcally epressed n equaton (5). d = ( ) + ( y y ), ϕ_ s the dfference between angle of and equaton (6). φ = α α = tan y β y and (5) as n β s the angle of and β s the angle of, θ_ s the counter-clockwse angle between the orentaton of and drecton from and The mert of Herarchcal Structure Check s t addresses ssue of rotaton and translaton pf parameter n fngerprnt mage. D. Face recognton Usng Fast Prncpal Component Analyss (FPCA) The Fast Prncpal Component Analyss (FPCA) procedure conssts of takng a sample of a grey scale mage n D matr and transformed nto D column vector of sze N by placng the matr column consecutvely [6]. (6) (7) 446

4 Internatonal Journal of Future Computer and Communcaton, Vol. 3, No. 6, December 4 These column vectors of n mages are placed column wse to form the data matr (mage set) X of dmenson N n. The mean q be the mean vector of the data vectors n matr X gven n equaton (5) q = n k = The vector of data matr X are centered by subtractng the mean vector Q from all the column vectors of X to obtan covarance matr C of the column vector n equaton (6). t C = YY (6) The Egen values and correspondng egenvectors are computed for covarance matr as n equaton (7). (5) CV = ΛV (7) where Λ s the set of egenvectors assocated wth egenvalues Λ. Set the order of the Egen vectors accordng to ther correspondng egenvalues from hgh to low. Ths matr of egenvalues s Egen space V. The data matr X s proected onto the Egen space to get P consstng n columns as n equaton (8). where t P = V X (8) In the recognton phase, the mage I to be recognzed s converted to D vector and form J whch s proected onto the same Egen space to get Z n equaton (9). t Z = X J (9) The Eucldean dstance d between Z and all proected samples n P s measured usng norm of an mage A and B gven n equaton (). N. ( ) ( ) L A B () A,B = = The proected test mage s compared to every proected tranng mage and the tranng that s found closet to the test mage s used to dentfy the tranng mage. The advantage of Fast Prncpal Component Analyss (FPCA) s that t s faster when used on large database and also gves accurate face recognton result. ) The enrolment phase Accordng to the system archtecture n Fg. 5, t s at ths stage that the fngerprnts are rotated n dfferent drectons to avod rotatonal and drectonal nvarance of the user fngerprnt durng the authentcaton stage, as the drecton used for the regstered user fngerprnt on the template of the stored user fngerprnt s captured usng a fngerprnt scanner or fngerprnt reader and ths s stored together wth other relevant nformaton on the user. The enrolment module s sub-dvded nto: Fg. 5. System archtecture. ) Image acquston stage As reflected n Fg. 5, the fngerprnt mages of users are captured wth a fngerprnt reader and are saved n a database wth other relevant nformaton. 3) Bometrc feature etracton stage Before feature etracton, the mage s passed through mage enhancement stages, whch nclude normalzaton, bnarzaton, segmentaton and thnnng. After these stages follows feature etracton, represented n Fg. 5, n whch the most mportant features, such as rdges and valleys, are etracted from the fngerprnt by subectng t to mage processng and etracton algorthms. The etracted features are set as bnares n whch the grey regon s represented as s and the whte regon s represented as s respectvely. The cross number (CN) concept s used for etracton of fngerprnt features as ether rdge-endng or bfurcaton. III. PROPOSED SYSTE ARCHITECTURE The system archtecture descrbed n Fg. 6 for a bmodal bometrc authentcaton system s dvded nto two stages: () fngerprnt authentcaton usng GF-HSC approach and () Face recognton usng fast PCA. A. Fngerprnt Authentcaton Usng GF-HSC Approach As ndcated n Fg. 5, the fngerprnt authentcaton phased s dvded nto two modules, namely: () enrolment module and () authentcaton module In algorthm, the rdges and valley n mnutae are computed by dvdng the mage nto block-szed 447

5 Internatonal Journal of Future Computer and Communcaton, Vol. 3, No. 6, December 4 centered (, ), and an orented wndow of w l s bult at each center. The second dervatves and the magntude of fst dervatves are obtaned. A local threshold value s set to determne rdge wdth and valley wdth: f the magntude of the frst dervatve s greater than the threshold, t s a rdge, otherwse t s a valley. B. Authentcaton Phase Accordng to the system archtecture depcted n Fg. 5, durng the authentcaton module the system requres the user to present hs or her fngerprnt physcally agan for the system to confrm whether he/she s who he/she clams to be. Ths module s subdvded nto two stages: ) Algnment stage usng a composte herarchal structural check (HSC) Fngerprnt msalgnment shown n the bottom left area of Fg. 4 s a man stage n the fngerprnt authentcaton system, caused by the dsplacement of the fngerprnt mage durng the authentcaton stage. The performance of the fngerprnt-matchng algorthm reles heavly on the accuracy of fngerprnt algnment. In order to mprove the performance of the fngerprnt authentcaton system, the template and query fngerprnt should be algned. Ths process occurs n the followng ways: The HSC that conssts of trplet composte features, whch are the Eucldean dstance (d), the angle between two mnutae and the angle between orentatons (θ), s constructed. A reference regon s located and an adaptve bo s drawn around the reference pont. The radus R s located, all mnutae are marked and the composte features are constructed nsde. The composte feature formng a seres of coordnate ponts and correspondng smlartes between the query fngerprnts wth the template n the database s estmated wth respect to all mnuta, rotated and translated at dfferent angles and n dfferent drectons. ) atchng stage of composte features n trplet form to determne f they are dentcal. From algorthm, n matchng two fngerprnts, the algorthm returns true f t matches (as determned by dverse parameters n the algorthm) and false f t does not match. C. Face recognton Usng Prncpal Component Analyss (PCA) Algorthm In developng face recognton system there are stages that are very crucal n contrbutng to the success of the system. The followng system archtecture of face recognton process n Fg. 5 Face recognton s dvded nto the followng stages: () Image pre-processng stage () Feature etracton usng fast Prncpal Component Analyss (FPCA) and () Face matchng ) Image pre-processng stage The acqured mages are algned at the pre-processng stage and the croppng operatons are performed at ths stage before face mage s passed to feature etracton stage. ) Tranng stage In the tranng stage, the acqured mage that has passed through an mage pre-processng stage such as hstogram normalzaton to adust the contrast process of the mage n order for the mage output to have a unform dstrbuton of grey values and to reduce the lght ntensty varaton level n the grey. At ths stage the query fngerprnts are compared wth the bank fngerprnt n the database (template) to determne f the person s who he clams to be. Ths s done by usng the matchng algorthm and matchng score of two mnuta pars From algorthm 3, s vector dmensonal representaton and proecton s an Egen face havng a bass ( << D). The D-dmensonal vector s used for dmenson reducton. The same procedure s followed durng the tranng stage before comparng the mage vector obtaned wth the mage n the database to determne ther correspondng smlartes. 3) Testng stage Durng ths stage, the mage to be recognzed s passed through the testng stage by passng the mage agan through mage pre-processng and features etracton, as done n the tranng phase. The etracted features are converted to an mage vector and the mage s proected to the Egen space. The Eucldean dstance between the tested mage and all proected traned mages s estmated to fnd the 448

6 Internatonal Journal of Future Computer and Communcaton, Vol. 3, No. 6, December 4 correspondng closest one and ths s used for recognton. From algorthm 4, the face mage passed through the mage pre-processng stages and features are etracted agan n whch the features are represented n a vector form. The vector obtaned durng the testng s classfed f normalzed face mage E s greater than Eucldean dstance E the mage s recognzed as face and f otherwse t s recognzed as non-face. where I the number of mposter s fngerprnt s accepted and N s total number of genune tested. All these equatons are used as obectve evaluaton schemes for degraded and fngerprnts matchng. The percentage of System Accuracy (SA) s computed by the followng formula n equaton (3). SA = (3) P where SA s the system accuracy, s the total number of organzed fngerprnt mage sample and P s the total number of fngerprnt sample. These equatons are used as obectve evaluaton schemes for measurng dstorted and msalgned fngerprnt enhancement. 4) atchng Stage At the matchng stage the tested facal mage s compared to every proected tranng mage, the tranng mage that s smlar to the test mage s then used to dentfy the tranng face mage. IV. SCORING AND EVALUATION SCHEE In ths secton, the performance of the proposed bmodal bometrc s studed through vsual nspecton as well as quanttatvely. Durng vsual nspecton, one compares the qualty of the pel value of dstorted and msalgned fngerprnts wth enhanced fngerprnt mages []. The followng evaluaton models were chosen as quanttatve scheme []: () the False Reecton Rate (FRR) and () the False Acceptance Rate (FAR) [9]; the schemes are computed by the followng formulas n equatons ()-(): G FRR = () N where G the number of mposter s fngerprnt s reected and N s total number of genune tested. I FAR = () N V. EXPERIENTAL EVALUATIONS One of the obectves of ths paper s to apply the theory of our approach n practce by emphaszng applcatons and carryng out practcal work on fngerprnts wth dstorton and algnment, as well as face recognton usng ATLAB, as shown n Fg. 6 and Fg. 8 respectvely. The fngerprnt and face mages are captured wth a Futronc fngerprnt scanner and canon dgtal camera respectvely. An orgnal fngerprnt wth dstortons and overlappng s shown n Fg. and 3. In calculatng the percentage of nosy regon, the fngerprnt mage s dvded nto 3 3 wndow sze; the CN method etracts the rdge endngs and bfurcatons by eamnng the local neghborhood of each rdge pel usng a 3 3 wndow. After etracton usng the CN concept, the regon wth dstorton s estmated usng a nose detector scheme n (4). The scheme states that: () f a pel has at least one pel y among the other eght pels n the neghborhood, then s consdered an orgnal pel and y s deemed smlar to pel ; and () f does not have at least one smlar pel among ts neghbors, t s consdered to be dstorted; ths s shown usng equaton (4): { } K y D N = n else th. (4) D s adopted as the mamum depth dfference between the smlar and y pels and s often assumed to be eght pels n the neghborhood. N th s as every pel s assumed to be smlar to at least pel, and K s the number of y pels that satsfes equaton (4) whle the dstorted pel s elmnated. However, ths work focuses on bmodal bometrcs and enhancng dstorted and msalgned fngerprnt mages. In terms of performance measures, the FAR and FRR, are computed when evaluatng the result of the proposed GF-HSC n Fg. 6. A. Eperment : Benchmarkng our Approach wth Publcly Avalable Templates and ethods The am of benchmarkng s to access the qualtatve 449

7 Internatonal Journal of Future Computer and Communcaton, Vol. 3, No. 6, December 4 performance of our proposed approach on FVC a DB fngerprnt database where fngerprnt are notceably dfferent from real-lfe fngerprnt mages. (a) Orgnal (b) Gabor (c) GF-HSC mage method [8] approach was able to brng the orgnal mage of the dstorted mage. The result n Fg. 9 shows the graphcal performance of our fast PCA approach when a certan percentage of the mages of the database are dstorted and then used as query mages. The graph n Fg. 9 shows that the system gves % accuracy when the orgnal faces (% dstorton) are used as query mages. The worst case scenaro when all the mages n the database are deformed and used as the query mages, the performance of the system s appromately 79% accurate. (d) Orgnal (e) Gabor (f) GF-HSC mage method [8] approach Fg. 6. Images (a), (c), and (e) are real-lfe fngerprnts wth dstorton and msalgnment; mages (b), (d) and (f) are the enhanced fngerprnts. The orgnal fngerprnts n Fg. 6 (a) and Fg. 6(d) respectvely contan dstorton. Gabor flterng and our GF-HSC approach were used to enhance and flter the dstorted regons. Fg. 6 (b), Fg. 6 (c), Fg. 6 (e) and Fg. 6 (f) show the result of Gabor flterng and our GF-HSC approach respectvely. In Fg. 5, the GF-HSC approach s benchmarked wth the Gabor method. One can see the result of the GF-HSC approach n Fg. 6(c) and Fg. 6 (f) respectvely, whch show better enhancement clarty compared to the mages n Fg. Fg. 6 (b) and Fg. 6 (e) respectvely. (a). Query facal mage. (b). Output. Fg. 8. Facal mage wth dstorton and traned usng fast PCA technque. Fg. 7. Graph for comparng modfed gabor flter wth tradtonal gabor flterng. The graph n Fg. 7 shows that GF-HSC approach gves a lower FAR and FRR compared to the Tradton Gabor ths s due to the fact that the parameter selecton are not based on assumpton. Also the GF-HSC approach s able to enhanced and etract feature better than TGF. B. Eperment : Performance of Fast PCA on Face Image In ths eperment the study endeavored to fnd the performance of the fast PCA on dstorted query face mages. Fg. 8(a) llustrates a dstorted face mage and Fg. 8 (b) gves the mage that was retreved. Ths showed how the system Fg. 9. Graph showng the accuracy of dstorted faces. VI. CONCLUDING REARKS In ths research, bmodal bometrc system for user access control authentcaton was the man nterest. An effectve and effcent authentcatng system for a user access control entals user havng an effectve physologcal or bologcal 45

8 Internatonal Journal of Future Computer and Communcaton, Vol. 3, No. 6, December 4 trats as a component of bometrc system. The fundamental components of bmodal bometrc system are mage pre-processng, mage segmentaton, mage feature etracton and mage matchng. In order to fulfl the obectve of ths research work revews of lterature, desgnng of new system model was done. And evaluaton of the system was done. The followng secton elucdate on the work done to fulfl the man obectve of bmodal bometrc system for authentcaton n access control. A prototype bometrc system whch ntegrates fngerprnts and faces n authentcatng a person for user access control has been developed. The system overcomes the lates of both fngerprnt authentcaton system and face recognton systems. The proposed system works n authentcaton mode. Epermental results demonstrate that the proposed system perform very well. It meets the securty as well as accuracy requrements. The bmodal bometrc system for authentcaton was fnally bult. The system was tested on users wth dstorted fngerprnt. The challenges come when a fngerprnt s completely dstorted. To tackle ths challenge a herarchal structural check algorthm was used for matchng and ths only utlzed the avalable query mnutae to compare wth the template mnuta for authentcaton. The fngerprnt authentcaton system performed well. Integratng fngerprnt authentcaton as well as face recognton system was done. The evaluators were satsfed wth the system. [5] A. Senor and R. Bole, "Improved fngerprnt matchng by dstorton removal," IEICE Trans. Inf. System, vol. 8, pp ,. [6] Neera and E. Wala, "Face recognton usng mproved fast PCA algorthm," Congress on Image and Sgnal Processng, 8. [7] K. W. Wong, K.. Lam, and W. C. Su, "An effcent algorthm for human face detecton and facal feature etracton under dfferent condtons," The Journal of Pattern Recognton, vol. 34, 4. [8] B. Bggo, Z. Akthar, G. Fumera, G. L. arcals, and F. Rol, Securty Evaluaton of Bometrc Authhentcaton Systems Under Realstc Spoofng Attacks, 9. [9]. N. Eshwarappa and. V. Latte, "Bmodal bometrc person authentcaton system usng speech and sgnature features," Internatonal Journal of Bometrcs and Bonformatcs (IJBB), vol. 4, pp. 47-6, 5. [] S. Vrr and J.-R. Tapamo, Integratng Irs and Sgnature Trats for Personal Authentcaton usng User-Specfc Weghtng, 6. Tranos Zuva s wth the Department of Computer Systems Engneerng at Tshwane Unversty of Technology, Soshanguve South Campus, and South Afrca. He has publshed many artcles n local and nternatonal conferences and ournals. Omobayo A. Esan s currently a doctoral student n the Department Computer Systems Engneerng, Unversty of Technology, Soshanguve South Campus, South Afrca. REFERENCES [] V. B. R. T. A. U. S. S. Servce, "A Study of Data Breach Investgaton Record n Fnancal Insttuton,". [] O. A. Esan, S.. Ngwra, and I. O. Osunmaknde, "Bmodal bometrcs for fnancal nfrastrucutre securty," presented at the Informaton Securty South Afrca (SSA), South Afrca, 3. [3] L. Hong and A. Jan, "Integratng faces and fngerprnts for personal dentfcaton," IEEE transacton on Pattern Analyss and achne Intellgence, vol., p., 999. [4] H. A. Aboalsamh, "Ven and fngerprnt bometrcs authentcaton-future trends," Internatonal Journal of Computer and Communcatons, vol. 3, 9. Seleman. Ngwra s workng as a professor n the Department of Computer Systems Engneerng, Unversty of Technology, Soshanguve South Campus, South Afrca 45