AUTOMATICALLY MULTIPLE FEATURES DETECTION OF FACE SKETCH BASED ON MAXIMUM LINE GRADIENT

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1 AUTOMATICALLY MULTIPLE FEATURES DETECTION OF FACE SKETCH BASED ON MAXIMUM LINE GRADIENT Arf Muntasa, Mohamad Harad, Maurdh Her Purnomo 3,,3 Eletral Engneerng Department, Insttut Teknolog Sepuluh Nopember, Surabaa -Indonesa Informats Engneerng Department, Unverstas Trunooo, Bangkalan - Indonesa. muntasa@elet-eng.ts.a.d, (mohar,her ABSTRACT In ths researh, author proposes new formulaton approah for multple features of fae sketh b smultaneousl movng shapes based on mamum lne gradent. It ontans four steps, those are tranng, reatng mage edge, shape ntalzaton and multple features deteton. On the tranng step wll be proessed to determne landmark all average. Shape ntalzaton s done b addng landmark varaton average and tranng set landmark average. Creatng mage edge s done to detet boundar of mage edge usng seond dervatve. Multple features deteton step s started b searhng value mamum lne gradent of tranng set. Rotaton, salng and landmark varane average on eah tranng set of mamum lne gradent s used to smultaneousl movng shapes b usng smlart transformaton. 50 halftone and hathng fae skethes have tested usng 7 features and 38 landmarks. Result of eperments show that deteton aura s 85.47% for halftone fae sketh and for hathng fae sketh. Kewords: Multple Features Deteton, Hathng and Halftone Fae Sketh, Lne Gradent, Smultaneousl Movng Shapes. INTRODUCTION Researh about fae mage nterpretaton was done b man researhes, but tranng and testng set have same modalt, both set of fae mage tranng and testng s taken from obet dretl [ 5]. whereas researh that usng fae sketh for testng was done b Xaoou Tang and Xaogang Wang Fae Sketh Snthess and Reognton [5] and Fae Sketh Reognton [6]. It shows that researh about fae sketh nterpretaton stll seldom be done. Tranng sample set whh have made for researh [5, 6] s usng halftone fae sketh, t s drawn b emplong gradaton effet from dark to brght n order to aheve a desred plastt [7]. Before feature deteton step, tranng set s transformed to be look lke wth fae sketh usng egentransform method. So dfferene between fae photo and sketh an be redued. It an be seen n Fgure [5, 6]. In Fgure an be seen, transformaton result of fae photo usng egentransform look lke wth halftone fae sketh, but not look lke wth hathng fae sketh. It means, ths method s not sutable for hathng fae sketh as testng set, sketh that s reated from dash lne whh s drawn regularl and repeatedl to gve gradaton effet [7], as shown n Fgure. Author proposes a new formulaton for multple feature deteon of fae sketh, both halftone and fae sketh based on mamum lne gradent wthout transformaton proess from fae photo to fae sketh. Author proposes a new formulaton approah that referene to Atve Shape Model (ASM. Man researhes have used ASM for researh, for eample to Vertebral Frature Deteton [8-4], Fae Image Interpretaton [6, ], and Model Buldng [-5]. On ASM, the obtaned model hanges onl n the avalable varatons n the tranng set. All hanges outsde of the tranng set are not overed b the model. The ntal shape whh easl affets the fnal result s another hallengng problem. If the ntal shape s sutable ASM leads to the orret result. Otherwse, the fnal result wll not be satsfator. Moreover, ASM onl uses data around the landmarks and does not utlze all avalable gre nformaton aross the obet. It ma be less relable, although the model boundares move to the plaes where has the most nformaton (boundares [5, 7]. The ASM also needs a sutable dreton for searh. The searh ponts are onl allowed to move aross vertal lnes to the shape. If there are few normal n a spef dreton or the boundar s not defned well n the edge profle, the algorthm wll have problems to move n ths dreton [6]. To overome weakness of ASM, author proposes the best movement dreton, though ts movement dreton s not avalable on tranng set based on mamum lne gradent, then 6 ISSN

2 6 The 5 th Internatonal Conferene on Informaton & Communaton Tehnolog and Sstems shape s moved based on translaton, salng and rotaton value whh have been found. To measure the true shape referene varaton X s transformed nto a normalzed frame of referene wth respet to the pose parameters, these are translaton (t, salng (s, s, and rotaton (φ [6, 7] X M ( φ, s [ X ] + t ( (a (b ( (d Fgure (. Photo-to-sketh Transformaton Eamples (a Orgnal Photo. (b Reonstruted Photo. ( Reonstruted Sketh. (d Orgnal Sketh. Ths proess s done usng Prorustes analss and allows apturng the ntrns varaton of shapes avodng smlartes. Applng Prnpal Component Analss (PCA an redue the dmensonalt whle mantanng relevant nformaton. Shape nstant an be generated b deformng the mean shape usng lner ombnaton of egenvetors (P. It an be wrtten b usng equaton (3. + Pb (3 (a (b ( Fgure (. Photo and Sketh Sample (a Orgnal Photo. (b Halftone Fae Sketh. ( Hathng Fae Sketh. ACTIVE SHAPE MODEL (ASM ASM s a deformable shape modelng tehnque that s basall used for segmentaton of obets n mages. ASM represents a parametr deformable model, where statstal model of the global shape varaton from a tranng set [6, 7]. Ths model alled the pont dstrbuton model (PDM. The ASM s onstruted from two stages. The frst, a profle model for eah landmark, whh desrbes the haratersts of the mage around the landmark. The model spefes what the mage s epeted to look lke" around the landmark. Seondl, a shape model whh defnes the allowable relatve poston of the landmarks. Durng searh, the shape model adusts the shape suggested b the profle model to onform to a legtmate shape. Ths s needed beause the profle mathes at eah landmark are unrelable. The shape referene an be represented as n pont polgon n mage oordnates. X (,,... n, n ( Where b s a vetor of elements ontanng the model parameters, t an be modeled b usng equaton (4. T b P ( (4 In mage searh, an ntal estmaton of the shape s manuall appled to an unseen mage. The ntal shape should ht the obet edges n the unseen mage and at the same tme be reasonabl short [6]. Then ASM uses the edge profle and the ovarane matr of the mean normalzed dervatves generated n the last stage to fnd the best movement. To measure the true shape referene varaton X s transformed nto a normalzed frame of referene wth respet to the pose parameters, these are translaton (t, salng (s, s, and rotaton (φ [6, 7] X M ( φ, s [ X ] + t ( Ths proess s done usng Prorustes analss and allows apturng the ntrns varaton of shapes avodng smlartes. Applng Prnpal Component Analss (PCA an redue the dmensonalt whle mantanng relevant nformaton. Shape nstant an be generated b deformng the mean shape usng lner ombnaton of egenvetors (P. It an be wrtten b usng equaton (3. ISSN

3 C- Automatall Multple Features Deteton Of Fae Sketh Based On Mamum Lne Gradent - Arf Muntasa 63 + Pb (3 Where b s a vetor of elements ontanng the model parameters, t an be modeled b usng equaton (4. T b P ( (4 In mage searh, an ntal estmaton of the shape s manuall appled to an unseen mage. The ntal shape should ht the obet edges n the unseen mage and at the same tme be reasonabl short [6]. Then ASM uses the edge profle and the ovarane matr of the mean normalzed dervatves generated n the last stage to fnd the best movement. 3 PROPOSED METHOD Gven tranng set I(, and number of features whh wll be etrated are k, on eah feature s F, where,,.... k. So feature of tranng set an be modeled as equaton (5 k X F F... F k F (5 Number of landmarks on eah feature s dfferent. If number of landmarks as a whole s n, then landmark of tranng set an be epressed usng equaton (6 X {(,, (,,.., ( n, n } (6 Fgure 3. Shape Model Usng 38 Landmarks on Eah Photo Image Tranng. Fgure 3 s eample of model shape whh s formed b landmark on eah tranng set. In ths researh dvde to beame four steps. Those are tranng proess, reatng mage edge, shape ntalzaton and multple features deteton. The followng s proposed method b author. 3. Tranng Gven tranng set I(,,, s par of landmark poston of tranng set and, X, then feature landmark of tranng set an be alulated landmark average usng equaton (7 and (8 n, (7 n n n, (8..m, where m s number of tranng set and..n, where n s number of landmarks on eah tranng set. So vetor of landmark average an be modeled as equaton (9. (, {(,,(,,..,( m, } (9 Average of and for all tranng set an be alulated usng equaton (0 and (. m n, X m* n (0 m n, Y m * n ( Based on result of alulaton from equaton (7 and (8 dan where.. m, then dfferene of mamum and mnmum devaton average an be epressed usng equaton ( and (3. ma ( mn ( Δσ ( ma( mn( Δσ (3 And mass enter for eah landmark of tranng set n fae mage an be modeled as equaton (4 and (5 v, (4 v, (5 m ISSN

4 64 The 5 th Internatonal Conferene on Informaton & Communaton Tehnolog and Sstems Mnmum and mamum value of and of mass enter s σmn, σma, σmn, and σma, respetvel. The an be wrtten as equaton (6. σmn σmn σma σma mn( v ma( v mn( v,, ma( v,, (6 So landmark varane total of and an be epressed as equaton (7 and (8. σ (7 σ (8, σ, + v, σ, + v And landmark varane average of and an be modeled as equaton (9 and (0. f (, f (, f (, f (, f (, f (, (4 (5 B substtutng equaton (4 and (5 nto (3 and, equaton (3 an be epressed as equaton (6. G f f (, + f (, 4 f (, + f ( +, + f (, + (6 Equaton (6 s used as matr mask that an form mage edge before multple features deteton. Fgure 4 s eample of halftone and hathng fae sketh, fae sketh gradent and quver of fae sketh gradent σ σ /n (9 σ σ /n (0 Whereas dfferene of varane devaton average of and for eah tranng set an be wrtten as equaton as ( and ( ( σma -σ Mn δ ( ( σma -σmn δ ( Output of tranng set wll be used to landmark ntalzaton on eah shape and multple features deteton proess. On shape ntalzaton s needed equaton (0, (, (9 and (0. Where as for multple features deteton s needed equaton (0, (, (, (3, (9, (0, ( and (. 3. Creatng Image Edge Change of bg ntenst on short dstane an be vewed as bg gradent funton. The smplest sotrop dervatve operator s the laplaan. For an mage funton of two varables s defned as equaton (3. G f f f + (3 B usng defnton of bakward dfferene appromaton n equaton (4 and (5 (a (b ( Fgure 4. (a Halftone and Hathng Fae Sketh (b. Fae Sketh Gradent. (. Quver of Fae Sketh Gradent. 3.3 Shape Intalzaton Shape ntalzaton an be estmated before moved; author proposes shape ntalzaton based on landmark varane average on eah tranng set and all of landmark varane average. It an be modeled usng equaton (7 and (8. X X + (7 σ Y Y + σ (8..n, n s number of landmark for eah tranng set. Pont dstrbuton model (PDM of tranng set an be seen n Fgure 5 ISSN

5 C- Automatall Multple Features Deteton Of Fae Sketh Based On Mamum Lne Gradent - Arf Muntasa 65 '. s (33 '. (34 s Equaton (33 and (34 an be wrtten as equaton (35 (a (b ( Fgure 5. (a. Pont Dstrbuton Model (b. Centre of Pont Dstrbuton Model (. Shape Model Intalzaton. 3.4 Multple Features Deteton Multple feature deteton s proess whh s used to searh feature on fae sketh. In ths researh, features whh wll be deteted are fae urvature, left eebrow, rght eebrow, left ee, rght ee, nose and lp Smultaneousl Movng Shapes. In ths step, shape s supposed as a unt. Shape movement s nfluened b number of landmarks (n, tranng set average of and (X and Y, dfferene of mamum and mnmum devaton average of and for all tranng set ( σ and σ gradent of testng set (G, varane average for all tranng set ( σ and σ, varane devaton average for all tranng set ( δ and δ and lne gradent of mage whh wll be deteted ts feature. For eah shape ntalzaton moves usng equaton (4 based on mamum lne gradent. If orgnal oordnate poston (, s translated equal to t and t, new oordnate (, an be modeled as equaton (9 and (30. ' + (9 ' + (30 If t t X, T t t and X ' ' (3 Equaton (9 and (30 an be epressed as equaton (3. X X + T (3 If orgnal oordnate poston (, s saled equal to s and s, new oordnate (, an be modeled as equaton (33 and (34. ' s ' 0 0. s It an be modeled b usng equaton (36 (35 X S. X (36 And f orgnal oordnate poston s rotated equal toφ, then new oordnate (, an be modeled b usng equaton (37 and (38. ' osφ sn φ (37 ' sn φ + osφ (38 Equaton (37 and (38 an be modeled as equaton (39 ' osφ ' snφ snφ osφ It an be epressed as equaton (40 (39 X R. X (40 Where osφ snφ R (4 snφ osφ Equaton (3, (36 and (40 an be ombned nto one equaton, t alled as smlart transformaton. T ( t, t s osφ s sn φ 0. R(, s s sn φ osφ 0, φ. S( φ,, s ( s ( s, s osφ + s osφ + s sn φ + t sn φ + t (4 For eah par of landmark testng set X (, wll be proessed usng equaton (4. Whereas angle (φ whh wll be used between -φ untl φ, salng (S and S between -S untl S, translaton ISSN

6 66 The 5 th Internatonal Conferene on Informaton & Communaton Tehnolog and Sstems of (t between X -d untl X +d and translaton of (t between Y -d untl Y +d Lne Gradent Result of equaton (4 s n par of landmarks whh wll be used to determne total of mamum lne gradent of testng set, between the frst and nth landmark, between th and (+th landmark, where <n-. If the frst landmark s (, and seond landmark s (,, author proposes formulaton to determne lne gradent usng equaton (43, (44, (45 and (46. At frst, dfferene of the bggest gradent s searhed usng equaton (43., f > n (43, f The net proess s alulatng among two landmarks usng equaton (44. It wll be used to determne number of teraton on gradent alulaton. New t New t S S Ma S S Ma φ φ Ma 4 EXPERIMENTAL RESULTS (49 Data set ontans 50 people, 3 of them are men and the other 37 are women. The poses were taken at dfferent tme wth varous knds of epressons (ees open/lose, smlng/not smlng. The fae poston s frontal wth 5 up to 0% angles. The fae mage sze s 050 pels. In ths researh, for tranng use 3 poses, those are normal, open smlng and lose smlng. Author use 7 features, those are fae urvature, left eebrow, Rght eebrow, left ee, rght ee, nose, and lp. For eah feature was gven 9, 4, 4, 5, 5, 5 and 6 landmarks, respetvel. So number of landmarks are 38. Fgure 6 shows that landmark representaton for tranng set was drawn usng blue polgons. Samples of tranng set an be seen n Fgure 7. d ( (44 + ( Gradent average an be alulated usng equaton (45, (46, and (47. + / d *( (45 + d *( (46 / d G (, G(, d (47 Result of gradent average wll be evaluated b usng equaton (48 based on n whh have been resulted n equaton (43, author proposes to epress t usng equaton (48 n < d, G(, * ( d /( d + β LG(, (48 n d, G(, Value of LG(, for eah translaton, salng and rotaton wll be ompared. If mamal lne gradent les on th landmark, value of translaton, salng and rotaton wll be updated usng equaton (49 Fgure 6. Landmark Dstrbuton Of Fae Photo Image For testng sample, author uses 50 halftone and 50 hathng fae skethes. Both Fgure 9 and, frst row s shape ntalzaton wth red polgons and blue ponts and seond row s result of multple features deteton wth green polgons and red ponts. 4. Epermental Results Usng Halftone Fae Sketh. In ths eperment, 50 halftone fae skethes wth front vew was made b skether, n Fgure 8 seond row s eample of some eperment results. ISSN

7 C- Automatall Multple Features Deteton Of Fae Sketh Based On Mamum Lne Gradent - Arf Muntasa Epermental Results Usng Hathng Fae Sketh. Fgure 7. Sample of Photo Images Tranng To prove that proposed method an detet multple features of hathng fae sketh wthout transformaton, author use 50 hathng fae skethes wth front vew was made b skether. In Fgure 0 seond row s eample of some eperment results. Number of error for eah feature and mage an be seen n Fgure 9, all of features an be deteted trul, eept on the frst olumn fourth, sth and seventh landmark on fae urvature. On the thrd olumn, seond landmark on fae urvature and fourth olumn steenth landmark on left eebrow of obet stll mpresel. Perentage of error on eah feature an be seen n Table. It shows that the bggest error s the nose feature (3%, whereas the smallest error s the rght eebrow and the left ee (.%. Fgure 8. Eperment Result Usng Halftone Fae Sketh. Perentage of error on eah feature an be seen n Table. It shows that the bggest error s the nose feature (7%, whereas the smallest error s the left eebrow and the rght ee (%. Table. Error Perentage of Features for Eah Halftone Fae Sketh b Smultaneousl Movng Shapes Features Number Of Errors Number Of Landmarks Error (% Fae urvature ,00% Rght eebrow 4 00,00% Left eebrow 4 00,00% Rght ee 5 50,00% Left ee ,0% nose ,0% Lp ,67% Error 4,53% Fgure 9. Eperment Result Usng Hathng Fae Sketh. Table. Error Perentage of Features for Eah Hathng Fae Sketh b Smultaneousl Movng Shapes Features Number Of Errors Number Of Landmarks Error (% Fae urvature 0 450,44% Rght eebrow 3 00,50% Left eebrow ,50% Rght ee 4 50,60% Left ee 3 50,0% Nose ,00% Lp ,00% Error 4,4% 5 DISCUSSION AND FUTURE WORK Based on epermental results usng halftone fae sketh, the bggest error that happened beause ISSN

8 68 The 5 th Internatonal Conferene on Informaton & Communaton Tehnolog and Sstems mamum lne gradent that lose to nose bgger than edge of nose feature. So result of nose shape s mpresel. Whereas the bggest error on epermental results usng hathng fae sketh, beause hathng around nose have mamum lne gradent bgger than nose feature, so result of nose shape s not presel. Therefore, on future work, t s neessar to be added shape movement b usng shape ntalzaton from proess before. It s neessar to be added one parameter for keep dstane among features. 6 CONCLUSION Based on epermental results an be nferred that a. Feature of fae sketh was deteted wthout transformaton proess from fae photo mage to fae sketh mage, though tranng and testng set have dfferent modalt. b. Aura of deteton rate s 84.47% for halftone fae sketh and 85.58% for hathng fae sketh.. Dfferene of aura perentage of multple features deteton between halftone and hathng fae sketh onl 0.%, t shows that, proposed method s not depended on fae sketh model. Aknowledgment The Author would lke to thank to DPM DIKTI (Indonesan Hgher Eduaton Dretorate General whh have supported our researh b dssertaton grand 009. REFERENCES []. Muntasa, A., Harad, M., Her Purnomo, M., (008b, "Mamum Feature Value Seleton Of Nonlnear Funton Based On Kernel Pa For Fae Reognton", Proeedng of The 4 th Conferrene On Informaton & Communaton Tehnolog and Sstems, Surabaa, Indonesa, pp []. Muntasa, A., Harad, M., Her Purnomo, M., (008d, "Automat Feature Etraton Based On Two Dmensonal Dsrete Snus Transform Segmentaton For Fae Reognton", Peneltan dan Pengembangan Telekomunkas Journal, Vol 3, no., pp 6-. [3]. Lu J., Platanots K.N., and Venetsanopoulos A.N., (003, Fae Reognton Usng Kernel Dret Dsrmnant Analss Algorthms, IEEE Trans. Neural Networks, vol. 4, no., pp [4]. Su, H., Feng D., and Zhao R.-C. 00, Fae Reognton Usng Mult-feature and Radal Bass Funton Network, Pro. of the Pan- Sdne Area Workshop on Vsual Informaton Proessng, Sdne, Australa, pp [5]. Xaofe He, Shuheng Yan, Yuao Hu, Partha Nog, and Hong-Jang Zhang, (005, "Fae Reognton Usng Laplaanfaes", Ieee Transatons On Pattern Analss And Mahne Intellgene, VOL. 7, NO. 3 pp [6]. D. Crstnae and T.F. Cootes, (003, "Faal Feature Deteton usng ADABOOST wth Shape Constrants", Pro.BMVC, Vol.,pp [7]. D. Crstnae, T.F. Cootes and I. Sott, (004, "A Multstage Approah to Faal Feature Deteton", Pro. Brtsh Mahne Vson Conferene, Vol., pp [8]. D. Crstnae and T.F. Cootes, (003, "A Comparson of two Real-Tme Fae Deteton Methods" Pro. 4th IEEE Internatonal Workshop on Performane Evaluaton of Trakng and Survellane, pp -8, Graz, Austra. [9]. D. Crstnae and T.F. Cootes, (006, "Faal Feature Deteton and Trakng wth Automat Template Seleton", Pro. 7th IEEE Internatonal Conferene on Automat Fae and Gesture Reognton, pp [0]. D. Crstnae and T.F.Cootes, (006, "Feature Deteton and Trakng wth Constraned Loal Models", Pro. Brtsh Mahne Vson Conferene, Vol. 3, pp []. D. Crstnae and T.F. Cootes, (007, "Boosted Atve Shape Models", Pro. Brtsh Mahne Vson Conferene, Vol., pp []. T.F. Cootes, C.J. Twnng and C.J. Talor, (004, "Dffeomorph Statstal Shape Models", Pro. Brtsh Mahne Vson Conferene, Vol., pp [3]. T.F. Cootes, C.J. Twnng, V.Petrov, R.Shestowtz and C.J. Talor, (005, "Groupwse Construton of Appearane Models usng Pee-wse Affne Deformatons", Pro. Brtsh Mahne Vson Conferene, vol., pp [4]. C.J.Twnng, T.F. Cootes, S. Marsland, V.Petrov, R. Shestowtz and C.J.Talor, (005, "A Unfed Informaton-Theoret Approah to Groupwse Non-rgd Regstraton and Model Buldng", Pro. Informaton Proessng n Medal Imagng, pp.-4. [5]. Xaoou Tang and Xaogang Wang (003, Fae Sketh Snthess and Reognton, ISSN

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