Efficient Eye Location for Biomedical Imaging using Two-level Classifier Scheme

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1 828 Iteratioal Joural of Cotrol, Automatio, ad Systems, vol. 6, o. 6, pp , December 2008 Efficiet Eye Locatio for Biomedical Imagig usig Two-level Classifier Scheme Mi Youg Nam, Xi Wag, ad Phill Kyu Rhee* Abstract: We preset a ovel method for eye locatio by meas of a two-level scheme. Locatig the eye by machie-ispectio of a image or video is a importat problem for Computer Visio ad is of particular value to applicatios i biomedical imagig. Our method aims to overcome the sigificat challege of a eye-locatio that is able to maitai high accuracy by disregardig highly variable chages i the eviromet. A first level of computatioal aalysis processes this image cotext. This is followed by object detectio by meas of a two-class discrimiatio (secod algorithmic level).we have tested our eye locatio system usig FERET ad BioID database. We compare the performace of two-level with that of o-level, ad foud it s better performace. Keywords: Biomedical imagig, eye locatio, two-level, image cotext. 1. INTRODUCTION This ito image-guided itervetio therapy is curretly focused o problems of locatig ad recogizig tissues ad objects of iterest durig surgery ad o real-time moitorig of therapy. Imagig techologies have become icreasigly accurate, offerig higher resolutio ad efficiecy ad, as biomedical image aalysis plays a importat role i areas of cliical diagosis, image techology has draw itese from scietists ad physicias [1]. Images of the eye are widely used i the diagosis ad treatmet of various eye diseases such as Diabetic Retiopathy ad glaucoma [2-4]. Diagosis ad treatmet of such eye diseases is greatly assisted by computer aalysis of images to locate the eye ad pupil with high accuracy. Moreover, equipmet to support both such medical diagoses ad studies of huma balace i real-time is uder developmet [5]. The eye is sesitive to cotact with ay exteral aget however, ad diagostic equipmet that touches the eye caot obtai precise data owig to the resistace offered by the eye s reflex. How to circumvet or overcome this is a challege. The modest cotributio Mauscript received November 5, 2007; revised October 30, 2008; accepted November 4, Recommeded by Guest Editor Daiel Howard. This work was supported by the Korea Research Foudatio Grat fuded by the Korea Govermet (MOEHRD, Basic Research Promotio Fud) (KRF D00424). Mi Youg Nam, Xi Wag, ad Phill Kyu Rhee are the Itelliget Techology Laboratory, Dept. of Computer Sciece & Egieerig, Iha Uiversity, Yoghyu-dog, Nam-gu, Icheo , Korea ( s: {rera, wag_xi}@im.iha. ac.kr, pkrhee@iha.ac.kr). * Correspodig author. of this study is a eye locatio method by meas of a two-level scheme. Its iteded use is to assist with the diagosis of eye diseases. I the real world, there is a close coectio betwee objects ad a set of eviromets where those objects are usually foud. Oe ca exploit this coectio to improve the accuracy ad efficiecy of a detectio system. With this i mid, a two-level that will behave i a robust maer uder such variatios of iput image data is proposed here. The algorithm will be addressed i a later sectio. The first level of the proposed scheme cosists of a image cotext aalysis. The image cotext aalysis has two stages with differet objectives: clusterig i the traiig phase ad idetificatio i the testig phase. The mai goal of the secod level of the twolevel classificatio system is to assig the image data ito oe of two categories: the target object, ad everythig else. The total system therefore cosists of cotext clusterig ad object detectio. Cotext clusterig i the first level may be doe by a usupervised learig method such as self orgaizig memory (SOM ) or k-meas clusterig. Cotext idetificatio may be implemeted by a classificatio method such as eural etwork (NN) or K- Nearest Neighbor (K-NN). Idetificatio or detectio i the secod level is a combiatio scheme usig multiple s that aims to produce superiority over schemes usig a sigle i terms of accuracy ad reliability [6]. The multiple s combiatio approach is popular i the literature [9,10]. I the proposed scheme, idividual cadidate s that deped o the output from the first level are activated i parallel. Their task is object detectio. Durig the task of it performs false detectio elimiatio that removes

2 Efficiet Eye Locatio for Biomedical Imagig usig Two-level Classifier Scheme 829 false detectios from the result of each idividual. I the fusio process, it combies output of the s to achieve a optimal classificatio result. The proposed method has bee tested usig two data sets ad their virtual data sets (the FERET ad BioID databases) where facial images are exposed to differet lightig coditios. The proposed system achieves ecouragig experimetal results, with performace that is superior to that of popular alterate methods. The orgaizatio of this paper is as follows. Problem idetificatio, literature review ad back groud work are discussed i Sctio 1. Sectio 2 of this paper itroduces the structure of the proposed eye locatio system usig the experimetal two-level scheme. This is followed with a presetatio of the objective ad makeup of the first-level ad the secod-level (Sctios 3 ad 4). Results are preseted i Sctio 5 showig the performace of the proposed eye locatio system. Fially Sctio 6 cocludes based o our research fidigs. 2. EFFICIENT EYE LOCATION USING TWO- LEVEL CLASSIFIER SCHEME I this sectio, the outlie of the proposed scheme is described. As the title suggests, a two-level scheme cosists of two levels, ad each level has differet objectives ad objects of the classificatio whe applied as part of a eye locatio system. Fig. 1 shows a overview of the eye locatio system with this two-level scheme. The first level performs image clusterig durig traiig ad idetificatio for testig purposes. I traiig, this level aims to cluster of image based o image cotext. I this paper, cotext represets various cofiguratios, dyamic task requiremet, applicatio coditios, evirometal coditios, etc. For the proposed eye-locatio applicatio, chagig illumiatio is the overwhelmigly importat factor. I testig, multiple clusters with characteristics similar to those of the give cotext were used as the cadidate clusters. Multiple clusters were used as cadidates because the method used for idetificatio may ot be sufficietly robust to determie a sigle cluster that is closest to the true situatio. I respose, the system was desiged to be adaptive to the varyig illumiatio coditios. The secod-level is based o the object detectio or two-class method: discrimiate a class of objects from all other backgroud objects. May such object detectors are implemeted with a Support Vector Machie (SVM) [7], Naïve Bayes (NB) [8], ad some other methods based o these, as described i [9,10]. A improved was used Traiig images Testig images Secod-level Feature extractio Feature extractio First-level Cotext-drive Multiple two-class Quasi eye widows detectio Fusio Fial eye locatio Multiple clusters idetificatio Traiig Cluster 1 Cluster 2 Cluster 3 Cluster Traiig Eye model class No-eye model class Fig. 1. The two-level scheme applied to the eye locatio system. as the mai i the secod-level of the proposed system. A multiple udertakes eye detectio. As a cosequece, a sufficiet umber of eye images ad of o-eye images is eeded to trai this. I this task, the varyig illumiatio coditios are the destabilizig factor that must be focused o. The s i the secod level must deped o the multiple clusters cadidates that are obtaied i the first level. I this sese, these s may also be called multiple s. The fial step i this level is optimally fusig the result from each cadidate to obtai the eye locatio. Such a two-level scheme is expected to produce superior performace to that of a sigle scheme i terms of accuracy ad reliability. 3. FIRST-LEVEL CLASSIFIER: CONTEXT- DRIVEN CLUSTERING AND IDENTIFICATION Fig. 2 shows the structure of the first level. I the process of traiig i this first-level, the root ode represets the traiig images ad the child odes represet the image clusters with commo attributes. A large umber of traiig images that typify differet properties are used. I the testig process, the root ode idicates the images that eed testig ad the child odes are images already chose durig traiig. The selectio of a proper cluster that is most likely to produce a accurate output for the curret eviromet of a give image is ext attempted. I traiig, the task of the first level is clusterig,

3 830 Mi Youg Nam, Xi Wag, ad Phill Kyu Rhee First level First level Traiig images Test images Eye sample kmeas K-NN Traiig No-eye sample Quasi eye widows Secod level Optimal eye locatio Secod level Fig. 2. The structure of the proposed first level. which depeds o the image cotext aalysis. A usupervised clusterig algorithm k-meas [11] is adopted so that the proposed system may accout for varyig illumiatio. The procedure follows a simple ad easy method to classify a give data set through a certai umber of clusters that are fixed a priori. The algorithm is composed of the followig steps: A. Place K poits ito the space represeted by the objects that are beig clustered. These poits represet iitial group cetroids. B. Assig each object to the group that has the closest cetroid. C. Whe all objects have bee assiged, recalculate the positios of the K cetroids. D. Repeat steps (b) ad (c) util the cetroids o loger move. This produces a separatio of the objects ito groups from which the metric to be miimized ca be calculated. I testig, the image cotext aalysis assigs detected face images or maually arraged face images ito image categories. Because of complex backgroud or illumiatio coditios, sometimes a image caot be classified ito just oe cluster. I respose to this issue, a strategy of multiple cadidate clusters that collects categories with properties similar to those of the iput image as cadidates is employed. I the proposed eye locatio system, K-earest eighbor [12] is employed for this task. K-earest eighbor is a supervised learig algorithm where the result of a ew istace query is classified based o the majority of K-earest eighbor categories. The purpose of this algorithm is to classify a ew object based o attributes ad traiig datasets. The two-cadidate cluster ad three-cadidate cluster schemes were used for testig, ad the experimetal results ca be foud i Sectio SECOND-LEVEL CLASSIFIER: ADAPTIVE EYE DETECTION I this level, idividual s act with differet thresholds, but with the same priciple Fig. 3. The structure of secod-level. discrimiat. Fig. 3 illustrates the outlie of this secod level. After idetificatio of cadidate-clusters i the first level, the correspodig s are idicated to perform object detectio usig differet thresholds. The outputs i the first level ca be treated as etrace to the secod level. Two classes must be modeled for the. The object class should be modeled first, followed by modelig ofthe o-object class. I the proposed eye locatiosystem, the eye is the target object. A large umber of eye images are extracted from each cluster ad ormalized to pixels. No-eye images are radomly extracted from the cluster. Fig. 4 shows a example of eye ad o-eye image extractio. Blue regios idicate areas that are extracted as eye images ad red regios are extracted as o-eye images Modelig eye ad o-eye classes With respect to this eye locatio system, oe class is the eye class ad the other is the o-eye class. I order to tolerate the chage of illumiatio suitable for varyig eviromets, multiple s are applied i this level. These multiple cadidates s are relative to the cadidate clusters that are decided i the first level. These multiple s ca be described by a ordered triplet data model that is defied as B= (F, E, N), where F={f1, f} is a set of clusters of face images, E={e1, e} is the eye class model for, ad N={1, } is the o-eye class model for the. The eye class model, ei, cosists of eye images extracted from face images i cluster i. The o-eye class model, i, cosists of o-eye images extracted from face images i cluster i. ωei is the posterior probability desity of the eye class of cluster i. It is modeled as a ormal distributio [13]: 1 / 2 1/2 pxω ( ei ) = (2 π )

4 Efficiet Eye Locatio for Biomedical Imagig usig Two-level Classifier Scheme t 1 exp{ ( x Me) e ( x Me)}, (1) 2 where M e ad e are the mea ad the covariace matrix of the eye images respectively. The covariace matrix e ca be factorized by pricipal compoet aalysis (PCA) to give: t e ERE i i i t i i 1 2 = (2) EE = diag{ λ, λ,..., λ }, (3) where E i is a orthogoal eigevector matrix, R i is a diagoal eigevalue matrix with diagoal elemets i.e., eigevalues, i dwidlig order ( λ1 λ2... λ ). A sigificat attribute of PCA is its optimal sigal decompositio i the sese of lowest mea-square error whe oly a subset of pricipal compoets is used to depict the earlier sigal. Vector X specifies the pricipal compoets: t i X = E ( x M ). (4) e The compoets of X are the pricipal compoets. Applyig the optimal sigal decompositio attribute of PCA, oly the first m ( m<< ) pricipal compoets are used to estimate the posterior desity fuctio. Some of the traiig images used to costruct the eye class model are show i Fig. 4. Practically ay image ca be offered as a o-eye sample because the domai of o-eye samples is much wider tha the domai of eye examples. However, selectig a represetative set of o-eye samples is a troublesome task. A ideal situatio would be if the o-eye samples were similar to eyes but ot. The o-eye class modelig starts by extractig o-eye samples that do ot cotai the whole eye regio (see the red regio of Fig. 3). The, represetative o-eye samples are geerated by applyig equatio (4) to the extracted images. Those represetative samples that lie closest to the eye class Cluster 1 Cluster 2 Cluster Fig. 4. Extractio of eye ad o-eye images from cluster traiig data. are chose as modelig samples for the estimatio of the posterior desity fuctio of the o-eye class, which is also modeled i a maer similar to that of the eye class. I Fig. 4 Squares with blue lies are eye regio ad squares with red lies are o-eye regio Discrimiate method i the secod level I order to sigify the presece or absece of a eye, the associated multiple s that have two models made up of a eye model ad o-eye model are applied for eye detectio. The eye class ad o-eye class models have bee costructed as described above. The discrimiate method i secod level employs the Mahalaobis distace istead of the Euclidea distace. The Mahalaobis distace from a group of values with a mea m=(m1,m2,m3,,m) ad covariace matrix for a multivariate vector I=(I1,I2,I3,,I) is defied as[15]: d( x) = ( x m) ( x m). t 1 (5) For the multiple cadidates, let di(x)e be the Mahalaobis distace betwee the patter of the regio of iterest ad the eye class of cluster i, ad di(x) be the o-eye class of cluster i. The distaces di(x)e ad di(x) ca be computed from the iput patter x. The two thresholds θ ad τ are used for classificatio. Their defiitios are show below: θ = max( di( E( x)) e), (6) τ = max( d ( N( x)) d ( N( x)) ). i e i I the above, Exad ( ) N( x ), respectively, are the patters of the traiig sample images of the eye ad o-eye classes separately. The two thresholds are computed durig traiig. The classificatio rule show below is used to detect the eye i the system. We defie the classificatio rule as: ωe if d( x) e < θ ad d( x) e + τ < d( x) x (7) ω otherwise The has some ivariace to positio ad scale, which results i multiple widows aroud both a eye s correct ad false locatios. To address this issue, the ext process focuses o removig the false locatio ad fuses the multiple quasi results ito a optimal eye locatio Decidig the optimal eye locatio The secod stage i the proposed secod level is a resolutio method that decides the optimal locatio of the eye from amog the multiple cadidate locatios. The strategy i this process has two parts: Elimiatio ad Fusio. Elimiatio ivolves the removal of false locatios aroud the eye regio while fusio ivolves mergig the multiple quasi locatios ito the optimal

5 832 Mi Youg Nam, Xi Wag, ad Phill Kyu Rhee locatio that is closest to the real oe Elimiatig false detectio Elimiatio oly aims at the sigle. Note that, i so may output images of a sigle, icorrect detectios (idicated by red rectagles i Fig. 5) ofte occur with less cosistecy. Because of this elicitatio, a elimiatio strategy is devised to remove much false detectio. The elimiatio of false detectio approach is outlied as follow: A. Eye cadidate widows are obtaied i each sigle B. The ceter of the eye cadidate widow is calculated, ad each is spread out with some x, y, scale to form a rectagle C. At each cluster of rectagles, the desity of overlappig spread out rectagles is couted. The cetroids of the rectagles i the cluster are collapsed ito a sigle cetroid. D. The cetroid with a desity higher tha the threshold is preserved. E. Each survivig cetroid has a expaded regio. If there is overlappig betwee widows, the widow with the lower desity will be elimiated. The itersectios of these remaiig regios are saved as quasi eye widows. Size is defied by the average size of the participatig cadidate widows Fusio of multiple detectors To further improve accuracy, multiple s are applied. Each is traied i a similar maer, but with differet traiig coditios based o the first-level. As a result, eve though the detectio results of every idividual for the same image may be quite close, because of differet traiig thresholds, the s will have differet biases ad will make differet errors. This will be used to fuse the output of each i order to obtai higher accuracy. I Fig. 5, a example shows the etire process flow from performig eye detectio with multiple cadidate s to decidig the fial optimal eye positio. I this figure, 1, i, j, ad represet the serial umbers of clusters icluded i the AN. AN represets the set of all clusters. CN idicate the umber of cadidate clusters. I the proposed system, 1 CN 3, ad CN=3 is used as a example. I Fig. 5, red squares preset false eye detectios ad blue squares preset modified eye regios usig elimiatio of false detectio approach. 5. EXPERIMENTS AND RESULTS I order to test the proposed scheme, several sets of experimets were performed. The data for the experimet was take from the FERET database (3816 images) ad BioID (1521 images), 5337 facial images i total. Based o previous research, there are may methods for evaluatig the accuracy of eye locatio [14]. I this paper, a scale-idepedet localizatio measuremet called relative accuracy of eye locatio is adopted to measure the accuracy of eye locatio [14]. This method aims to compare the maually marked eye locatio with the automatically detected eye locatio results from the proposed eye locatio system. C L ad C R are defied as the maually assiged left ad right eye cetroids, C L ad C R are the automatically detected left ad right eye cetroids. D L is the Euclidea distace betwee C L ad C ad R 1 i j k Secod level A cadidate cluster which has similar character to the iput images ( i,j,k N K-NN CN=3 Elimiatio strategy Fusio A associated with the cadidate cluster decided i first-level Fig. 5. A example of the process that decides the optimal eye locatio. C L, D R is the Euclidea distace betwee C R. D is the Euclidea distace betwee the left ad right eyes. The relative accuracy of detectio is defied as follows [14]: max( DL, DR error =. (8) D For each case, the evaluatio criterio of (8) is a measure. I the proposed system, error values uder 0.14 are regarded as acceptace-such a value idicates that the deviatio values are smaller tha 7 pixels o both databases. The performace of sigle usig oly the elimiatio strategy is show i Table 1. From the table, it ca be see that the

6 Efficiet Eye Locatio for Biomedical Imagig usig Two-level Classifier Scheme 833 C L C L D C R C R Fig. 6. A illustratio of the cetroids of the left ad right eyes. sigle performace is isufficiet to meet the requiremets of eye locatio. Accuracy is paramout for a eye locatio system. As show i Table 2, a set of experimets was udertake to compare the performace of a o-level ad the proposed two-level scheme for eye locatio systems. I total, 1093 images were radomly chose from the database ad the cluster umber was varied rage betwee 3 ad 9, which was decided by k- meas based o the illumiatio characters i the proposed system. The acceptace rate of the proposed scheme is obviously much better tha for the olevel scheme. From the table it ca be see that although the average acceptace rate is highest whe the traiig data is classified ito ie clusters, ot every cluster achieves better performace. Overall, based o the aalysis of Table 2, it is cocluded that the performace is best whe the data cotext category is 3. I order to further improve accuracy, multiple cadidate clusters were employed istead of a sigle cadidate cluster i the first level as the etrace ito the secod level. Owig to the complex eviromet, it is usually hard to idetify the optimal cadidate, i.e., whe a test image is iput, more tha oe cluster is idetified as fittig the iput image. Table 3 demostrates the acceptacerate amog differet umbers of cadidate clusters. From these results, it seems the optimal outcome ca be obtaied whe three cadidate clusters are employed. Table 1. Performace of a sigle with elimiatio detectio. Source Images Accepted faces False Detects Acceptace rate FERET % BioID % Total % Table 2. Eye locatio compariso betwee the Nolevel ad Two-level s. Data cotext category Six-cluster Threecluster Niecluster cluster Images (total 1093) No-level method 6. CONCLUSIONS Two-level based eye locatio Cluster % 95.85% Cluster % 96.90% Cluster % 96.85% Average 95.70% 96.61% Cluster % 94.34% Cluster % 97.70% Cluster % 97.18% Cluster % 95.00% Cluster % 94.74% Cluster % 98.20% Average 94.97% 96.71% Cluster % 87.50% Cluster % 91.40% Cluster % 91.67% Cluster % 97.50% Cluster % 89.47% Cluster % 97.59% Cluster % 98.40% Cluster % 96.30% Cluster % 98.97% Average 94.69% 97.07% Table 3. Eye locatio performace compariso with differet umbers of cadidate clusters. Source Images FERET 3816 Classified ito sigle cadidate cluster Classified ito two cadidate clusters Acceptace rate Classified ito three cadidate clusters 89.54% 95.67% 97.56% BioID % 96.83% 97.29% A two-level scheme for a eye locatio system i Computer Visio has bee preseted i this paper. The objective of the first-level is clusterig step with idetificatio ivolvig traiig ad testig. The goal of the secod-level is detectio ad locatio of the eye. K-meas was used for image clusterig to accout

7 834 Mi Youg Nam, Xi Wag, ad Phill Kyu Rhee for various illumiatio levels ad K-NN was used to idetify the multiple cadidate clusters that offer a fit to the iput test image. It was observed that usually oly oe relative fit cluster is chose for a test image. As is well kow, complex coditios always cause some cofusio. It is ot possible to simply pick the best cadidate to fit the iput. Therefore, havig multiple cadidate clusters is hady. These clusters are the output of the first level as well as the iput of secod level. I this secod level, the associated multiple s work towards object detectio. Moreover, a elimiatio strategy was used i each sigle cadidate to remove the false detectio ad i order to further improve the accuracy. A fusio strategy was also used for combiig the results after elimiatio. A optimal eye locatio is obtaied for a more accurate ad efficiet eye locatio system uder varyig eviromets by this process. A set of experimets was preseted to verify the strategy. The tables show the compariso of acceptace rates betwee usig this proposed twolevel scheme ad usig a o-level scheme for eye locatio i various illumiatio settigs. The results provide evidece that whe this scheme is icorporated i the proposed system, overall eye locatio performace is optimized. I additio, sice the proposed scheme shows good performace, it is postulated that it ca be used i the locatio of other tissues or i idetificatio tasks for improved precisio i various domais of applicatio i biomedical imagig. REFERENCES [1] K. Noroha, J. Nayak, ad S. N. Bhat, Ehacemet of retial fudus Image to highlight the features for detectio of abormal eyes, Proc. of TENCON 2006, IEEE Regio 10 Coferece, [2] F. Zaa ad J. C. Klei, A multimodal registratio algorithm of eye fudus images usig vessels detectio ad hough trasform, IEEE Tras. o Medical Imagig, vol. 18, o. 5, pp , May [3] Z. B. Sbeh ad L. D. Cohe, A ew approach of geodesic recostructio for druse segmetatio i eye fudus images, IEEE Tras. o Medical Imagig, vol. 20, o. 12, pp , December [4] K. P. White, Jr, Modelig huma eye behavior durig mammographic scaig: Prelimiary results, IEEE Tras. o Systems, Ma, ad Cyberetics-Part A, vol. 27, o. 4, pp , July [5] M. V. Figueira, D. F. G. de Azevedo, T. Russomao, C. A. Zaffari, ad M. F. da Rocha, Improvemets o a fast algorithm for real time eye movemet quatificatio, Proc. of the 28th IEEE EMBS Aual Iteratioal Coferece, New York City, USA, pp , Augest [6] L. Kucheva ad L. C. Jai, Desigig fusio systems by geetic algorithms, IEEE Tras. o Evolutioary Computatio, vol. 4, o. 4, pp , September [7] P. Shih ad C. Liu, Face detectio usig discrimiatig feature aalysis ad support vector machie, Patter Recogitio, vol. 39, o. 2, pp , February [8] V. B. Berikov, A approach to the evaluatio of the performace of a discrete, Patter Recogitio Letters, vol. 23, o. 1-3, pp , Jauary [9] R. R. Yager, A extesio of the aive, Iformatio Scieces, vol. 176, o. 5, pp , March [10] Y. Li, S. Gog, J. Sherrah, ad H. Liddell, Support vector machie based multi-view face detectio ad recogitio, Image ad Visio Computig, vol. 22, o. 5, pp , May [11] S. J. Redmod ad C. Heegha, A method for iitialisig the K-meas clusterig algorithm usig kd-trees, Patter Recogitio Letters, vol. 28, o. 8, pp , Jue [12] F. Perkopf, etwork s versus selective k-nn, Patter Recogitio, vol. 38, o. 1, pp. 1-10, Jauary [13] C. Liu, A discrimiatig features method for face detectio, IEEE Tras. o Patter Aalysis ad Machie Itelligece, vol. 25, o. 6, pp , [14] O. Jesorsky, K. Kirchberg, ad R. Frischholz, Robust face detectio usig the Hausdorff distace, AVBPA2001, LNCS 2091, pp, 90-95, 6-8 Jue [15] K. Youis, M. Karim, R. Hardie, J. Loomis, S. Rogers, ad M. DeSimio, Cluster mergig based o weighted mahalaobis distace with applicatio i digital mammograph, Proc. of IEEE Aerospace ad Electroics Coferece, pp , Mi Youg Nam received the B.Sc. ad M.Sc. degrees i Computer Sciece from the Uiversity of Silla Busa, Korea i 1995 ad 2001 respectively ad the Ph.D. degree i Computer Sciece & Egieerig from the Uiversity of Iha, Korea i Curretly, She is Post-Doctor course i Itelliget Techology Laboratory, Iha Uiversity, Korea. She s research iterest icludes Biometrics, Patter Recogitio, Computer Visio, Image Processig.

8 Efficiet Eye Locatio for Biomedical Imagig usig Two-level Classifier Scheme 835 Xi Wag received the M.S. degree i Iformatio Egieerig from Chegdu Uiversity of techology i Her research iterests iclude imagig processig, object detectio, ad system idetificatio. Phill Kyu Rhee received the B.S. degree i Electrical Egieerig from the Seoul Uiversity, Seoul, Korea, the M.S. degree i Computer Sciece from the East Texas State Uiversity, Commerce, TX, ad the Ph.D. degree i Computer Sciece from the Uiversity of Louisiaa, Lafayette, LA, i 1982, 1986, ad 1990 respectively. Durig he was workig i the System Egieerig Research Istitute, Seoul, Korea as a research scietist. I 1991 he joied the Electroic ad Telecommuicatio Research Istitute, Seoul, Korea, as a seior research staff. Sice 1992, he has bee a Associate Professor i the Departmet of Computer Sciece ad Egieerig of the Iha Uiversity, Icheo, Korea ad sice 2001, he is a professor i the same departmet ad uiversity. His curret research iterests are Patter Recogitio, Machie Itelligece, ad Parallel Computer Architecture. Dr. Rhee is a member of the IEEE Computer Society ad KISS (Korea Iformatio Sciece Society).