Int'l Conf. IP, Comp. Vson, and Pattern Recognton IPCV'5 7 Face Recognton and Usng Ratos of Face Features n Gender Identfcaton Yufang Bao,Yjun Yn *, and Lauren Musa 3 Department of Mathematcs and Computer cence and Center of Defense and Homeland ecurty Fayettevlle tate Unversty, NC, UA Department of Computer cence, Rutgers Unversty, NJ, UA 3 Department of Mathematcs and Computer cence, Fayettevlle tate Unversty, NC, UA Abstract - In ths paper, we have developed a system of algorthms for human face dentfcaton and for gender classfcaton. he DRLE level set method s used for dentfyng the face locaton, n whch we propose to use a rentalzaton step to accelerate the speed of fndng the face contours. Gabor wavelet transformaton s also used to extract the eye and eyebrow regons of a face, from whch a set of trplet parameters are created as rato values n term of the eye and eyebrow features. A three dmensonal lnear dscrmnaton algorthm s appled to ths set of trplet parameters. hs gender dentfcaton method takes advantage of the nvarant rato of feature dstances to buld a crteron that s robust and avods potental problems caused by the change of the feld of vew (FOV). he crteron s further appled to a set of testng face mages to dentfy the gender of each ndvdual human face, and mproved accuracy rate s acheved. Keywords: Level set functon, Gabor wavelet transformaton, human face recognton, gender dentfcaton, 3D lnear dscrmnant method. Introducton Human gender s an mportant feature used n a computer securty system when dentfyng a person of nterest, such as bometrc authentcaton. It s a well-known fact that humans naturally perceve features, ncludng dentfyng the gender, of a person quckly whle t not an easy task for a computer program to do so because t nvolves complcated nformaton to be processed through varous facal appearances. ypcally, computerzed gender recognton technque s preceded by a face recognton technque. Face recognton s mostly based upon detectng nvarant features of faces regardless of dfferent poses, skn tone, lghtng condtons and background. Even though numeral face recognton technques have exsted n lterature [, ], there are stll many challenges as each algorthm fts a specfc settng. A partcular algorthm may work well n fndng faces n a certan settng, but t may fal n a dfferent settng due to the face mage qualtes. For example, har, hat, or eye glasses can ntroduce problems n recognzng a face. One dffculty n face recognton s to establsh well-defned rules for dentfyng a face. Extractng features of a frontal face appeared as a round object wth two symmetrc eyes, a nose and a mouth can become a complcated process. It nvolves technques such as segmentaton, morphologcal operatons, and crcle fttng, etc.. Varous algorthms have been developed for dentfyng human faces. he approach dffers when dentfyng the face outlne frst and then the eyes/nose/mouth, or n the reverse order. ome researchers also proposed usng a template of human faces. However, each algorthm has ts lmtatons. When locatng a head boundary as a closed contour of round shape, the dffculty les upon ntegratng detected components together as a face outlne because classcal edge detecton algorthms mostly extracts the edge of a supposedly contnuous face outlne as dsconnected components. ome researchers even suggest usng votes of the occurrence of har and skn textures to fnd the face n an mage [3]. Lam and Yan [4] proposed usng snake (actve contour) method to locate head boundares wth a greedy algorthm n mnmzng an energy functon. he snake method s ndeed equvalent to a level set method [5]. he common problem wth ths knd of methods s the expense of the evoluton; thus the snake curve (level set surface) needs to be rentalzed n order to effcently drve the contour to the boundary. In ths paper, we proposed to dentfy face locaton n a gray scale frontal face mage usng Dstance Regularzed Level et Evoluton (DRLE) method [6]. DRLE s the most recent mprovement of the level set method that has devsed an ntrcate adjustment of the level set functon durng ts evoluton course. Wth a bult-n functon, t automatcally controls the forward and backward dffuson of the level set functon. Although t seems no need to rentalze the level set functon, we found that a re-ntalzaton step to accompany the DRLE method wll mprove face outlne dentfcaton. Our result shows that re-ntalzaton perodcally after the DRLE method was appled have actually accelerated the speed of the algorthm n searchng for the face contour. hs s effectve when a sngle face s presented as a frontal vew gray scale mage. We have also proposed a gender dentfcaton algorthm. For gender dentfcaton, statstcal method has been used from neurologcal research pont of vew. Cellerno et al [7] has used a statstcal approach together wth two modaltes of spatal fltraton methods to study the mnmum nformaton
8 Int'l Conf. IP, Comp. Vson, and Pattern Recognton IPCV'5 requred for correct gender recognton. Lower accuracy rate and more nstablty s reported for recognzng female faces than recognzng male faces. Geometrcal based methods are further used to ncorporate shape features of human faces. Lan and Lu [8] used the local bnary pattern (LBP) method to extract texture nformaton such as edges and corners by labelng mage pxels. LBP hstograms of separated small regons n a face, namely, the hstogram of the labels, are extracted and concatenated nto a sngle vector to represent a face mage. upport vector machne (VM) s then used to perform the gender classfcaton on all the vectors collected from face mages. Dong and Woodard [9] mproved the gender dentfcaton rate by extractng three global geometrc features from each eyebrow. Mnmum dstance (MD) classfer, lnear dscrmnaton analyss classfer and support vector machne classfer are then used for dentfyng human genders. In ths paper, we propose to extract three parameters of dfferent rato values for dentfyng genders. hs elmnates problems caused by the varous FOV of faces n an mage and s relatvely robust n dentfyng. We then use a three dmensonal lnear dscrmnaton algorthm to establsh a crteron for classfyng the gender of a human face. Our result shows that ths method s robust, and an mproved accuracy rate s acheved for recognzng the gender of a human face. DRLE Level et Method he advantage of usng a level set functon for dentfyng the face outlne s that an enclosed contnuous contour s ready for use to determne f t belongs to a face. Once a face s located, a crcle fttng algorthm can be appled to determne the center of the face. DRLE [6] s a geometrc actve contour model that s mplemented usng level set gradent flow to mnmze desgned energy functonal consstng of a dstance regularzaton and an external energy. he contour s obtaned as a zero level set of an auxlary functon x, y called a level set functon. he gradent flow drves ts zero level set towards desred boundary locatons n an mage. he evoluton can be descrbed as the followng partal dfferental equaton: t dv d dvg g p (.) where,, are constants and functon d p x s a doublewell potental functon defned as he functon d p x x s sn s f x s f x s s defned as x [ cos ] x he functon g s defned as g( I) G f x f x I he functon g s ndeed defned as a functon of the gradent of the mage, I, after a Gaussan smooth operator s appled. It takes smaller values at object boundares than at the smooth locatons. Eqn. (.) uses the ntal level set functon selected as the followng c, f x R ( x) c, otherwse Where R s a rectangle regon usually selected to enclose the object of nterest so that the ntal contour of the zero level set wll be placed outsde the object. Eqn. (.) drves the zero level set of functon towards the boundary presented nsde an mage. electon of the ntal level set poston s crucal for the zero level set to evolve to the desred object boundary. When usng DRLE level set functon to detect a face n an mage, t s best to place the ntal level set close to the outsde of the head area. ypcally, a user has to nput ths nformaton. An approprate level set functon can be determned quckly usng nward evoluton and small teratons. In ths paper, nstead of manually selectng the ntal LF, we placed the LF n a broad rectangle regon that covers most of the mage regon. We then appled an adaptve algorthm to perodcally determne a new ntal level set functon based on the local propertes of the pxels. he sdes of the rectangle R wll be adjust nward by 3 unts accordngly when A, where =left, rght, top, bottom,, s the threshold to determne f the left, rght, top, and bottom extreme postons are too far out. In our algorthm, we used = 4 and A s defned as A = Dfference n the most left (rght) x-coordnate of the zero and -.9 level sets; for =left, rght; A = Dfference n the hghest (lowest) y-coordnate of the zero and -.9 level sets; for =hghest, lowest; We also take nto consderaton the relatve sharp shape of the chn by adjustng the lowest sde of rectangle to the lowest vertcal center pont poston when the dfference between the average vertcal poston of the 5 bottom center ponts and the bottom extreme ponts s greater than.75. he proposed re-ntalzaton step combnng DRLE was appled to a low resoluton mage, wth sze 5x5, whch s a reszed copy of the orgnal face mage, for locatng the face outlne. In Fgure, the ntal rectangle level set s shown n (a). It s rentated nto a new level set (shown n (c)) based
Int'l Conf. IP, Comp. Vson, and Pattern Recognton IPCV'5 9 on applyng the above crteron to (b). Our result shows that the re-ntalzaton has reduced the number of teratons needed for the orgnal DRLE method to fnd the face outlne. the sze of the support for the Gaussan envelop; γ s the spatal aspect rato, and specfes that the support of the Gabor functon s an ellpse shape when γ. he coordnate x, y s obtaned from rotatng the coordnate x, y by an angle θ, and can be wrtten as: (3.) x' cos sn x y' sn cos y where θ specfes the orentaton of the rotated major axs of the ellptcal Gaussan shape functon n eqn. (3.). (a) (b) (c) Fgure. (a) the ntal rectangle regon LF. (b) the evoluton of the level set. (c). the rentalzed rectangle LF that s closer to the face. he real and magnary parts of the Gabor functon can be wrtten as: 3 Proposed Gender Identfcaton Method x y Real x x, y;,,,, exp cos (3.3) 3. Gabor Wavelet ransformaton Gabor wavelets [-3] are known for ts capablty of capturng edge nformaton n the shape of a curve n relatvely large coeffcents. Gabor wavelets play an mportant role for facal representaton, especally n representng round face features, such as face outlnes, eyes, eyebrows, and lps. ypcally local features can be obtaned usng a set of wavelet coeffcents obtaned from a sequence of dlatng and rotatng a selected mother wavelet. hese locally estmated wavelet coeffcents are robust to llumnaton change, translaton, dstorton, rotaton, and scalng [], therefore, the local features obtaned are also robust. We utlze Gabor wavelets as part of our algorthm n recognzng the left eye and eyebrow n a face mage. he Gabor wavelets are also called Gabor flters n applcatons. Gabor wavelets are self-smlar: all flters can be generated by dlatng and rotatng one selected mother wavelet. he frequency and orentaton of resultng Gabor flters are smlar to those of the human vsual system [4]. After Gabor flters are appled to an mage, the sgnfcant coeffcent values obtaned typcally ndcated the transents from one feature to another, whle small coeffcents ndcated smooth textures wthn each object. he Gabor wavelets are defned based on a complex functon, called the Gabor functon, and s defned as: g, x y x x y;,,,, exp exp (3. ) where the frst exponental functon s a -D Gaussan-shaped functon, known as the envelope, and the second exponental functon s a complex snusod. he parameter λ s the wavelength of the snusodal factor; ψ s the phase offset; σ s the standard devaton of the Gaussan envelope that decdes x y x (3.4) x, y ;,,,, exp sn Imag he rotaton of the Gabor functon resulted n Gabor flters along N number of orentatons, whch are further appled to an mage. hey are correspondng to N angles used n Gabor functons wth the angles equdstantly dstrbuted between and π radans and are ncreased at an nterval of π/n. he value of N s selected based on the computaton tme used and the completeness of mage representaton. In our study, we chose N=6. hus, N convolutons wll be computed, and s defned as: r x, y g * I I, gx, y;,,,, dd (3.5), where Ω s the collecton of mage pxels.he Gabor wavelet representaton of an mage s the combnaton of the convolutons at the N dfferent orentatons. Examples of the mage output usng -D Gabor flter are gven Fgure. Fgure. Magntude output of Gabor flter when N=6. ypcally, the preferred spatal frequency and the wave sze are not completely ndependent when selectng the Gabor wavelets. he Gabor wavelets are used as a set of bass that best represents an mage and typcally the bandwdth and σ should satsfy the followng equaton []:
Int'l Conf. IP, Comp. Vson, and Pattern Recognton IPCV'5 bw ln bw (3.6) where s the radal frequency n radans per unt length and bw s the bandwdth. In ths paper, we select 4. 4974. and. 7854 3. Extractng the Eyebrow and Eyeball Features Gender nformaton s embedded all over human faces, but s more sgnfcant n salent features such as eyes, eyebrows and lps [4]. In ths paper, because of the symmetry of the two eyes and eyebrows, we extract three rato parameters from the left eye regon only. In order to locate the desred area, the located face area s dvded nto 3 3 square sub-regons followng the common face pattern that s shown n Fgure 3(a). he left eye of a human face s typcally located n the frst regon. o ensure that the left eye and eyebrow are fully selected, ths regon s extended to nclude a larger regon, see Fgure 3(b). EyebrowLength rato, EyebrowHeght EyeballLen gth rato, EyeballHeght EyebrowHght rato3, EyeballHeght (3.8) he length and heght of the left eyeball are measured as the sze of a rectangle that contans the extracted vsble eye area see Fgure 4(c)(d). he heght of the eyebrow s measured as the heght at the mddle pont locaton of the eyebrow. he length of the eyebrow s calculated by takng nto account the curvature of the eyebrow and s approxmated as the sum of two lne segments, see Fgure 4(a)(b). Because the eyes and eyebrows are most sgnfcant features of human face, the rato parameters selected from these locatons provde suffcent nformaton n measurng the sze of the eyes n relaton to the sze of a face; therefore, t s nvarant to the FOV of the face. (a) (b) (a) (b) Fgure 3. he man face s dvded nto (a) 3 3 squares and (b) Extended mage of the left eye area. he selected left eye area s further separated nto an eyebrow and an eye. he geometrcal propertes of these two objects are studed to exact ther unque features separately. Here, we use the adaptve threshold algorthm by Nblack [5], whch was orgnally used for segmentng document mages. hs method fnds threshold value wthn a local wndow by calculatng pxel wse threshold usng local mean, μ(x, y), and local standard devaton, σ (x, y) for a pxel (x, y) [6,7]. Let the local area of nterested pxels be of sze k k, the threshold for each pxel, x, y, s calculated by usng the followng equaton: x, y x, y k x, y (3.7) 3.3 Ratos for Gender Representaton he eye n ths paper s referred to the vsble part of a human eyeball from a frontal face mage; therefore, we also call t an eyeball n the rest of ths paper. he length and heght of the left eyeball and eyebrow of a human face are measured, and three parameters are defned as the ratos of the measured values: (b) Fgure 4. Extracted bnary mages of left eyebrow and left eye. (a) male eyebrow (b) female eyebrow (c) male eye (d) female eye. 3.4 Lnear Dscrmnant Method Fsher's lnear dscrmnant (FLD) s a well-known method wdely used n statstcs, pattern recognton and machne learnng to characterze or separate two or more classes of objects or events from a lnear combnaton of features [8]. he resultng combnaton of features may be used as a lnear classfer, or more commonly, for dmensonalty reducton to serve as a crteron for classfcaton. nm nm Let and represent observatons from two classes, n our case, female and male classes. has n propertes and m observatons. has n propertes and m observatons. he lnear dscrmnant method frst n fnds a vector w so that, after the observed data beng projected onto vector w, the dstances of means n the projected space for dfferent classes wll be maxmzed whle the data scattered n the projected space wll be mnmzed. he projecton of a data onto w can be defned as an operaton of nner product:
Int'l Conf. IP, Comp. Vson, and Pattern Recognton IPCV'5 yw (3.9) where stands for the transpose of a vector. o maxmze the dstance of means n the projected space and mnmze all of the scatters n the projected space, a crteron can be defned for the degree of dscrmnaton, whch s the Fsher dscrmnant rato defned as f, (3.) w,,, where, are the correspondng projected value of the mean,, the wthn each class scatter,,of (=, ), and are calculated as: w and where s defned as:, w It can be verfed that w w. herefore we have: ' ' w w w he scatter wthn the classes, class, B, of and can be defned as: w ( )( ) B w, and the scatter between Hence, the Fsher dscrmnant rato eqn (3.) can be further wrtten as: w (( )( ) ) w f( w) w ( ) w (3.) he maxmum of the Fsher dscrmnant rato s reached at w( ) ( ) whch gves the maxmum Fsher dscrmnant rato as (3.) y m y m y (3.4) m m where m, m are the number of observatons of, separately. Once the crteron of eqn. (3.4) s calculated, t can be used to determne the gender class of a new observaton Z nto the followng two cases: ) For y <y( ), f y(z)> y, Z belongs to class; otherwse, Z belongs to class. ) For y <y( ), f y(z)> y, Z belongs to class; otherwse, Z belongs to class. he trplet parameters defned n eqn. (3.8) s calculated on all the mages n our human face lbrary. here are men faces and women faces collected n the lbrary. he Fsher lnear dscrmnant s then appled to the data array of 4 enttes to buld a crteron. he data array conssts of trplets of rato values. hs results n a crteron that s a three dmensonal plane assocated wth the dscrmnant functon. he equaton of the dscrmnaton surface can be wrtten as: where c, c c y cx cyc3z (3.5) w, 3 s calculated from eqn. (3.) and y s calculated from eqn. (3.4). For our data collected from men faces and women faces, the coeffcents are: y = -.69; c = -.457; c = -.69; c 3=.5 hs gves the functon of dscrmnaton surface as.457x.69y.5z.69 hs crteron s then appled to test new mages to classfy whether the person on each frontal face mage s a male or female. he mage of dscrmnaton surface s shown n Fgure 5. It can be seen that the trplet data ponts from male face mages fell nsde the blue dot area and the trplet data ponts from female face mages fell nsde the red dot area for most of the mages n our lbrary. max f( w) ( ) ( ) ( ) w (3.3) he calculated w s used n eqn. (3.9) to obtan the projected value, y( ) and y( ). A crteron s then establshed to determne the class of the sample:
Int'l Conf. IP, Comp. Vson, and Pattern Recognton IPCV'5 Fgure 5 Dscrmnant surface and data used to buld the dscrmnaton functon 4 Expermental Results o test our crteron usng the dscrmnant plane obtaned from the lnear dscrmnant method, we apply the crteron to a new set of men and women s mages, and thus total 4 frontal face mages for gender recognton. he testng data are shown n Fgure 6, from whch we can see that the data dstrbuted n the three-dmenson feature space, wth majorty male and female face data fell n the two sdes of the dscrmnaton surface. he fgures shown n ths paper s of an ndvdual face from the database n [9]. he dentfcaton result s provded n a table shown n able, n whch the accuracy of ths proposed gender classfcaton method s calculated. able. he experment result of dscrmnaton functon Men Women otal Number Correct Recognzed 9 8 uccess Rate 95.% 9.% and the average success rate for lnear dscrmnaton analyss classfer s 83.5%. For four features, the success rate of male dentfcaton s 73.3% and the success rate for female s 84%. Our proposed gender recognton algorthm mproves the accuracy of gender dentfcaton rate by usng the robust features of both the eyebrow and eye. It acheves a success rate of 95% for male and 9% for female on human frontal face mages, whch s a sgnfcant mprovement compared to Dong and Woodard s study. hs shows that the rato parameters we selected provded more sgnfcant nformaton for gender dentfcaton. he calculated three rato measurements of human eyebrow and eyeball have taken advantage of the common features of every frontal human face mage. he three measurements also take nto account the common sense that the sze of sgnfcant feature s proportonal to the change of other landmark feature n a human face, whch s ndeed related to FOV that determned the face sze n a face mage. herefore, the parameters chosen automatcally elmnate the possble error caused by the dfferent FOV of face mages. 5 Conclusons In ths paper we have presented our algorthms to mprove the accuracy of recognzng a human face and dentfyng the gender of an ndvdual from a human frontal face mage. hs study shows the sgnfcant advantage of combnng features of both eyebrow and eye n human gender dfferentaton. In addton, usng the related ratos of the acqured parameters frees the users from worryng about the FOV of the mages, thus ncreases the accuracy for gender dentfcaton. 6 Acknowledgments hs research s Partally funded by Natonal cence Foundaton, IA HBCU-UP #3657. * Part of the research n ths paper was done whle Mr. Yn was a student at Fayettevlle tate Unversty. 7 References [] W. Zhao, R. Chellappa, P. J. Phllps, et al. Face recognton: A lterature survey. ACM Computng urveys (CUR), 3, 35(4): 399-458. Fgure 6. Dscrmnaton surface and testng data dstrbuton. Compared wth Dong and Woodard s study[9], n whch a geometrcal-based feature extracton method s used to extract three global features from eyebrow only, ther average success rate, nclude the left eye and the rght eye, s 79.5% [] M. Yang, D. Kregman, N. Ahuja, Detectng Faces n Images: A urvey. IEEE ransactons On Pattern Analyss And Machne Intellgence, January, Vol. 4():34-58. [3]. Fahlman and C. Lebere, he Cascade-Correlaton Learnng Archtecture, Advances n Neural Informaton Processng ystems, D.. ouretsky, ed., pp. 54-53, 99.
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