An Automatic Eye Detection Method for Gray Intensity Facial Images

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1 272 An Automatc Eye Detecton Method for Gray Intensty Facal Images M. Hassaballah 1,2, Kenj Murakam 1, Shun Ido 1 1 Department of Computer Scence, Ehme Unversty, , Japan 2 Department of Mathematcs, Faculty of Scence, South Valley Unversty, Qena, 83523, Egypt Abstract Eyes are the most salent and stable features n the human face, and hence automatc extracton or detecton of eyes s often consdered as the most mportant step n many applcatons, such as face dentfcaton and recognton. Ths paper presents a method for eye detecton of stll grayscale mages. The method s based on two facts: eye regons exhbt unpredctable local ntensty, therefore entropy n eye regons s hgh and the center of eye (rs) s too dark crcle (low ntensty) compared to the neghborng regons. A based on the entropy of eye and darkness of rs s used to detect eye center coordnates. Expermental results on two databases; namely, FERET wth varatons n vews and BoID wth varatons n gaze drectons and uncontrolled condtons show that the proposed method s robust aganst gaze drecton, varatons n vews and varety of llumnaton. It can acheve a correct detecton rate of 97.8% and 94.3% on a set contanng 2500 mages of FERET and BoID databases respectvely. Moreover, n the cases wth glasses and severe condtons, the performance s stll acceptable. Keywords: Eye detecton, Irs detecton, Facal features extracton, Face detecton, Entropy. 1. Introducton Automatc face recognton has attracted sgnfcant attenton n mage analyss and understandng, computer vson, pattern recognton, securty system, and credtcard verfcaton for decades [1,2]. Several face recognton systems are based on basc facal features such as eyes, nose and mouth, and ther spatal relatonshp. For most 2D and 3D recognton algorthms, t s crtcal that faces be algned before beng compared. Typcally, algnment begns wth the detecton of facal features. Statstcal-based face recognton systems such as egenface [3] or ndependent component analyss method [4] use eye corners for algnment. Also, n order for face recognton algorthms-based on geometrc, that use the overall geometrcal confguraton of the facal features, to work well, facal features should be detected before any other processng can take place [5]. Among these facal features, eyes reman the most mportant one because they can be consdered salent and relatvely stable features on the face n comparson wth other facal features. So detecton of eyes wll be the frst step n a face recognton system. On the other hand, some works consder that the postons of other facal features can be estmated usng the eye postons [6]. A bref revew of exstng eye detecton methods s gven n the next secton. The rest of ths paper s organzed as follows. A bref revew on exstng eye detecton methods s presented n Secton 2. The proposed method for detecton of the center of two eyes s ntroduced n Secton 3. Expermental results are reported n Secton 4 and fnally, the conclusons and future research are gven n Secton Bref revew on the exstng eye detecton methods Detecton of the human eye s a very dffcult task because the contrast of the eye s very poor. Eye detecton s dvded nto two categores; eye contour detecton [7], [9] and eye poston detecton [3], [6]. Ths paper focuses on the second type;.e., eye poston detecton, as most algorthms for eye contour detecton such as those are based on the deformable template [6] requre the detecton of eye postons to ntalze eye templates. Thus, eye poston detecton s mportant not only for face recognton but also for eye contour detecton. Several eye detecton methods have been developed n the last ten years. Deformable template [10], [11] s the popular method n locatng the human eye. In ths method, an eye model s frst desgned and the eye poston can be obtaned through a recursve process. However, ths method s feasble only f the ntal poston of the eye model s placed near the actual eye poston. Moreover, deformable template suffers from two other lmtatons.

2 273 Frst, t s computaton expensve. Second, the weght factors for energy terms are determned manually. Improper selecton of the weght factors yelds unexpected results. Lam and Yan [12] extended Yulle's work [11] by ntroducng the concept of eye corners, whch proved to be effectve n reducng the processng tme. In the template matchng aspect, Ryu and Oh [13] propose an algorthm based on egenfeatures and neural networks for the extracton of eyes usng rectangular fttng from gray-level face mages. The advantage s that t does not need a large tranng set by takng advantage of egenfeatures and sldng wndow. However, ther algorthm can fal on the face mages wth glasses or beard. It was tested on a small set of 180 mages only from ORL database and ts best performance s 91.7% and 85.5% for left and rght eye respectvely. Pentland et al. [6] use the egenspace method to detect the eyes. The egenspace method shows better eye detecton performance than a smple template matchng method snce tranng samples cover dfferent eye varatons n appearance, orentaton and lghtng condtons. But, ts detecton performance s largely dependent on the choce of tranng mages. Another drawback s that, t requres the tranng and test mages to be normalzed n sze and orentaton. Hough transform s also wdely used eye detecton method. It s based on the shape feature of an rs and often works on bnary valley or edge maps and t does not requre an mage of a specfc person's eye for the eye model. The shortcomng of ths approach s that ts performance depends on the threshold values selected for the bnarzaton of valley or edge maps and t s dffcult to detect the crcle correspondng to the rs unless the lkely regon of occurrence of the rs s narrowed down, snce the rs s smaller than the face. Usng Hough transform and deformable template technque, Chow and L [14] propose a method of detectng the lkely rs regon. Frst, a valley mage s gven, consstng of the dfference between the orgnal mage n gray scale and an mage to whch the closng operaton of gray-scale morphology s appled to the orgnal mage. Then, the valley regon s detected by bnarzng the valley mage. The succeedng components of the valley mage are approxmated by rectangles. Then, two rectangles correspondng to the eyes are selected by usng ther postonal relatonshp. Unfortunately, correct selecton of the two rectangles requres that the left and rght eyes be n separate rectangles, that the whole of each eye be enclosed by a sngle rectangle, and that each eye and eyebrow be n a separate rectangle. Therefore, t s dffcult to determne the threshold value for bnarzaton of the valley mage. Kawaguch and Rzon [15] detect the rs usng the ntensty and the edge nformaton. Ther method frst detects the face regon n the mage, and then extracts ntensty valleys from the face regon. Next, t extracts rs canddates from the valleys usng the feature template and the separablty flter. Fnally, usng cost functon, a par of rs canddates correspondng to the rses s selected. The costs are computed by usng Hough transform, separablty flter and template matchng. To evaluate the valdty of ther method, they use mages from two databases; the Bern and AR database. The method acheves a correct rs detecton rate of 95.3% for 150 Bern face mages and 96.8% for 63 AR mages. But they do not explan how to automatcally detect the lght dot n the rs. Besdes these three classcal approaches, recently other eye detecton methods have been proposed. In [16], a method s proposed for eye detecton that uses rs geometres to determne the regon canddates whch possbly contan the eye, and then the symmetry for selectng the couple of eyes. Ehsan et al. [17] present a rotaton-nvarant facal feature detecton system based on combnng the Gabor wavelet and the entropy measure. One advantage of ther method s that t can be traned for any ndvdual facal feature usng a small set of sample mages. Song et al. [18] use the bnary edge mages and ntensty nformaton to detect eyes. Ther method conssts of three steps: frst extracton of bnary edge mages (BEIs) from the grayscale face mage based on mult-resoluton wavelet transform, second extracton of eye regons and segments from BEIs, and thrd eye localzaton based on lght dots and ntensty nformaton. A correct eye detecton rate of 98.7% and 96.6% may be acheved on 150 Bern and 564 AR mages, respectvely. Though ths hgh detecton rate, ths method depends bascally on dfferent types of thresholds on dfferent database. So the method s nether smple nor applcable. Cho and Km [19] propose an eye detecton method usng the Modfed Census Transform (MCT)-based pattern correlaton. The method detects two eyes by the MCTbased AdaBoost eye detector over the eye regons. To reduce the falsely detected eyes due to the lmted detecton capablty of the eye detector, they propose a method for eye verfcaton that employs the MCT-based pattern correlaton map. They verfy whether the detected eye patch s eye or non-eye dependng on the exstence of a notceable peak. When one eye s correctly detected and the other eye s falsely detected, the method can correct the falsely detected eye usng the peak poston of the correlaton map of the correctly detected eye. The method acheves detecton rate of 98.7% and 98.8% on the Bern and AR-564 databases, respectvely. Zhou and Geng [20] extend the dea of the ntegral projecton functon (IPF) and varance projecton functon (VPF) [7] to the generalzed projecton functon (GPF) and showed wth expermental results that the hybrd projecton functon (HPF), a specal case of GPF, s better

3 274 than VPF and IPF for eye detecton. Although the detecton rate of ths method on BoID database s 94.81%, t bascally requres that each eye should be n a separated wndow. Ths depends on detecton of the rough eye poston whch s not trval process. On the other hand, Peng et al. [21] combne the two exstng technques feature based method and template based method to overcome ther shortcomngs. The method frstly makes use of feature based methods to detect two rough regons of eye. The precse locatons of rs centers are then detected by performng template matchng n these two regons. When t s tested on 227 mages from ORL face database wthout glasses, t gves 95.2% detecton rate. In spte of consderable amount of prevous work on the subject, detecton of eye features wll remans a challengng problem and there s stll a long way to go before the methods become really mature [22,23]. 3. The proposed method In ths paper, the entropy s used to detect facal feature ponts such as eyes. In the eye regons the PDF (probablty dstrbuton functon) of gray scale ntenstes s flatter, whch ndcates that pxel values are hghly unpredctable and ths corresponds to hgh entropy. On the other hand, n the other regons the PDF s peaked, whch means that most of these pxels are hghly predctable and hence entropy s low. To show ths pont clearly, sx dfferent regons of the top half of the face and ther correspondng PDF (An ntensty hstogram n ths paper) are depcted n Fg. 1. The two eyes regons (wndows) (b) and (f) exhbt unpredctable local ntensty ndcatng that flatter of PDF and hence entropy s hgh, whle n the other areas such as (c) or (d) the PDF s peaked and therefore low entropy. From Fg. 1, one can note that entropy value of eye regons (b) and (f) s and whle entropy value of other regons (c) and (d) s and respectvely. Ths fact can be used to detect the facal feature such as eyes. 3.1 Entropy Suppose that there exsts a set of events S = { x 1, x1,... xn }, wth the probablty of occurrence of each event p( x ) p. These probabltes, P p, p,..., p } are such that each p 0 { 1 2 n, and the probablty dstrbuton functon (PDF) satsfes that n 1. For measurng the uncertanty and 1 p unpredctablty of a set of events S, Shannon ntroduced an mportant concept whch s the entropy n the form H(S) = H p, p,..., p ) = - ( 1 2 n n 1 p( x )log 2 p( x ) (1) A good measure for uncertanty should have some propertes; contnuous, a strctly convex functon, whch reaches a maxmum value when all probabltes are equal, and maxmzed n a unform probablty dstrbuton context. Because entropy satsfes these propertes, we chose t to measure the uncertanty of eye regon. The Shannon entropy can be computed for an mage, where the probabltes of the gray level dstrbutons are consdered n the Eq. (1). A probablty dstrbuton of gray values can be estmated by countng the number of tmes each gray value occurs n the mage or sub-mage and dvdng those numbers by the total number of occurrences. An mage consstng of a sngle ntensty wll have a low entropy value; t contans very lttle nformaton. Fg. 1: The PDF and entropy value of sx dfferent regons of eye area. 3.2 Irs detecton The eye features nclude eye center (pupl or rs), eye corners and eyeld contours. Ths work wll focus on eye center detecton or rs detecton. To detect the eye center (rs), the above fact of unpredctable gray ntensty n small wndow of sze w x h pxel around rs and the fact that the rs s dark wll be used. The flowchart of the proposed method s shown n Fg. 2. Frst, the face regon s extracted from the nput gray scale mage by applyng the Boosted Cascade Face Detector due to Vola and Jones [24]. Ths algorthm utlzes a boostng method known as AdaBoost to select and combne a set of features, whch can dscrmnate between face and non-face mage regons. The detector s run over a test mage and mage wndow wth the hghest face calculated by summng the classfer s from each level of the

4 275 cascade deemed to be the locaton of the face n the mage. Second the top part of detected face s scanned wth overleaped small wndows of sze w x h pxels (Fg. 2(c)) to fnd eye regon. Therefore the total number of wndows wll be large (say M), for more llustratve a few number of these wndows s drawn n Fg. 2(c), calculate entropy value for each wndow usng Eq. (1), the hghest entropy value wndows should be around the rs because as mentoned n secton 3.1 n ths area the varaton of pxels s hgh so the entropy wll also be hgh. Then, we chose only n wndows that have hghest entropy value from all these M wndows and exclude the other wndows (M-n) as shown n Fg. 2(d). Entropy alone (Eq. (1)) s not enough to detect rs or the wndow whch contans rs from these chosen n hghest entropy value because t measures the varaton of pxels values n the canddate regon not regon features. In other words entropy help us to select n wndows around rs, one of these wndows contans the rs (dark crcle). So, other cues are requred to select only one wndow W from these n wndows whch wll be the rs. To do ths, entropy and darkness of the rs are combned together. We consder the fact that the rs s crcle and dark, and calculate the sum of ntensty pxel value n a crcle of radus r around the center of each wndow W (.e., the center of ths crcle s the center of the wndow). Based on the entropy value and ths sum of ntensty pxel value, a total s gven to each wndow. Ths s as follow T where entropy, and H C (2) T s the total of each wndow, H C s the of rs darkness; 2(e). The center of the selected wndow s the requred pont (see Fg. 2(f)). In some cases, few hghest entropy value wndows are away from the rs, may be at eyebrow or near the edge of scanned area as shown n Fg. 1 (a,e), but these regons are not crcle or dark around the center, only rs s crcle and dark (Fg. 1(b,f)) whch means that darkness and crcle s a unque feature for the wndow that contans the rs among all the other wndows. So the dea here s to select the wndow whch has hgh entropy value and s dark around the center. Accordng to that, these wndows do not affect too much on the performance of the proposed method. Eq. (2) can be consdered as open research problem, now ths method guarantees that 99% of hghest entropy value wndows are around rs of eye (Fg. 2(d)). In ths work, darkness of rs cue s used n Eq. (2) to gude for the correct wndow; other cues may outperform our darkness cues. The most advantage of the proposed method s that, t s smple and can be mplemented easy because t dose not requre complcated pattern matchng or a predefned threshold. 3.3 Selectng of wndow sze Choosng the wdth and heght of wndows s mportant. Fg. 3 shows two examples for selectng the sze of wndow. If the wdth and heght are chosen as n case (a) the role of entropy n Eq. 2 wll dsappear, because n ths case there s not any knd of varaton n ntensty but only dark pxels. On the other hand case (b) wll guarantee the ntensty varaton and hence hgh entropy value. H C n Entropy( W ) 1 1 Entropy( W ) Darkness( W ) n 1, and Darkness( W ) where Entropy (W ) and Darkness (W ) are calculated for each wndow W, ( =1,, n) usng Eq. (1) for Entropy (W ) whle Darkness (W ) s calculated n a crcle of radus r around the center of a wndow usng sum of ntensty pxels value. Fnally, accordng to Eq. (2), the eye regon s the wndow that has the hghest total T, ths wndow contans the rs as shown n Fg. (3) (a) Bad wndow sze (b) Good wndow sze Fg. 3: Examples of selectng wndow sze. A Geometrc eye model s used to optmze wndow sze as shown n Fg. 4, the eye regon wdth d s consdered to be equal ¼ of face wdth. Then 2r=d/3, where r s radus of rs. Therefore the wdth w and heght h of the wndow can be determne usng the formula, w 2 r x, and h 2 r y (4) As ths model s not 100% accurate and we need to avod wndow sze of case (a) n Fg. 4 (a), small value x, y 1 s added to the formula. For example, n ths work we use faces of sze 128x128 pxels, therefore eye wdth d=128/4= 32, then the rs radus 2r =32/ So we emprcally choose x =3.3 and

5 276 y 1.3. In ths way, the wndow sze s adapted to 14 x 12 pxels, wth overlap or shfted 2 pxels n horzontal and vertcal drectons, and radus of crcle to 5, whle the number of wndows whch have hghest entropy value n s chosen to be 50 wndows. (b) Face extracton (c) Scannng top part (d) n wndows wth hgh entropy (a) Input mage (f) Centers of two (e) Wndows wth hghest Fg. 2: Flowchart of the proposed eye detecton method. wdth Fg. 4: Geometrc eye model. 2r=d/3 d=wdth The BoID face database s also a head-and-shoulder mage face database. However, t stresses real world condtons. Sample mages from BoID database are shown n Fg. 5. The BoID face database features a large varety of llumnaton and face sze. Background of mages n the face database s very complex. The mages were recorded durng several sessons at dfferent places. The database conssts of 1521 frontal vew gray level mages of 23 dfferent test persons wth a resoluton of pxel. 4. Expermental results 4.1 Data sets The proposed method s bascally tested on two face databases. One s a subset of the FERET database [25] and the other s the BoID face database [26]. A subset of 2500 face mages (fa, hl, hr, fb) was randomly selected from the FERET database. Where fa s regular frontal mage, hl half left- head turned about 67.5 degrees left, hr half rghthead turned about 67.5 degrees rght, and fb alternatve frontal mage, taken shortly after the correspondng fa mage. Images n ths database are color of 256 x 384 pxels, and before used they are converted to 8-bt gray level mages. The mages prmarly contan an ndvdual's head, neck and shoulder. There are nearly no complex background n these mages. Fg. 5: Sample mages of BoID database wth complex background. 4.2 Evaluaton crteron of the results To quanttatvely assess and farly compare the methods that am at addressng the eye detecton or face localzaton, algorthms should be tested on the same benchmark dataset accordng to a standard testng procedure. Unfortunately, such a requrement s seldom satsfed n practce. Moreover, a unversal objectve measure for evaluatng eye detecton or face localzaton methods dose not exst [27]. Although numerous

6 277 algorthms have been developed, most of them have been tested on dfferent datasets n a dfferent manner. In ths paper to evaluate the performance of the proposed method, the crteron of [28] s used. The crteron s a relatve error measure based on the dstances between the expected and the estmated eye postons. Let C l and C r be the manually (ground-truth) extracted left and rght eye postons of a face mage, C ~ l and C ~ r be the estmated postons by the eye detecton method, d l be the Eucldean dstance between C l and C ~ l, d r be the Eucldean dstance between C r and C ~ r, and d lr be the Eucldean dstance between C l and C r. Then the relatve error of ths detecton s defned as large varety of llumnaton and gaze drectons exsted n these mages, the performance of our method s reasonable; n ths case the detecton rate s 94.3%. Fg. 8 shows some samples for whch ths method success to detect the two eyes, the frst and second rows show the robustness of ths method aganst gaze drecton, whle the thrd and fourth rows show ts robustness aganst varety of llumnaton. The dstrbuton functon of the relatve error aganst successful detecton rate for ths test s drawn n Fg. 9 (a), one can see that when the relatve error Rerr = 0.15, the detecton rate s 94.1 %. R err max( d, d ) l r (5) dlr If Rerr < 0.25, the detecton s consdered to be correct. Notce that Rerr =0.25 means the bgger one of d l and d r roughly equals half an eye wdth. Therefore, for a face database comprsng N mages the detecton rate s defned as R N 1 N 100, R err (6) 4.3 Results and dscussons 0.25 Ths secton presents the expermental results of the proposed method. Frst the method s tested on 2500 mages of FERET database; examples of successful detecton of ths test are shown n Fg. 6. From these results one can note that the method can detected eye center accurately form frontal and non frontal vew mages even f these mages are occluded by glasses. Fg. 7 depcts the dstrbuton functon of the relatve error aganst successful detecton rate, our method acheves 97.8 % eye detecton rate when the relatve error s equal to Recently, some works [18] consder the crteron Rerr < 0.25 s very loose and may not be very sutable when the detected eye postons are used for face normalzaton, the method gves 96.7% successful detecton rate at Rerr =0.15, whch means that the proposed method s stll effcent. Second the proposed method s tested on BoID database. As mentoned before ths database features a large varety of llumnaton, gaze drectons, and face sze. Though the Fg. 6: Examples of FERET mages for whch two eyes are correctly detected. Successful Detecton Rate % Relatve error Fg. 7: Relatve error versus detecton rate for FERET mages. For a thorough quanttatve analyss of the performance of the method n the case of mages wth glasses, 150 mages wth glasses of BoID are chosen randomly. The detecton rate n ths case s 92.4 %, whch s less than the case where the mages are wthout glasses. It s also shown n Fg. 9 (b) that when relatve error Rerr s 0.15 the successful detecton rate s 89.2 %. Ths low detecton rate s due to the dffculty of these mages and reflecton of lght near to rses. Samples of the successful detected eyes are shown n Fg. 10.

7 278 Fg. 8: Examples of BoID mages for whch two eyes are correctly detected. Successful Detecton Rate % Relatve error (a) Successful Detecton Rate % Relatve error (b) Fg. 9: Relatve error versus detecton rate for BoID mages, (a) wthout glass, (b) wth glass. Fg. 10: Examples of BoID mages wth glass for whch two eyes are correctly detected.

8 279 The performance of the method on FERET mages s better than on BoID mages, because the BoID face database s beleved to be more dffcult than FERET and other commonly used head-and-shoulder face database wthout complex background. For example, when the same detecton method and evaluaton crtera were appled to both XM2VTS and BoID face databases, the successful detecton rates are 98.4% and 91.8%, respectvely [28]. Some examples of the mages for whch the method faled to correctly detect rses are shown n Fg. 11 and Fg. 12 for FERET and BoID database respectvely. The false detecton s manly due to some reasons; shad, eyes are almost closed and therefore the rs s hdng, glsten of glasses on eyes, the frame of glass s black and too wde whch n turn can acheve the unque feature of darkness and crcle around the center of wndow and hence gude to wrongly selecton of ths wndow, or the mage s too dark to dscrmnate eyes from other parts. Fg. 11: FERET mages for whch eyes are wrongly detected. turn weaks the role of second part n Eq. 2, snce ths part measures the darkness of rs n a crcle regon. Therefore the performance of the method s reduced to 84.2% when the relatve error Rerr s Fg. 14 shows some examples of these severe condtons mages for whch the proposed method can correctly detect both eyes. The dstrbuton functon of the relatve error aganst successful detecton rate for ths test s drawn n Fg. 15. Generally, because the proposed method depends on darkness of rs (.e., second part n Eq. 2), we can conclude that n mages wth severe condtons lke reflecton on the surface of eyeglasses ths method can fal n the cases where the rs s not dark for any reason or totally occluded by glsten of glasses or eyeld. Examples of fal due to these reasons are shown n Fg. 16. Fnally, t s reasonable that hgh successful detecton rate of a certan algorthm should be on a large number of mages not on a few number such as 150 Bern database or 63 AR mages. Table 1 compares the detecton performance of varous eye detecton methods aganst the total number of used mages to test these methods. Although, these methods worked well they were tested on a small number of database mages. It s clear that the proposed method s the only method that gves acceptable detecton rate on 4000 mages. The average calculaton tme to detect the two eyes center-pont s 30 ms on a PC of PIII 1GB, 256 Ram, and OS wndows XP. 5. Conclusons Fg. 12: BoID mages for whch eyes are wrongly detected. In order to verfy that the proposed method s stll robust f t s used on other dfferent mages wth dfferent condtons than FERET or BoID mages, we tested t on mages for persons of Georga Insttute of Technology face database. Each person has 15 mages of frontal and/or tlted faces wth dfferent facal expressons, lghtng condtons and scale. Fgure 13 shows that ths method works well under varous condtons. One mage of each person was lost due to the used face detecton method and three only (rght bottom) of 28 mages are faled to detect the rses correctly usng our method. The frst two are faled to detect correctly because the glass frame covers or hdes the rs and the last one the eye s closed so the rs can not be seen clearly. The contrbuton of the proposed method to mages wth severe condtons s also studed separately. For ths purpose 260 mages wth severe condtons such as reflecton on the surface of eyeglasses, rs occluson by eyeld or sleepng, shade, and lghtng condtons are collected from dfferent resources. The dffculty n these mages leads to hde the rs partally or totally whch n Ths paper ntroduced an effcent method to detect the eye s center-pont. Ths method s based on two fact: frst eye regon exhbt unpredctable local ntensty, whch means that pxel values are hghly unpredctable and ths corresponds to hgh entropy compared to other regons. Second, eyes (rs) are crcle and dark. A total based on the hgh entropy and darkness of the rs s gven to rectangle regons of fxed sze, the hghest regon s consdered to contan the rs. The proposed method s tested on the BoID and a subset of FERET databases. It shows that a correct eye detecton rate of 94.3% and 97.8% can be acheved on BoID and FERET, respectvely. These two datasets mages are combned wth other mages of dfferent databases to create a set of more than 4000 mages and we tested the method on ths number of mages. It gves average correct eye detecton rate of 96.2%. The proposed method along wth a robust face detecton method can be effectvely used n real-tme applcatons because t s very smple and works well under varous condtons than other exstng eye detecton methods.

9 280 Fg. 13: Testng the proposed method on two person s mages of Georga Tech face database. Fg. 14: Examples of mages wth severe condtons for whch eyes are correctly detected.

10 281 References Fg. 15: Relatve error versus detecton rate for the set of mages wth severe condtons. Fg. 16: Examples of mages wth severe condtons for whch eyes are wrongly detected. Table 1: Comparson of varous eye detecton methods aganst number of used mages. Method Total number Detecton of used mages Rate Proposed method % Zhou and Geng [20] % Cho and Km [19] % Song and Lu [18] % Kawaguch and Rson [15] % Peng et al. [21] % Acknowledgments The authors would lke to thank the Egyptan Mnstry of Hgher Educaton (Msson Department) for supportng ths work under Grant No.1/13/ Portons of the research n ths paper use the FERET database of facal mages collected under the FERET program sponsored by the DOD. [1] X. Tan, S. Chen, Z-H. Zhou, F. Zhang, Face recognton from a sngle mage per person: A survey, Pattern Recognton, Vol. 39, pp , [2] W. Zhao, R. Chellappa, P. J. Phllps and A. Rosenfeld, Face Recognton: A Lterature Survey, ACM Computng Surveys, Vol. 35, No. 4, pp , [3] A. Pentland, B. Moghaddam, T. Starner, Vew-based and modular egenspaces for face recognton, In Proc. of IEEE Internatonal Conference on Computer Vson and Pattern Recognton, IEEE Computer Socety Press, USA, pp ,1994. [4] B. A. Draper, K. Baek, M. S. Bartlett, and J. R. Beverdge, Recognzng faces wth PCA and ICA, Computer Vson and Image Understandng, Vol. 91, pp , [5] W. Janxn, Z.-H. Zhou, Effcent face canddates selector for face detecton, Pattern Recognton, Vol. 36, pp , [6] R. Brunell, T. Poggo, Face recognton: features versus templates. IEEE Transacton on Pattern Analyss and Machne Intellgence, Vol. 15, No. 10, pp , [7] G.C. Feng, P.C. Yuen, Varance projecton functon and ts applcaton to eye detecton for human face recognton, Pattern Recognton Letters, Vol. 19, pp , [8] K. H. Mohammad, R. Safabakhsha, Human eye sclera detecton and trackng usng a modfed tme-adaptve self-organzng map, Pattern Recognton, Vol. 41, No. 8, pp , [9] Z. Zheng, J. Yang, L. Yang, A robust method for eye features extracton on color mage, Pattern Recognton Letters, Vol. 26, pp , [10] D. Beymer, Face recognton under varyng poses, In Proc. of IEEE Internatonal Conference on Computer Vson and Pattern Recognton, IEEE Computer Socety Press, USA, pp , [11] A.L. Yulle, P.W. Hallnan, D.S. Cohen, Feature extracton from faces usng deformable templates, Internatonal Journal of Computer Vson, Vol. 8, No. 2, pp , [12] K.M. Lam, H. Yan, Locatng and extractng the eye n human face mages, Pattern Recognton, Vol. 29, No. 5, pp , [13] Y.-S. Ryu, S.-Y. Oh, Automatc extracton of eye and mouth feld from a face mage usng egnfeatures and multlayer perceptrons, Pattern Recognton, Vol. 34, pp , 2001 [14] G. Chow, X. L, Toward a system for automatc facal feature detecton, Pattern Recognton, Vol. 26, No. 12, pp , [15] T. Kawaguch, M. Rzon, Irs detecton usng ntensty and edge nformaton, Pattern Recognton, Vol. 36, pp , [16] T. D'Orazo, M. Leo, G. Ccrell, A. Dstante, An algorthm for real tme eye detecton n face mages. 17th Internatonal Conference on Pattern Recognton, Aug. Cambrdge, UK, Vol. 3, pp , 2004.

11 282 [17] F. Ehsan and John S. Zelek, Rotaton-Invarant facal feature detecton usng gabor wavelet and entropy, LNCS 3656, pp , [18] J. Song, Z. Ch, J. Lu, A robust eye detecton method usng combned bnary edge and ntensty nformaton, Pattern Recognton, Vol. 39, pp , [19] I. Cho, D. Km, Eye correcton usng correlaton nformaton, In Y. Yag et al. (Eds.): ACCV 2007, Part I, LNCS 4843, pp , [20] Z-H. Zhou, X. Geng, Projecton functons for eye detecton, Pattern Recognton, Vol. 37, pp , [21] K. Peng, L. Chen, S. Ruan, G. Kukharev, A robust algorthm for eye detecton on gray ntensty face wthout spectacles, Journal of Computer Scence and Technology, Vol. 5, No. 3, pp , [22] W. H. Dan, J. Qang: In the eye of the beholder: A survey of models for eyes and gaze, IEEE Trans. On Pattern Analyss and Machne Intellgence, Vol. 32, No. 3, pp , [23] M. Hassaballah, T. Kanazawa, S. Ido, and S. Ido, An effcent eye detecton method based on gray ntensty varance and ndependent components analyss, IET Computer Vson, Vol. 4. No. 4, pp , [24] Vola P., Jones J. M., Robust real-tme face detecton, Internatonal Journal of Computer Vson, Vol. 57, No. 2, pp , [25] P.J. Phllps, H. Moon, S. Rzv, P. Rauss, The FERET evaluaton methodology for face recognton algorthms, IEEE Transactons on Pattern Analyss and Machne Intellgence Vol. 22, No. 10, 2000, pp [26] R.W. Frschholz, U. Deckmann, Bod: a multmodal bometrc dentfcaton system, IEEE Computer, Vol. 33, No. 2, pp , [27] Y. Rodrguez, F. Cardnaux, S. Bengo, and J. Marethoz, Measurng the performance of face localzaton systems, Image and Vson Computng, Vol. 24, No. 8, pp , [28] O. Jesorsky, K. Krchberg, R. Frschholz, Robust face detecton usng the hausdorff dstance. In Proc. of the Thrd Internatonal Conference on Audo- and Vdeobased Bometrc Person Authentcaton (AVBPA), Halmstad, Sweden, pp , M. Hassaballah receved a B.Sc. degree n Mathematcs n 1997, then M.Sc. degree n Computer Scence n 2003, all from South Valley Unversty, Egypt. In Aprl 2008, he joned the laboratory of Intellgence Communcaton, Department of Computer Scence, Ehme Unversty, Japan. Hs research nterests nclude: mage processng, facal features detecton, face detecton and recognton, object detecton, content-based mage retreval, smlarty measures, fractal mage compresson, and hgh performance computng. Kenj Murakam graduated n 1971 wth a specalty n electrcal engneerng from the Department of Engneerng at Ehme Unversty, completed a master s course n 1973, and became an assstant n the Electroncs Dvson there. Currently, he s a professor of computer scence at Ehme Unversty, and s engaged n research on mage processng, neural networks, and nonmonotonc logc. He holds a D.Eng. degree. Shun Ido receved a Ph.D. n Tokyo Insttute of Technology. Currently, he s a Senor Assstant Professor of Graduate School of Scence and Engneerng, Ehme Unversty. Hs research nterests nclude: Image Processng, Image Codng, and Vrtual Realty.

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