A New Face Authentication System for Memory-Constrained Devices

Transcription

1 1214 IEEE ransactons on Consumer Electroncs, Vol. 49, o. 4, OVEMBER 2003 A ew Face Authentcaton ystem for Memory-Constraned Devces Kyunghee Lee and Hyeran Byun Abstract hough bometrcs to authentcate a person s a convenent tool, typcal authentcaton algorthms by usng bometrcs may not be executable on the memory-constraned devces such as smart cards. We present a soluton of a face authentcaton algorthm for the open ssue. Our achevement s two-fold. One s to present a face authentcaton algorthm wth low memory requrement, whch uses support vector machnes (VM) wth the feature set extracted by genetc algorthms (GA). he other contrbuton s to suggest a method to reduce further, f needed, the amount of memory requred n the authentcaton at the expense of verfcaton rate by changng a controllable system parameter for a feature set sze. Gven a pre-defned amount of memory, ths capablty s qute effectve to mount our algorthm on memory-constraned devces. Our expermental results show that the proposed method provdes good performance n terms of accuracy and memory requrement 1. Index erms Bometrcs, Face Authentcaton, Memory-Constraned Devces. I. IRODUCIO In recent years, there s an ncreasng trend of usng bometrcs nformaton, whch refers the human bologcal features used for user authentcaton, such as fngerprnt, rs, and face, to strengthen the securty measure of dfferent electronc/embedded systems, ncludng smart card systems [1]-[4]. Compared to the dgt Personal Identfcaton umber (PI), the crtcal data stored n the card can be protected more securely by usng the bometrc nformaton. Furthermore, cardholder s fngerprnt or rs pattern cannot be stolen or forgotten. mart cards can play an mportant role n bometrcs, too. For nstance, n an dentfcaton system, the bometrcs templates are often stored n a central database. Wth the central storage of a bometrcs, there s an open ssue of msuse of the same for purposes that the owner of the bometrcs may not be aware of. We can decentralze the database storage part nto mllons of smart cards and gve t to the owners. 1 hs work was supported n part by Bometrcs Engneerng Research Center (KOEF). Kyunghee Lee s wth the Department of Computer cence, Yonse Unversty, eoul, Korea. Also, he s currently workng for Bometrcs echnology Research eam, ERI, Daejeon, Korea (e-mal: unrom@etr.re.kr). Hyeran Byun s wth the Department of Computer cence, Yonse Unversty, eoul, Korea (e-mal: hrbyun@cs.yonse.ac.kr). Contrbuted Paper Manuscrpt receved June 12, /00 $ IEEE However, most of these systems have a common characterstc that the bometrcs authentcaton process s solely accomplshed out of the smart card processor [5]. For example, for fngerprnt-based smart card system, the card needs to nsecurely release the crtcal fngerprnt master template nformaton nto an external fngerprnt reader, whch performs the fngerprnt checkng. o hghlght the securty, the comparson of the fngerprnt sample and the master template needs to be performed by the n-card processor,.e., match-on-card [5], not the external reader. However, the memory sze and the processng power of the n-card processor s very lmted. hus, a lghtweght authentcaton algorthm that requres small memory and processng power needs to be developed. Among bologcal features, the face s one of the most acceptable bometrcs, because humans use t n ther vsual nteractons and acqurng face mages s non-ntrusve. However, t s dffcult to develop an automatc face recognton system, whle people can easly recognze famlar human faces. It s because face mages can vary consderably n terms of facal expressons, 3D orentaton, lghtng condtons, har styles, and so on. wo prmary approaches to face recognton are the holstc (or transform) approach and the analytc (or attrbute-based) approach [6]. In the holstc approach, the unverse of face mage doman s represented usng a set of orthonormal bass vectors. Currently, the most popular bass vectors are egenfaces [7]. Each egenface s derved from the covarance analyss of the face mage populaton. wo faces are consdered to be dentcal f they are suffcently close n the egenface feature space. A number of varants of such an approach exst. emplate matchngbased face recognton systems are also classfed nto ths approach. In the analytc approach, facal attrbutes lke nose, eyes, etc. are extracted from the face mage, and the nvarance of geometrc propertes among the face landmark features s used for recognzng features [8]. hs approach has characterstcs of hgh-speed and low-memory requrement, whle the selecton and extracton of features are dffcult. Many prevous works usng ths approach have been reported. For nstance, recognton accuracy of 90% could be acheved by usng geometrc features [8]. Hybrd approaches combnng both holstc and analytc have also been reported [6].

2 K. Lee and H. Byun: A ew Face Authentcaton ystem for Memory-Constraned Devces 1215 Recently, face detecton and recognton systems usng upport Vector Machne (VM) bnary classfers [9] have been proposed. For example, methods for face detecton [10], face pose dscrmnaton [11], and recognzng face mages [12]-[14] have been proposed usng VMs. Compared wth the standard PCA-based face authentcaton method, verfcaton system based on VM has shown to be sgnfcantly better by Phllps [14]. In ths paper, we present a memory-effcent face authentcaton algorthm that uses the features chosen by Genetc Algorthms (GAs) [15] as an nput vector to an VM and also provde ts mproved verson wth the property of adjustable memory requrement. he part of ths algorthm has been publshed n [16]. Our face authentcaton system uses only dscrmnatng features selected by GA, where the features are unque for each person and thus, the memory requrement can be decreased sgnfcantly. Also, f needed, the amount of memory requred n the authentcaton can be reduced further at the expense of verfcaton rate by changng a controllable system parameter for the feature set sze. he on-lne authentcaton tme can be decreased due to the optmal feature set, although the off-lne tranng tme s ncreased due to GAs. Furthermore, by usng a tunng data set n the computaton of the GAs evaluaton functon, the feature set whch s less dependent on llumnaton and expresson can be selected. he expermental results on the Yale face database [17] and the Cambrdge Olvett Research Lab (ORL) face database [18] show that our face authentcaton algorthm wth VM whose nput vectors consst of dscrmnatng features extracted by GA has much better performance than the algorthm wthout feature selecton process by GA, n terms of accuracy and memory requrement. Experment also shows that the number of the feature to be selected s controllable by a system parameter. Owng to those features, a smart card can encapsulate all the crtcal nformaton, ncludng the bometrcs data, and perform all the comparson securely nsde the card wthout any data leakng out. he outlne of the paper s as follows: background for VM, GA, and Prncpal Component Analyss (PCA) s gven n ecton 2. ecton 3 descrbes method to construct our face authentcaton system and ecton 4 explans the authentcaton procedure usng the face authentcaton system. In secton 5, we descrbe how to mplement our system on match-on-card. ecton 6 contans expermental results and analyss, and concludng remarks are made n ecton 7. II. BACKGROUD In ths secton we wll gve bref overvew of VM and GA. More detaled descrptons of VM and GA can be found n [9] and [15]. Also, PCA that s used as a feature lst s surveyed concsely. A. upport Vector Machnes (VM) upport Vector Machnes (VM) have been recently proposed by V. Vapnk and hs co-workers as an effectve and general purpose method of pattern recognton [9]. VM s a bnary classfcaton method that fnds the optmal lnear decson surface based on the concept of structural rsk mnmzaton. he decson surface s a weghted combnaton of elements of the tranng set. hese elements are called support vectors and characterze the boundary between the two classes. We start wth a labeled set of tranng samples ( x, y ), d where x R and y s the assocated label ( y { 1,1} ). Assumng lnearly separable data, the goal of maxmum margn classfcaton s to separate the two classes by a hyperplane such that the dstance to the support vectors s maxmzed. hs hyperplane s called the optmal separatng hyperplane (OH). he OH has the form: f ( x) = α y ( x x) + b, (1) = 1 where the coeffcents α and the b are the solutons of a quadratc programmng problem. α s non-zero for support vectors and s zero otherwse. For lnearly non-separable data, VMs can nonlnearly map the nput to a hgh dmensonal feature space denoted by F where a lnear hyperplane can be found. A hgh dmensonal mappng such as φ : R d a F, (2) s used to buld nonlnear support vector machnes. As both the objectve functon and the decson functon s expressed n terms of dot products of data vectors x, the potentally computatonally ntensve mappng φ ( ) dose not need to be explctly evaluated. A kernel functon, K ( x, x ), satsfyng Mercer s condton can be used as a substtute for ( φ( x) φ( x )) whch replaces ( x x ) [9]. he decson surface has the equaton: f ( x) = α yk( x, x) + b. (3) =1 he followng kernel functons are frequently used n VM: the polynomal kernels gven by K ) p ( x, x ) = ( x x + 1, (4)

3 1216 the RBFs kernels gven by 1 2 K ( x, x ) = exp x x 2, (5) 2σ and the tangent hyperbolc kernels gven by K ( x, x ) tanh( α x x + β ). (6) = In ths paper, we use a polynomal kernel functon of the 4th order to construct an VM. B. Genetc Algorthms (GA) Genetc Algorthm(GA)s are adaptve and robust computatonal procedures modeled on the mechancs of natural genetc systems[15]. GAs typcally mantan a constant-szed populaton of ndvduals that represent canddate solutons to the optmzaton problem beng solved. he ndvduals are typcally represented n-bt bnary vectors, and thus the resultng search space corresponds to an n- dmensonal boolean space. he goodness of each canddate soluton can be evaluated usng a ftness functon. Evolutonary algorthms, usng some form of ftness-dependent probablstc method, select ndvduals from the current populaton to produce ndvduals for the next generaton. Genetc operators are appled to the selected ndvduals to obtan new ndvduals that consttute the next generaton. Mutaton and crossover are two of the most common operators used wth genetc algorthms. Mutaton operator s appled to a sngle strng and generally changes a bt at random. Crossover, on the other hand, operates on two parent strngs to produce two offsprng. he process of ftness-dependent selecton and applcaton of genetc operators to generate successve generatons of ndvduals s repeated untl a satsfactory soluton s found. Recently, a GA-based representaton transformaton has been developed for a general classfcaton problem [19]. It can select and create approprate features n order to represent a problem sutably. C. Prncpal Component Analyss (PCA) PCA s a standard technque used to choose a dmensonalty reducng lnear projecton that maxmzes the scatter of all projected samples. he basc approach of the PCA s to compute the egenvectors of the covarance matrx, and approxmate the orgnal data by a lnear combnaton of the leadng egenvectors [7]. Let the tranng set of face mages be Γ, Γ, Γ,..., Γ, M where a face mage s by. he average face of the set s defned by IEEE ransactons on Consumer Electroncs, Vol. 49, o. 4, OVEMBER 2003 ranng Data unng Data Ψ = 1 VM Constructon VM Evaluaton M = Γ n 1 M Images Preprocessng Constructon of Feature Lst Instances GA Evoluton for Feature electon Evaluaton of Ftness Value toppng Crteron yes VM wth the Best Feature ubset Measurng the ze of of Feature ubset no Feature ubset ze Control Parameter GA Fg. 1. Constructon Procedure of the Proposed Face Authentcaton ystem n. (7) Each face dffers from the average face by the vector Φ = Γ Ψ. he vectors u k and scalars λ k are the egnvectors and egenvalues, respectvely, of the covarance matrx 1 M C = = Φ Φ = n n n AA, (8) 1 M where the matrx A = [ Φ 1Φ 2... Φ M ]. nce these egenvectors have the same dmenson as the orgnal mages and they are face-lke n appearance, they are referred to as Egenfaces. Gven the egenfaces, each face s represented as a vector of weghts. he weghts are obtaned by projectng the mage ( Γ ) nto the egenface components by a sngle nner product operaton, ω k = u k ( Γ Ψ) for k = 1,..., M, (9) where the M sgnfcant egenvectors are chosen wth the largest assocated egenvalues. he weghts form a vector Ω = [ ω ω,..., ω ] 1, 2 M, (10) whch descrbes the contrbuton of each egenface n representng the nput face mage. III. CORUCIO OF FACE AUHEICAIO YEM he block dagram of constructon of our face authentcaton system s shown n Fg. 1. Each step n the

4 K. Lee and H. Byun: A ew Face Authentcaton ystem for Memory-Constraned Devces 1217 dagram s descrbed n ths secton. Constructon of the face authentcaton system begns wth the preprocessng A. Preprocessng By normalzng the dstance between both eyes, a facal regon s cropped from an nput mage, whch s then normalzed nto a pxel mage so as to make the authentcaton system scale-nvarant. o fnd out eyes from an nput mage, any eye detecton algorthm may be used. If the axs connectng both eyes s on the skew, rotaton s appled to the mage. After then, hstogram equalzaton s appled to mnmze the effect of varatons n the mage brghtness and contrast. B. Constructon of Feature Lst Instances An element of a feature lst represents what the feature s, and a feature lst nstance ncludes values of elements of a feature lst. o avod duplcatng computaton n the GA step, the feature lst nstances to be used for the feature selecton are pre-computed. We suggest two knds of feature lsts: one s made up of average of ntensty values and that of edge values, and the other conssts of PCA projecton weghts. Besdes these feature lsts, varous feature lsts can be appled to the proposed system. 8 4 Feature Lst { f 1, f 2, f 3, f 4,...., f n, f n+1, f n+2,, f -1,f } Gene ze of Feature Lst () Fg. 3. Chromosome Representaton of Feature ubset C. electon of Dscrmnatng Features usng GA In our face verfcaton system, we employ GA to explore the space of all subsets of the gven feature lst. From now on, a subset of the gven feature lst s denoted by a feature subset. Durng the GA procedure, a preference s gven to feature subsets that acheve the best classfcaton performance and have small szes. Each of the selected feature subsets s then evaluated usng an VM. hs whole process s terated along evolutonary lnes untl the best feature subset s found. Fg. 3 shows that each chromosome s represented as a fxed-length bnary strng standng for some subset of the feature lst. Each bt of the chromosome represents whether the correspondng feature s selected or not. 1 n each bt means the correspondng feature s selected, whereas 0 means t s not selected. Durng the evoluton, the smple crossover operator and the eltst strategy are appled. 8 4 { f 1,..., f n,..., f 225,f 226,, f 225+n,.., f 450 } f 1 ~ f 225 : average of the ntensty values of 8*8 wndow f 226 ~f 450 : average of the edge values of 8*8 wndow Fg. 2. Feature Lst of Averages of Intensty and Edge Values D. Evaluaton of Chromosomes Usng VMs o evaluate a chromosome (equvalently, a feature subset), we construct an VM usng a polynomal kernel of the 4th order wth the selected feature subset nstances of a tranng data set as nput vectors. he ftness value of a chromosome s proportonal to the classfcaton performance of the VM for a tunng data set and s nversely proportonal to the sze of feature subsets. he ftness value s defned as Vself V other ftness = λ + ( 1 λ) (1 η) + η F( ), (11) self other he feature lst that conssts of averages of ntensty/edge values s computed from the mages of sze As Fg. 2 shows, each mage s scanned wth the wndow of sze 8 8 wth a 4-pxel overlap. Consequently, the number of scanned wndows s 225. A feature lst nstance for each mage s composed of values computed from the averages of the pxels wthn 225 wndows before and after applyng the obel edge operator [20]. he other feature lst s composed of the PCA projecton weghts. We defne the feature lst usng PCA as the weght vector Ω n (10) that s obtaned by projectng an mage nto the egenfaces correspondng to the M largest egenvalues. where s the total number of mages of subject hmself, self V s the number of mages of subject hmself verfed self correctly, s the total number of mages of others, and other V s the number of mages of others verfed correctly. λ other controls False Reject Rate(FRR) and False Acceptance Rate(FAR),.e., as λ ncreases, FRR decreases and FAR ncreases. he functon F ( ) s nversely proportonal to, whch s the sze of the feature subset. hus, F ( ) gves ftness more score when the chosen feature subset s smaller. ow, F ( ) s defned as followng:

5 1218 IEEE ransactons on Consumer Electroncs, Vol. 49, o. 4, OVEMBER / 4 F( ) = 1.0 /4 0.0 Best Feature ubset V, Weghts f / 4, (12) f / 4 < /2 f > /2 where s the sze of feature set. In (12), F ( ) = 1. 0 for / 4 gves all feature subsets whose szes are less than / 4 perfect scores(=1.0) not to dscrmnate them. By applyng the rule, we can score all feature subsets whose szes are less than / 4 by authentcaton rate wthout consderng the sze. hs s because havng a preference for a feature subset wth smaller sze among them results n subsequent degradaton of authentcaton rate. F ( ) = 0. 0 for > / 2 excludes feature subsets whose number of features are greater than half of, whch reduces the sze of feature subset used durng the constructon of the authentcaton system. In (11), η s the trade-off parameter between error rate and memory requrement. hat s, the larger η s, the less memory s requred because a small feature subset gets more score. Choce of small η means the preference for the small error rate rather than small memory usage. Images Preprocessng Preprocessng of Feature Extracton ubset Instance of VM VM Constructon Authentcaton usng usng VM Matched/on-Matched Fg. 4. Authentcaton Procedure Usng the Proposed Face Authentcaton ystem IV. AUHEICAIO PROCEDURE UIG OUR FACE AUHEICAIO YEM Usng the face authentcaton system establshed n secton 3, a real-tme face authentcaton procedure s provded n ths secton. he overall flow of the authentcaton procedure s shown n Fg. 4. A. Preprocessng and Extracton of Feature ubset Instance Frst, the same preprocessng algorthms as one n the system setup phase are appled durng ths phase. hen, feature subset nstance correspondng to the stored feature subset of an ndvdual to be authentcated, whch s chosen durng the authentcaton setup phase, s extracted from the mage to be authentcated. In ths paper, feature subset nstance s computed for the feature lsts composed of PCA projecton coeffcents, or for the 225 wndows of sze 8 8 wth a 4-pxel overlap generated by scannng a szed face mage, feature subset nstance s computed by calculatng average pxel ntenstes of those wndows and by computng averages of pxel ntenstes after obel edge operator s appled. B. Face Authentcaton usng VM An VM that s a face authentcaton system s constructed usng the stored support vectors and correspondng weghts of an ndvdual to be authentcated. he authentcaton s performed by gvng the constructed VM feature subset nstances and by observng the result. V. FACE AUHEICAIO YEM I MACH-O-CARD We descrbe how to apply our face authentcaton system to smart card whose processor performs matchng. Fg.5 depcts the enrollment and the authentcaton procedure n a match-oncard system. o enroll a user, an mage from a camera s preprocessed and a face authentcaton system s constructed usng our algorthms. he constructed face authentcaton system s stored nto the smart card, where the stored objects are composed of the support vectors, the weghted values and feature subset. hen, the system evolves usng the ftness value. After the evoluton, we obtan the VM n whch the nput vectors are the feature subset nstances correspondng to the most promnent chromosome. he feature subset s unque for each ndvdual, where the unqueness both n feature subset and n VM s obtaned from applcaton of an evolutonary algorthm to each ndvdual. ow, the VM acts as a face authentcaton system. o perform authentcaton on devces such as smart cards, the VM and the most promnent feature subsets are stored n the devces. Enrolled Face Image Input Face Image Enrollment Constructon of Face Preprocessng Authentcaton ystem Authentcaton Extracton of Preprocessng Feature Lst Instance Card Reader Feature ubset, VM Feature Lst Instance mart Card ORE VM, Feature ubset MACH Yes o Fg. 5. Authentcaton Procedure Usng the Proposed Face Authentcaton ystem

6 K. Lee and H. Byun: A ew Face Authentcaton ystem for Memory-Constraned Devces o authentcate a user, an mage to be authentcated s preprocessed n the same way as n the enrollment step. After the preprocessng, a feature lst nstance s extracted to be transported to the smart card. he smart card constructs an VM that s a face authentcator usng ts nternal nformaton such as the support vectors and the weghts. Among the feature lst nstance gven, only feature subset nstance correspondng to ndvdual feature subset stored n the card are extracted, and used as nput to the VM to authentcate a user. VI. y 1219 est data set: For each person, 5 mages of subject hmself and total 124 mages of 14 other persons were used for testng. Among these 124 mages, 44 mages are those of people whose mages do not appear n the tranng set nor n the tunng set. EXPERIMEAL REUL AD AALYI (a) Face Images from the Yale database (b) Face Images from the ORL database Fg. 6. ample mages from Yale and ORL databases o evaluate performance of our face authentcaton system, we make an experment on the very famous face databases such as Yale database and ORL database wth three knds of feature lsts. In ths secton, the expermental results are shown n varous aspects. A. Face Database We demonstrate our algorthm on both the Yale face database and the Cambrdge Olvett Research Lab (ORL) face database. he Yale face database [17] conssts of 11 mages per 15 people, one per dfferent facal expresson or confguraton: center-lght, w/glasses, happy, left-lght, w/no glasses, normal, rght-lght, sad, sleepy, surprsed, and wnk. he sze of mages s pxels. here are 40 persons n the ORL database [18] and 10 dfferent mages wth each person, ncludng varatons n pose, facal expresson (open or closed eyes, smlng or nonsmlng) and wth glasses or no-glasses, but there s lttle llumnaton varaton. he sze of mages s pxels. Fg. 6 shows sample mages of each database. Each database s categorzed nto the followng three data sets: a tranng data set that constructs the VM, a tunng data set that evaluates the VM and a test data set that estmates the performance of the fnal face verfcaton system after tranng. Yale database s categorzed as followng: y y ranng data set: For each person, 3 mages hmself and each 3 mages of 5 other persons for tranng. unng data set: For each person, 3 mages hmself and each 3 mages of 5 other persons for tunng. of subject were used of subject were used In the same way, three data sets for each person n the database are constructed, and then the proposed algorthm uses tranng data set and tunng data set to buld a face

7 1220 authentcaton system for each person. Evaluaton of the face authentcaton system s performed wth hs test data set. B. Feature Lst We perform experments for three knds of feature lsts. Each experment has the followng feature lst for each mage, where E p( Wk) denotes the average of gray ntensty values of the k-th wndow of an mage and E e( W k) represents average of edge values of the k-th wndow of an mage. ω M represents the PCA projecton weghts of an mage. Refer to (9) for the defnton of ω M Exp 1 : Feature lst for experment 1 { ω M M =1,...,50 } Exp 2 : Feature lst for experment 2 { E ( Wk), Ee( Wk) k = 1,...,225 } (13) p (14) Exp 3 : Feature lst for experment 3 { E ( Wk), Ee( Wk), k = 1,...,225, = 1,...,50 } p ω (15) M M o evaluate the effectveness of the proposed method, we consder two cases for each experment. One s only wth VM of whch nput vectors are composed of the feature lst nstances, and the other has GA feature selecton stage, where the nput vectors for VM consst of feature subset nstances chosen by GA. C. Expermental Results and Analyss able 1 and 2 compare performance of an VM-based face authentcaton algorthm havng GA feature selecton step wth one not havng GA step for (13), (14) and (15) on the very famous face databases such as Yale database and ORL database. Addtonally, performance of an VM-based face authentcaton s shown when pxel gray ntensty values of mage tself are a feature lst of the VM. Values n the tables are averages for every ndvduals n each database. In the experments ncludng GA feature selecton step, parameters related wth GA are pre-determned: the number of generatons=3000, λ = 0.45, η = As can be seen n Fg. 7 and Fg. 8, λ = 0.45, η = gve us the best FRR/FAR results. Refer to (11) for λ, η. able 1,2 shows that our face authentcaton algorthm has much less error rate than the algorthm that has only VM n all cases, even though only 24.0%-41.6% of the number of features (more exactly, the sze of the feature subset) are used. Especally, the face authentcaton algorthm that uses pxels gray ntensty tself as a IEEE ransactons on Consumer Electroncs, Vol. 49, o. 4, OVEMBER 2003 feature lst of the VM shows poor error rate compared to one wth GA feature selecton step n the experment 2, even though the former uses 4096(=64 64) features whch s about 20 tmes of the number of features that the latter uses. As a result, we can state that by usng dscrmnatng feature subset of an ndvdual that s extracted by our algorthm, the memory requrement as well as the overall error rate s substantally mproved. ow, we show effectveness of two controllable parameters by experments on Yale database. he frst one s λ, whch can control FRR and FAR,.e., as λ ncreases, FRR decreases but FAR ncreases. Fg. 7 shows the tradeoff between FRR and FAR whle changng λ. In the experment, let η = and the number of generatons =3000, where the feature lst (14) s used. As λ ncreases from 0.00 to 1.00, FRR decreases from 0.15 to 0.08 and FAR grows from 0.05 to hus, by choosng a proper λ, a system desgner can reflect user s requrement regardng FAR and FRR. he second one s η that can control memory requrement and error rate. Fg. 8 and 9 show the tradeoff between error rate and memory requrement whle changng η. In the experment, let λ =0.45 and the number of generatons=3000, where the feature lst (14) s used. Results are on Yale face database, too. As η ncreases from 0.00 to 0.90, FRR ncreases from 0.09 to 0.15 and FAR grows from 0.06 to Meanwhle, the sze of the selected feature subset decreases from 220 to 159 on the average. hus, by choosng a proper η, we can make our algorthm work n memory-constraned systems at the expense of verfcaton rate. ABLE 1 ERROR RAE OF VM OLY V. GA+VM O HE YALE FACE DAABAE Feature Lst Performance Exp. 1 (PCA) Exp. 2 (Avg. of Intensty & Edge) Exp. 3 (Avg. of Intensty & Edge, PCA) Pxel Intensty VM GA+VM VM GA+VM VM GA+VM VM FAR FRR o. of Features o. of Vs ABLE 2 ERROR RAE OF VM OLY V. GA+VM O HE ORL FACE DAABAE Feature Lst Performance Exp. 1 (PCA) Exp. 2 (Avg. of Intensty & Edge) Exp. 3 (Avg. of Intensty & Edge, PCA) Pxel Intensty VM GA+VM VM GA+VM VM GA+VM VM FAR FRR o. of Features o. of Vs

8 K. Lee and H. Byun: A ew Face Authentcaton ystem for Memory-Constraned Devces 1221 VII. COCLUIO mart card s a model of very secure storage, and bometrcs s the ultmate technology for authentcaton. he two can be combned n many applcatons to enhance both the securty and authentcaton. However, a careful desgn s requred to ntegrate the bometrcs nto the smart cards because the smart cards have very lmted memory. hs paper proposed a memory-effcent method of face authentcaton by ntegratng GA nto VM. Comparatve experments showed that our face authentcaton algorthm wth VM whose nput vectors consst of dscrmnatng features extracted by GA has much better performance than the algorthm wthout feature selecton process by GA has. Experments are performed on Yale face database and ORL face database for three carefully chosen feature lsts. Results showed that both FAR and FRR of our algorthm are lower than those of an VM-based face authentcaton algorthm wthout GA feature selecton step, even though our algorthm uses only 24.0%-41.6% of the number of features. Also, a method to reduce, f needed, the amount of memory requred n the authentcaton at the expense of authentcaton rate by changng a controllable system parameter s suggested. By adjustng the system parameter, we can reduce further the amount of memory requred by 35% for a feature lst usng average of ntenstes and that of edge values. herefore, the small memory requrement of the proposed method makes t applcable to ether large-scale face dentfcaton systems or memory-constraned smart card systems. It further enhances the securty ssues n adoptng the smart card nto many emergng applcatons, n contrast to the tradtonal PI verfcaton currently beng used. REFERECE [1] A. Jan, R. Bolle, and. Pankant, Bometrcs: Personal Identfcaton n etworked ocety, Kluwer Academc Publshers, [2] D. Zhang, Bometrc olutons For Authentcaton In An E-World, Kluwer Academc Publshers, [3] L. Jan, U. Halc, I. Hayash,. Lee, and. sutsu., Intellgent Bometrc echnques n Fngerprnt and Face Recognton, CRC Press, [4] A. oore, Hghly robust bometrcs smart card desgn, IEEE rans. on Consumer Electroncs, Vol.46, o.4, pp , [5]. Pan, Y. Gl, D. Moon, Y. Chung, and C. Park, A memory-effcent fngerprnt verfcaton algorthm usng a mult-resoluton accumulator array, ERI Journal, Vol. 25, o. 3, o be publshed, June [6] K. Lam and H. Yan, An analytc-to-holstc approach for face recognton based on a sngle frontal vew, IEEE ransactons on Pattern Analyss and Machne Intellgence, Vol. 29, o. 7, 1998, pp [7] M. urk and A. Pentland, Face recognton usng egenfaces, Proceedngs of IEEE Internatonal Conference on Computer Vson and Pattern Recognton, 1991, pp [8] R. Brunell and. Poggo, Face recognton: features versus templates, IEEE ransactons on Pattern Analyss and Machne Intellgence, Vol. 15, o. 10, 1993, pp [9] V. Vapnk, tatstcal Learnng heory, John Wley & ons, ew York, [10] E. Osuna, R. Feund, and F. Gros, ranng support vector machnes: an applcaton to face detecton, Proceedngs of IEEE Internatonal Conference on Computer Vson and Pattern Recognton, 1997, pp J. Huang, X. hao, and H. Wechsler, Face pose dscrmnaton usng support vector machnes, Proceedngs of Internatonal Conference on Pattern Recognton, 1998, pp [11] G. Guo,. Z. L, and K. L. Chan, upport vector machnes for face recognton, Image and Vson Computng 19, 2001, pp [12] K. Jonsson, J. Matas, J. Kttler, and Y. L, Learnng support vectors for face verfcaton and recognton, Proceedngs of Internatonal Conference on Automatc Face and Gesture Recognton, 2000, pp [13] P. Phllps, upport vector machnes appled to face recognton, Advances n eural Informaton Processng ystems 11, MI Press, 1999, pp [14] D. Goldberg, Genetc Algorthms n earch, Optmzaton, and Machne Learnng, Addson-Wesley, 1989 [15] K. Lee, Y. Chung, and H. Byun, Face recognton usng support vector machnes wth the feature set extracted by genetc algorthms, Proceedngs of Audo- and Vdeo-Based Bometrc Person Authentcaton, 2001, pp [16] Yale Face Database, [17] ORL Face Database, [18] H. Lu and H. Motoda, Feature Extracton, Constructon and electon: A Data Mnng Perspectve, Kluwer Academc Publshers, 2001 [19] R. Gonzalez, and R. Woods, Dgtal Image Processng, Addson Wesley Longman, 1992.

9 1222 IEEE ransactons on Consumer Electroncs, Vol. 49, o. 4, OVEMBER 2003 Kyunghee Lee receved the B.. and M.. degrees n computer scence from Yonse Unversty, Korea, n 1993 and 1998, respectvely. he s currently pursung Ph.D. degree at Yonse Unversty. he had been a researcher of the LG oft Company from 1993 to he s currently a senor member of engneerng staff of the Electroncs and elecommuncatons Research Insttute, Daejeon, Korea. Her research nterests nclude bometrcs, especally face recognton, pattern recognton, and mage processng. Hyeran Byun receved the B.. and M.. degrees n Mathematcs from Yonse Unversty, Korea. he receved her Ph.D. degree n Computer cence from Purdue Unversty, West Lafayette, Indana. he was a assstant professor n Hallym unversty, Chooncheon, Korea from nce 1995, she has been an assocate professor of Computer cence at Yonse Unversty, Korea. Her research nterests are multmeda, computer vson, mage and vdeo processng, artfcal ntellgence and pattern recognton.