3-Parameter Hough Ellipse Detection Algorithm for Accurate Location of Human Eyes

Transcription

1 JOURNAL OF MULTIMEDIA, VOL. 9, NO. 5, MAY Parameter Hough Ellpse Detecton Algorthm for Accurate Locaton of Human Eyes Qufen Yang School of Informaton Scence and Engneerng, Central South Unversty, Changsha, Chna Emal: Huosheng Hu School of Computer Scence & Electronc Engneerng, Unversty of Essex, Colchester CO4 3SQ, UK Emal: Wehua Gu School of Informaton Scence and Engneerng, Central South Unversty, Changsha, Chna Emal: Shuren Zhou Computer & Communcaton Engneerng School, Changsha Unversty of Scence & Technology, Changsha, Chna Emal: Can Zhu School of Traffc and Transportaton Engneerng, Changsha Unversty of Scence & Technology, Changsha, Chna Emal: Abstract Accurately postonng the Human Eyes plays an mportant role n the detecton of the fatgue drvng. In order to mprove the performance of postonng of human face and eyes, an accurately postonng method of the human eyes s proposed based on the 3-parameter Hough ellpse detecton. Frstly, the human face area s dvded by usng the skn color clusterng and segmentaton algorthm. Then, the segmented mage s fltered by usng ts geometrc structure and the approxmate postons of the human face area and eyes are calculated. Fnally, on the bass of the spnnng cone-shape eye model, the poston of human face and eyes s accurately determned by usng the 3-parameter Hough transformaton ellpse detecton algorthm. The dfferent mages of human face are used to test the performance of the proposed method. The expermental results show that the extreme value of upper and lower eyelds and the actual poston s and the proposed algorthm has hgher postonng accuracy. Index Terms Skn Color Clusterng; Postonng of Human Eyes; Hough Transformaton; Ellpse Detecton I. INTRODUCTION Along wth rapd economc development, the number of automobles n the whole globe has been constantly ncreasng; therefore, the problem of road safety becomes a hot ssue. Besdes, fatgue drvng s one of the major reasons for traffc accdents. In order to solve ths problem, we thus conduct researches about fatgue drvng and place our emphass on human eye trackng n detectng devces for fatgue drvng as human eye trackng serves as an mportant step n drowsy drvng detecton system. Domestc researches on fatgue drvng have become more and more snce 003, focusng on the state nformaton of the head or face of the drvers. In 001, from the perspectve of the mage recognton technology, Zheng Pe et al. appled the PERCLOS fatgue parameters to develop the measurement system of motor drver fatgue (L Zengyong and Wang Chengzhu, 001). Wang Xaojuan started the research on the combnaton of the eye state nformaton and mouth state nformaton(cootes T F and Taylor C J, 1995), capturng the head poston nformaton through the moble camera mounted on the platform, then fndng the eyes and the mouth poston n the mage, and extractng and combnng all the data nformaton to determne fatgue.therefore, t has mportant research value to use the camera to contnuously observe the mage of drver s facal features such as eyes, ears, nose and mouth and then determne whether the drver s fatgue drvng n accordance wth exstng facal recognton technology. Federal Hghway Admnstraton uses the PERCLOS method, whch determnes the fatgue degree of eyes n accordance wth the duraton of eyes beng closed durng a certan perod, and ts precondton s the accurate postonng of human eyes. Therefore, t s partcularly mportant to conduct accurate postonng of eyes n accordance wth the color mage sequences captured by the camera. The postonng method of human eyes generally conssts of two steps: the frst step s coarse postonng, do: /jmm

2 60 JOURNAL OF MULTIMEDIA, VOL. 9, NO. 5, MAY 014 whch s to fnd the possble spot of eyes on the mage or ntally determne the approxmate poston of human eyes; the second step s accurate postonng, whch uses certan rules or verfcaton method to determne the exact poston of two eyes. At present, the postonng algorthms for human eyes nclude: the method proposed by Renders et al. whch uses the neural network and the mcro characterstc of eyes to poston the feature of human face [1], ts shortage s that t requres a hgh calculated quantty for mult-scale detecton of human eyes; Zhu et al. used the ntegral mage to fnd the canddate ponts of pupl and then used SVM for detecton [], whch seldom used the shape nformaton of eyes; Lu et al. used the geometrc characterstcs of rs to detect human eyes, then conducted parng n accordance wth certan rules, and then used the neural network to conduct verfcaton [3]; Bala et al. proposed a eye postonng method based on genetc algorthm and decson tree [4], whch cannot effectvely address the stuaton of face rotaton, and t does not have adequate recognton ablty of objects smlar to human eyes; Feng and Yuen proposed a mult-thread method to poston eyes [5]; Huang and Weshler used the SVM method to obtan the posture of human face and the poston of human eyes [6]; Wang Shoujue et al. used the geometry complexty to rapdly poston human eyes [7]; based on edge extracton, Zhang et al. used the Hough transformaton [8] and crcle detecton algorthm to poston the rs of eye, but the generated mage s not necessarly consstent wth the actual shape of eyes. Therefore, all the accurate postonng algorthms for human eyes proposed up untl now requre a hgh calculated quantty, some are dffcult to realze, and some are senstve to the rotaton, translaton or sze change of mage, whch are hghly lmted. In ths paper, an nnovatve accurate postonng method for human eyes based on the color space of skn color clusterng and the Hough transformaton ellpse detecton proposed. Frst of all, through the skn color clusterng and segmentaton algorthm, ths method dvdes the human face area to obtan the bnary mage of human face; then, the skn color clusterng and segmentaton algorthm s used to conduct connectvty analyss of the bnary mage that ncludes the human face area, then, geometrc fltraton s conducted, and the centrod pont of hole n the canddate human face area s calculated to fnd possble human eye par; based on approxmate detecton of the human face area and eyes, the spnnng cone-shape eye model s proposed to analyze the parameters of ellptcal calculaton, whch overcomes the shortage of tradtonal 5-parameter space ellpse method, and the 3- parameter space method s used; at last, the Hough transformaton ellpse detecton algorthm s used to conduct accurately postonng of human eyes. Wth the relatve error scale of 0.104, ths algorthm can reach an accuracy of 100%. II. COLOR MODEL OF SKIN COLOR CLUSTERING A. Color Space of Skn Color Clusterng In order to segment the human face area from the whole mage, a relable skn color model s bult, whch apples to varous factors that affect the skn detecton result, such as dfferent skn colors, lghtng condtons and coverng. The RGB space conssts of the red, green and blue components, and because n ths space, the color nformaton and brghtness nformaton are mxed, whle the dfference of skn color s manly caused by brghtness, therefore, ths color model s not sutable. Through statstcs, we fnd that the Cb and Cr components present very stable clusterng characterstcs, as shown n Fgure 1. By usng the clusterng between the chromatcty and brghtness of skn [1] [], the chromatcty and brghtness components are selected to dvde the YCbCr color space and conduct skn color modelng. Frequency Dstrbuton Of C b Cb Frequency I- Dstrbuton Of C r Fgure 1. Dstrbuton of Cb and Cr components n skn color In Fg., the dstrbuton of gray level dfferences over mages of eyes s shown. The gray level dfferences, calculated over dfferent scales of skn, are shown n the Fgure It can be seen that the dstrbuton of gray level dfferences for the class of eye mages s well approxmated by the YCbCr color space. Further, t can be seen that the wdth k of the dstrbuton ncreases wth the scale. Relatve frequency Scale 1 Scale Scale Gray level dfference Fgure. The dstrbuton of gray level dfferences B. Gaussan Model of Skn Color Clusterng After projectng the obtaned facal mage nto the YCbCr space, the number of dfferent Cb Cr pars s calculated, the fnal count value s used as the vertcal coordnate, and wth Cb as the horzontal ordnate and Cr as the longtudnal coordnate, a three dmensonal stereogram s drawn(fgure 3) []. Cr

3 JOURNAL OF MULTIMEDIA, VOL. 9, NO. 5, MAY For any pxel, ts brghtness component Y and the two chromatcty components Cb and Cr are statstcally ndependent, whch present Gaussan dstrbuton. Because the brghtness component Y s susceptble to the change of lght, therefore, only the two stable chromatcty components Cb and Cr are used to buld the Gaussan model G( m, V ) : m ( Cr, Cb) (1) 1 N Cr Cr () N 1 1 N Cb Cb (3) V N 1 Cr, Cr Cr, Cb Cb, Cr Cb, Cb In whch, Cr and Cb are the correspondng mean values of Cr and Cb, and V s the covarance matrx. Through the bult skn color model, we transform a color mage nto a grayscale mage, the grayscale value corresponds to the possblty of ths pont belongng to the skn area, and then, the grayscale mage can be further transformed nto a bnary mage by selectng approprate threshold value, n whch, 0 and 1 refer to the non-skn area and skn area respectvely. Y Cr Cb Fgure 3. Dstrbuton of skn color n the YCbCr space III. AREA SEGMENTATION DURING COARSE POSITIONING OF HUMAN EYES Because the human face s a connected area, we also conduct connectvty analyss of the bnary mage that ncludes the human face area obtaned earler n the color space of skn color clusterng. For the nose exstng n the nsde and background of mage, the 3 3 md-value s adopted to conduct flterng and de-nosng, and the expanson operator of morphology s used for fllng to make the non-flled area as small as possble. Based on the color segmentaton algorthm proposed n the lterature [1], the skn color clusterng and (4) segmentaton algorthm s proposed, and the purpose of ths algorthm s to ensure varous clusterng areas have consstent color. The skn color clusterng and segmentaton algorthm s as the followng: (1) Dvde the skn color mage nto m n blocks, and calculate the mean value of skn color pxel ycb Cr of each block. () Set the two-dmensonal array clus to mark the clusterng area of each block, and the ntal value s set as 0. (3) Scan the mage from top to bottom and left to rght, fnd the frst block wth a clus value of 0, use t as a new clusterng area, then set clus as 1, and ntalze Cb, Cr as well as the mean values of Cr and Cb of the area. (4) Successvely examne all skn color blocks wth a clus value of 0 n the surroundng area of Block 8, f ts varance wth the Y component of all blocks n current Cb Cb Cr Cr area s smaller than c 1, c Cb Cr ( Cb) ( Cr) t should be ncluded nto the area, the clus value should be set as 1, and Cr, Cb as well as the mean values of Cr and Cb should be recalculated; repeat (4) untl all connected blocks are scanned. (5) Record the searched area, return to Step (3), conduct a new round of search, and add 1 to the clus value of each block untl the clus values of all skn color blocks are not 0. A. Geometrc Fltraton In addton to the facal skn, the skn color area extracted wth the skn color clusterng and segmentaton algorthm also ncludes the skn of arm and shoulder, whch even ncludes movng external surface that does not belong to human body. However, the human face has unque features, and the non-human face area can be fltered through the followng prncple: (1) The human face occupes a certan proporton n the mage, whch s the common three parts and fve organs, calculate the sze of connected area n the detected canddate area of human face, set the threshold value as T, when the connected area s smaller than T, t s regarded as non-human face area, and ths area wll be abandoned and blackened. () The front human face area s close to an ellpse, and under non-extreme stuaton, the length-wdth ratos of the boundng rectangles of the profle, lookng up face and lookng down face are all wthn a certan scope. Through multple experments, we obtaned the scope of length-wdth rato s [0.5,.5], and therefore, the canddate area outsde of ths scope can be fltered and blackened. (3) Because the color and skn color of eyebrows and eyes (sometmes the nostrls and mouth area) have sgnfcant dfference on both chromatcty and brghtness, therefore, these areas form one or more than one hole n the facal skn area. However, ths knd of stuaton s rare n other skn area, so n accordance wth the Euler

4 6 JOURNAL OF MULTIMEDIA, VOL. 9, NO. 5, MAY 014 number crteron, the non-human face area can be fltered. Set E as the Euler number, C as the number of connected area and H as the number of hole, then for the human face area, there s: E C H The area wth E>0 should be abandoned and blackened. (4) The lps area s not consdered for now. In accordance wth the regon segmentaton algorthm and the bnary mage after geometrc fltraton, see Fgure 4. Fgure 4. Bnary mage after regon segmentaton B. Coarse Postonng of Human Eyes The canddate areas of human face obtaned above all contan a certan number of holes. Through parng of the holes n each area, all possble two eye pars are formed. Defne a data structure set {eyes eyes={l(x,y),r(x,y), d,θ}}, and descrbe these possble two eyes. In t, d refers to the dstance between two holes, θ refers to the ncluded angle between the lne lnkng the two holes and the horzontal axs, n the meantme, ths angle s also the nclnaton angle of human face, and L and R refer to the centrod coordnates of the two hole areas respectvely. The centrod coordnate s calculated n accordance wth x n1 ml 0 j0 jb[, j] A formula, n whch,, y n1 ml 0 j0 n1 ml 0 j0 B[, j] A, the followng A B[, j] refers to the area of hole. Search each canddate area of human face respectvely, fnd all possble two eye pars wth 45 and L( x) R( x), and add them to the above set. Here, for the defned sze, t s consdered that the nclnaton angle of drver s face captured by the camera won t be too bg, and the front mage s preferred to reduce the computaton overhead. IV. ACCURATE POSITIONING ALGORITHM OF HUMAN EYES Through the above process, the real human face area has been found, and the approxmate poston of two eyes s found durng postonng of the fve sense organs. In the followng, a new Hough transformaton ellpse detecton algorthm wll be used to accurately and dynamcally generate the whole curve shape of eyes. A. Human Eye Model At present, related postonng methods for human eyes select the eyeball center or the rs center as the postonng pont, and see Fgure 4 for the eyeball structure. Eyeball s a sphere wth a radus of R: the rs s at the front of eyeball wth a radus of r; the dstance between the eyeball center and the rs center s d, and there s R =r +d. In accordance wth the descrpton n lterature [13], the raduses of the eyeball and rs are constant values, and R/r s also a constant. Therefore, we only need to measure the radus of rs r, and we can obtan R and d. The front structure of eye s defned as the spnnng cone shape, the mddle part s rs, the endponts at two sdes are the nteror angle pont and exteror angle pont respectvely, and the upper and lower arcs refer to the upper eyeld and lower eyeld respectvely, as shown n Fgure 5. In our algorthm, the nteror angle pont, exteror angle pont, upper eyeld, lower eyeld and the centrod pont obtaned through the above coarse postonng of human eye area are used to draw the longtudnal secton of ellpse, so t does not need to conduct accurate postonng of eyeball or rs. Frst of all, the 5 parameters of the ellpse are analyzed, and the Hough transformaton ellpse detecton algorthm s used to generate the eye curve and accurately poston the area of human eyes. the nteror angle pont r Irs O r d R O eyeball Fgure 5. Eyeball structure U 1 O t r U L L1 Fgure 6. Spnnng cone-shape eye model B. Ellpse Detecton Algorthm exteror angle pont The ellpse detecton algorthm has always been a key ssue durng mage processng, and just due to ths reason, there are varous ellpse detecton methods now. Because t requres 5 parameters to completely defne an ellpse, therefore, t requres a fve-dmensonal parameter space to detect an ellpse, whch s a very tme consumng work. Lterature [14] used a new ellpse detecton algorthm, whch uses the long axs of ellpse to rapdly and effectvely fnd the parameters of ellpse, and t only needs a one-dmensonal accumulatve array to accumulate the lengths of the short axes of ellpse. In ths way, the requred computaton storage space s much

5 JOURNAL OF MULTIMEDIA, VOL. 9, NO. 5, MAY smaller than the prevous algorthms. We used the method of Lterature [14] nto our system. ( x1, y1) f1 ( x0, y0) d f ( xy, ) Fgure 7. Geometrcal characterstc of ellpse ( x, y ) In the ellpse n Fgure 6, there are 5 ellpse parameters: the centrod pont ( x0, y 0), the ellpse drecton angle α and the lengths of long axs and short axs ( ab, ). We add addton nformaton to each boundary pont or use specal boundary pont, and n ths way, we only need a small amount of boundary ponts to determne the poston of an ellpse. The nteror angle pont ( x1, y 1) and exteror angle pont ( x, y ) represent the two endponts of long axs (whch can only be the two endponts of long axs). The 4 parameters of ellpse can be calculated n accordance wth the followng method: x1 x x0 (5) y1 y y0 (6) ( x x1) ( y y1) a (7) y y1 arctan x x 1 (8) Assume f1 and f are the two foc of ellpse, ( xy, ) s a random thrd pont on the ellpse, and we can use ( xy, ) to calculate the ffth parameter of ths ellpse. Apparently, the dstance between ( xy, ) and ( x0, y 0) s smaller than the dstance between ( x1, y1) and ( x0, y0) or the dstance between ( x, y ) and ( x0, y 0). In ths way, the computatonal formula for short axs s: b ad sn a d cos a d f In whch, cos, d refers to the ad dstance between ( xy, ) and ( x0, y 0), and represents the ncluded angle between ( xy, ) and ( x0, y 0). C. Hough Transformaton Ellpse Detecton Hough transformaton s a very effectve shape analyss method, whch s nsenstve to stochastc nose, and t has been wdely used n detecton of straght lne, crcle and ellpse. Its basc dea s to transform the spatal (9) doman of mage to the parameter space and use a certan parameter form that satsfes most boundary ponts for descrpton. Curve on the mage (area boundary). The Hough transformaton detecton technque calculates the parameters of boundary curve n accordance wth local measurement, so t has great fault tolerance and robustness to the nterrupton of area boundary caused by nose nterference or beng covered by other target [15]. The general form to express the parameters of Hough transformaton analytc curve s: f ( X, a) 0 At ths moment, pont X [ x, y] T s a pont on the ellpse, and pont a [ d,, b] T corresponds to the parameter of ellpse. The ellpse of mage space corresponds to one pont n the parameter space ( d,, b). A set pont ( xy, ) restrans a group of parameters ( d,, b) of ellpse that pass through ths pont, whch s equvalent to restranng the track of pont (d, β) that generates a seres of ellpses. When pont ( xy, ) moves along ths seres of ellpses n the mage space, for each pont on the ellpses boundary, the parameter varaton n correspondng parameter space forms a spnnng cone track. Ths s consstent wth the human eye model descrbed n Secton IV. Conduct approprate quantfcaton to the parameter space of Hough transformaton, a three-dmensonal accumulator array s obtaned, and n the array, each small cubc lattce corresponds to the dscrete value of parameter ( d,, b). Through the above ellpse detecton algorthm, accurate postonng of human eyes can be rapdly and stably conduced based on detecton of human face. The specfc realzaton measures are as the followng: (1) For the color mage, frst of all, transform the mage nto grey-scale mage n accordance wth the color space of skn color clusterng. () Then, the grayscale mage can be further transformed nto a bnary mage by selectng approprate threshold value. (3) In accordance wth the connectvty of human face and the clusterng feature of skn color, the skn color clusterng and segmentaton algorthm n ths paper s used to conduct regon segmentaton of human face, then, geometrc fltraton s conducted to the segmentaton mage, and the centrod pont of hole n the canddate human face area s calculated to fnd possble human eye par. (4) In ths paper, the Hough transformaton ellpse detecton algorthm s used to accurately poston the human face. Conduct shape detecton to the ellpse boundary exstng n the mage space, frstly, calculate the gradent nformaton of the ntensty of each pont on the mage, then, obtan the edge n accordance wth approprate threshold value, and then add 1 to the accumulator of edge ( d,, b) and small cubc lattce.

6 d(eye) 64 JOURNAL OF MULTIMEDIA, VOL. 9, NO. 5, MAY 014 V. EXPERIMENT RESULT For the Hough transformaton ellpse detecton algorthm to accurately poston the curve of human eyes based on the skn color clusterng space, we selected 50 mages from the JAFFE (Japanese Female Facal Expresson) database [10] of Japanese Kyushu Unversty (00 mages) and the Internet (300 mages) respectvely to test the human eye detecton of dfferent mages of human face. For the standard human face database, due to the smple background and standard posture, t can rapdly fnd the poston of human eyes wth ths algorthm; for the latter wth multple postures and backgrounds, some even have certan nose dsturbance, ths algorthm can also accurately poston human eyes, but there exts certan pxel error, and the test method s as the followng: Frst of all, fnd the centrod pont of human eye par n the bnary mage n Fgure 3, as shown n Fgure (%) Fgure 10. The relatve error curve Several expermental result fgures are shown as n Fg. 9, some of whch belong to pctures of daly lfe. (a) refers to the orgnal mage, (b) shows the skn color smlarty mage, (c) s the bnary mage after morphologcal flter processng, and (d) represents the mage of human face and eye postonng results (the human face s marked wth the green frame, and the human eyes are marked wth the red frame). Fgure 8. Centrod pont of characterstc part of human face Frst of all, manually fnd the coordnates L( x, y ) and R( x, y ) of the centrod ponts of the left and rght human eyes, then, fnd the nteror angle pont and exteror angle pont of human eye area n the bnary mage n Fgure 3, and conduct accurate postonng of the profle curve of human eyes wth the algorthm n ths paper. Then, manually fnd the actual postonal coordnates of the extreme ponts of upper and lower eyelds (take the left eye for example) Aup ( xup, y up ) and Bdown ( xdown, y down ), then, on the accurate postonng mage of human eyes n Fgure 8 obtaned wth ths algorthm, fnd the computaton postons of the extreme ponts of upper and lower eyelds Aup ( xup, y up ) and Bdown ( xdown, y down ), calculate the Eucldean dstances between them d and d respectvely, and then calculate the relatve errors: (a) The relatve error s (b) The relatve error s Fgure 9. Sample dagram of the test result from the JAFFE human face database By makng use of the data obtaned from the test, the relatve error curve can be drawn, whch s shown n the Fgure The ordnate represents the relatve error d (eye) values, and the abscssa refers to the percentage (%) of eye mage number tested n correspondng relatve error d (eye) among the total number of mages (500). (c) (a) Fgure 11. Executon process of the test result from a complcated background Table 1 has lsted the executon tme comparson of each step of the precse postonng fg.11 (a) ~(d)of ths algorthm and the relevant algorthm (operatng n a computer wth the CPU frequency of 1.8GHz). TABLE I. EXECUTION TIME COMPARISON OF EACH STEP OF THE PRECISE POSITIONING FIGURE 11 (A) OF THIS ALGORITHM AND THE RELEVANT ALGORITHM Lterature [4] method Lterature [13] Hough transformaton method Method of ths paper Postonng of the true human face regon (ms) (b) (d) Precse eye postonng (ms) Total executon tme (ms) In accordance wth the data of test results, we can see that the above algorthm can be used to rapdly and

7 JOURNAL OF MULTIMEDIA, VOL. 9, NO. 5, MAY accurately poston human eyes durng human face detecton. Durng the test to accurately poston human eyes wth the Hough ellpse detecton algorthm, the maxmum relatve error d(eye) between the obtaned extreme values of upper and lower eyelds and the actual poston s In other words, among the 50 mages used for test, wthn the relatve error scope of 0.104, t s regarded that human eyes have been accurately postoned, and the accuracy of ths algorthm can reach In the meantme, durng the test to accurately poston human eyes wth the algorthm proposed n ths paper n a complcated background, we fnd ths algorthm can also conduct rapd and accurate postonng. We can see the algorthm proposed n ths paper has a low tme complexty. As can be seen from Table 1, the algorthm provded by ths paper requres less tme for the postonng of true human face regon and precse eye postonng compared wth the lterature [4] method and the lterature [13] Hough transformaton method, the ncreased rate of total executon tme s by 38.04% and 98.9% respectvely. Wheren, the average computaton tme refers to the mean obtaned by 10 tmes of algorthm. Through contrast, t can be seen that the computng result of Hough transformaton of ths paper s bascally n consstency wth the classcal Hough transformaton calculaton, but the speed of the former has been doubly ncreased, whch s sgnfcant. Ths s manly because 5 parameters are requred to be tested by the classcal Hough transformaton ellpse test method. The calculated amount 5 of t s ( n* n ), n whch, n refers to the sze of mage. However, the amount of the algorthm provded by ths 3 paper s only ( n* n ). The larger the mage s, the more ellpses shall be detected, and the hgher the effcency of ths algorthm wll be. VI. CONCLUSION Fatgue drvng has always been a man cause of car accdents, so t has mportant realstc sgnfcance to detect the drver s fatgue state and reduce the accdents caused by fatgue drvng. Postonng of human eyes s the precondton to buld the detecton system of fatgue drvng. In ths paper, an nnovatve method for accurate postonng of human eyes based on the color space of skn color clusterng and Hough transformaton ellpse detecton s proposed, n other words, the regon segmentaton method based on the clusterng color space s used to conduct coarse postonng of eyes frst, and then, the accurate postonng of eyes based on the Hough transformaton of ellpse detecton s conducted. Ths can sgnfcantly mprove the computaton speed of eye postonng, whch can also ncrease the accuracy and robustness of postonng. How to conduct fatgue test to the already accurately postoned human eyes s our next research task. ACKNOWLEDGMENT Ths research s partally supported by the Scence and Technology Plannng Project of Hunan Provnce (No. 01GK3096,No.01FJ4334); the Natural Scence Foundaton of Hunan Provnce of Chna (No.1JJ6057); and Scentfc Research Fund of Hunan Provncal Educaton Department (No. 1C1158, No. 13B13, No. 10C0374,NO.13C613 and NO. 13C614). REFERENCES [1] M. Renders, R. Koch, and J. Gerbrands, Locatng facal features n mage sequences usng neural networks, FG, pp , [] Z. W. Zhu, K. K. Fujmura, and Q. J, Real-tme eye detecton and trackng under varous lght condtons, n Proceedngs of ACM SIGCHI Symposum on Eye Trackng Research and Applcatons, New Orleams: Academc, 00, pp [3] X. Lu, F. Xu, and K. Fujmura, Real-tme eye detecton and trackng for drver observaton under varous lght condtons, n IEEE Intellgent Vehcle Symposum, Versalles: Academc, 001, pp [4] J. Bala, K. DeJong, and J. 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8 66 JOURNAL OF MULTIMEDIA, VOL. 9, NO. 5, MAY 014 [17] G. S. Xang,X. Y. Wang, and D. T. Lang, Real-tme detecton and trackng algorthm based on the color and character of human face, Opto-Electronc Engneerng, vol. 4, pp , 007. [18] V. Hemge, Object-Orented desgn of the groupware layer for the ecosystem nformaton system, Unversty of Montana, [19] A. Rose, M. Perez, and P. Clements, Modechart toolset user s gude, Techncal Report, NML/MRL/ , Austn: Unversty of Texas at Austn, [0] Bng Feng, Xao Qng Dng, Off-lne handwrtten Chnese character recognton wth hdden Markov models, n: 5th Internatonal Conference on Sgnal Processng Proceedngs, WCCC-ICSP 000, vol. 3, pp [1] A.V. Nefan, Embedded Bayesan networks for face recognton, Proc. of the IEEE Internatonal Conference on Multmeda and Expo, Vol., 6-9 August 00, Lusanne, Swtzerland, pp [] Petar S. Aleksc, Member, IEEE, and Aggelos K. Katsaggelos, Fellow, IEEE. Automatc Facal Expresson Recognton Usng Facal Anmaton Parameters and Multstream HMMs. IEEE Transactons on Informaton Forenscs and Securty, Vol. 1, No. 1, pp. 3-11, March [3] Mchael J L, Julen Budynek, Shgeru A kamatsu. Automatc Classfcaton of Sngle Facal Images. IEEE Transactons on Pattern Analyss and Machne Intellgence, 1999, 1 (1) pp [4] Zhong We, Lu Zhmng, Zhou Jlu.Study on Precse Eyes Locaton n Face Detecton. Computer Engneerng and Applcatons, 004, 36, pp Qufen Yang receved her B. Eng. and M. Eng. degrees degree from Nanjng Unversty, Jangsu, Chna and Natonal Defense Scence and Technology Unversty, ChangSha, Hunan, Chna n 1996 and 004, respectvely. She s currently pursung her Ph. D. degree n the School of software, Central South Unversty, Changsha, Hunan, Chna. Her current research nterests are n the areas of data fuson and computer vson. Huosheng Hu receved hs Ph. D. degree from Oxford Unversty, UK. He s a Professorn Computer Scence at the Unversty of Essex, UK, leadng the Human Centred Robotcs Group. Hs research nterests nclude bologcally nspred robotcs, servce robots, human robot nteracton, evolutonary robotcs, data fuson, artfcal lfe, embedded systems, pervasve computng and RoboCup. He s also a Chartered Engneer, a senor member of IEEE and a member of IEE, AAAI, IAS, IASTED and ACM. Wehua Gu receved the degree of the B. Eng. (Automatc Control Engneerng) and the M. Eng. (Control Scence and Engneerng) from Central South Unversty, Changsha, Chna n 1976 and 1981, respectvely. From 1986 to 1988 he was a vstng scholar at Unverstät-GH-Dusburg, Germany. He has been a cademcan of Chnese Academy of Engneerng, a full professor n the School of Informaton Scence & Engneerng, Central South Unversty, Changsha, Chna, snce Hs man research nterests are n modelng and optmal control of complex ndustral process, dstrbuted robust control, and fault dagnoses. Shu-Ren Zhou eceved hs PhD n 009 from Central South Unversty, Chna. He receved hs MS and BS n 004 and 1999 respectvely from the Changsha Unversty of Scence& Technology and Central South Unversty, Chna. Hs currently a post-doctoral at the Natonal Unversty of Defense Technology. Hscurrent research nterests nclude mage processng, pattern recognton, human pose estmaton and computer vson. Can Zhu receved hs Ph. D. n Computer Applcaton from Central South Unversty, Chna, n 009. He s engaged as a lectorate of Changsha Unversty of Scence & Technology, Chna. Hs research nterests nvolve modelng and optmzaton concerned transportaton plannng and management.