Real-Time Hand Tracking and Gesture Recognition System

Transcription

1 GVIP 5 Confeene, 9- Deembe 5, CICC, Caio, Egp Real-ime Hand aing and Gesue Reogniion Ssem Nguen Dang Binh, Enoida Shuihi, oshiai Ejima Inelligene Media Laboao, Kushu Insiue of ehnolog 68-4, Kawazu, Iizua, Fuuoa 8, JAPAN [ndbinh, enoida, oshi]@mie.ai.ueh.a.jp Absa In his pape, we inodue a hand gesue eogniion ssem o eognize eal ime gesue in unonsained envionmens. he ssem onsiss of hee modules: eal ime hand aing, aining gesue and gesue eogniion using pseudo wo dimension hidden Maov models (P-DHMMs). We have used a Kalman file and hand blobs analsis fo hand aing o obain moion desipos and hand egion. I is fai obus o bagound luse and uses sin olo fo hand gesue aing and eogniion. Fuhemoe, hee have been poposed o impove he oveall pefomane of he appoah: () Inelligen seleion of aining images and () Adapive heshold gesue o emove non-gesue paen ha helps o qualif an inpu paen as a gesue. A gesue eogniion ssem whih an eliabl eognize single-hand gesues in eal ime on sandad hadwae is developed. In he expeimens, we have esed ou ssem o voabula of 36 gesues inluding he Ameia sign language (ASL) lee spelling alphabe and digis, and esuls effeiveness of he appoah. Kewods: Hand gesue eogniion; Hand aing; Kalman file; Pseudo -D Hidden Maov models.. Inoduion Gesue eogniion is an aea of aive uen eseah in ompue vision. I bings visions of moe aessible ompue ssem. In his pape we fous on he poblem of hand gesue eogniion using a eal ime aing mehod wih Pseudo wo-dimensional hidden Maov models (P-DHMMs). We have onsideed singlehanded gesues, whih ae sequenes of disin hand shapes and hand egion. AGesue is defined as a moion of he hand o ommuniae wih a ompue. Man appoahes o gesue eogniion have been developed. Pavlovi [] pesen an exensive eview of exising ehnique fo inepeaion of hand gesues. A lage vaie of ehniques have been used fo modeling he hand. An appoah based on he -D loaions of fingeips and palms was used b Davis and Shah []. Bobi and Wilson [3] have developed dnami gesues have been handled using famewo. A sae-based ehnique fo eogniion of gesues in whih he define he feaue as a sequene of saes in a measuemen o onfiguaion spae. Human-ompue ineaion using hand gesues has been sudied b a numbe of eseahes lie Sane and Penland [4], and Kjeldsen and Kende [5]. Use of induive leaning fo hand gesue eogniion has been exploed in [6]. Yoon e al. [8] have poposed a eogniion sheme using ombined feaues of loaion, angle and veloi. Loon e al. [9] popose a eal ime gesue eogniion ssem, whih an eognize 46 ASL, lee spelling alphabe and digis. he gesues ha ae eognized [9] ae 'sai gesue' of whih he hand gesues do no move. Seveal ssem use Hidden Maov models fo gesue eogniion [7,7]. his eseah is foused on he appliaion of he HMM mehod o hand gesue eogniion. he basi idea lies in he eal-ime geneaion of gesue model fo hand gesue eogniion in he onen analsis of video sequene fom CCD amea. Sine hand images ae wodimensional, i is naual o believe ha he -DHMM, an exension o he sandad HMM, will be helpful and offe a gea poenial fo analzing and eognizing gesue paens. Howeve a full onneed -DHMMs lead o an algoihm of exponenial omplexi (Levin and Pieaini, 99). o avoid he poblem, he onneivi of he newo has been edued in seveal was, wo among whih ae Maov andom field and is vaians (Chellapa and Chaejee, 985) and pseudo -DHMMs (Agazzi and Kuo, 993). he lae model, alled P- DHMMs, is a ve simple and effiien -D model ha eains all of he useful HMMs feaues. his pape fouses on he eal-ime onsuion of hand gesue P- DHMMs. Ou P-DHMMs use obsevaion veos ha ae omposed of wo-dimensional Disee Cosine ansfom (-D DC) oeffiiens. In addiion, ou gesue eogniion ssem uses boh he empoal and haaeisis of he gesue fo eogniion. Unlie mos ohe shemes, ou ssem is obus o bagound lue, does no use speial glove o be won and e uns in eal ime. Alhough use o ou nowledge fo eogniion is

2 GVIP 5 Confeene, 9- Deembe 5, CICC, Caio, Egp no new bu his appoah fis ime is inodued o he as of hand gesue eogniion. Fuhemoe, he mehod o ombine hand egion and empoal haaeisis in P-DHMMs famewo is new onibuion of his wo. Use of boh hand egions, feaues of loaion, angle, and veloi and moion paen ae also novel feaue in his wo. he oganizaion of he es of he pape is as follows. Seion desibes oveview of he gesue eogniion sheme. In Seion 3, we disuss ou aing famewo. We disuss hand gesue aining and gesue eogniion mehod in Seion 4. he nex seion pesens esuls of expeimens and finall. he summaize he onibuion of his wo and idenif aeas fo fuhe wo in he onlusion seion.. Oveview of he Gesue Reogniion Sheme In his pape we onl onside single handed posues. A gesue is a speifi ombinaion of hand posiion, oienaion, and flexion obsevaion a some ime insane. Ou eogniion engine idenifies a gesue based upon he empoal sequene of hand egions in he image fame. he eogniion poess involves aing of he gesue's hand. he hand egion being aed is eognized b blob analsis and Kalman file based appoah. P-DHMMs based appoah uses shape and moion infomaion fo eogniion of he gesue. he algoihm poposed in ou eogniion engine is show in figue :. Choose iniial seah window size and loaion.. While hand is in view, (a) a and exa he hand fom an image sequene. (b) Veif he exaed hand egion. 3. Using P-DHMMs eognize he gesue, whih gives maximum pobabili. Hand Deeed & aing Exaing he hand fom an image sequene Daabase of pedefined gesues Hand egion & fileed ajeo Hand ROI Hidden Maov Models Gesue b bes mah Movemen of he exaed hand Figue : Ssem Oveview Pedefined gesues is fowad o he eogniion algoihms Gesued eogniion 3. Hand aing Famewo We develop a eal ime hand aing mehod based on he Kaman file and hand blobs analsis, whih is obus and eliable on hand aing in unonsained envionmens and hen he hand egion exaion fas and auael. We need o onside he ade-off beween he ompuaion omplexi and obusness. he segmened image ma o ma no inlude he lowe am. o avoid his, he use is equied o wea long-sleeves shi. his is neessa o avoid using a wis-opping poedue, whih is moe inonvenien o uses. 3. Hand deeion in an image fame We mus fis exa hand egion in eah inpu image fame in eal ime. Exaing hand egion. Exaing hand based on sin olo aing [], we dew on ideas fom obus saisis and pobabili disibuions wih Kalman file in ode o find a fas, simple algoihm fo basi aing. Ou ssem idenifies image egions oesponding o human sin b binaizing he inpu image wih a pope heshold value. We hen emove small egions fom he binaized image b appling a mophologial opeao and sele he egions o obain an image as andidae of hand. Finding a palm's ene and hand oll alulaion. In ou mehod, he ene of use's hand is given as fis momens of he -D pobabili disibuion duing he ouse of hand aing algoihm opeaion whee (x, ange ove he seah window, and I(x, is he pixel (pobabili value a (x, : he fis momens ae M = xi ( x,, M = I ( x,. x hen he enoid is x = M /M, = M / M. Seond momens ae M = x I ( x, x M I( x, =. x hen he hand oienaion (majo axis) is M x aan M M M x M M θ = he fis wo Eigenvalues (majo lengh and widh) of he pobabili disibuion "blobs" ma be alulaed in losed fom as follows []. Le a = M M x, b ( / ) = (( M / M ) x ) and ( ) = M M / hen lengh l and widh w fom disibuion enoid ae ( a + ) + b + ( a ) l =, w = ( a + ) b, + ( a ) When used in hand aing, he above equaions give us hand oll, lengh and widh as maed in figue 3.

3 GVIP 5 Confeene, 9- Deembe 5, CICC, Caio, Egp Figue 3: Oienaion of he flesh pobabili disibuion maed on he soue video image fom CCD amea. 3.. Hand aing and measuing ajeoies We use Kalman file o pedi hand loaion in one image fame based on is loaion deeed in he pevious fame. Kalman file is used o a he hand egion eni in ode o aeleae hand segmenaion and hoose oe sin egion when muliple image egions ae sin olo. Using a model of onsan aeleaion moion he file povides and esimaes he hand loaion, whih guides he image seah fo he hand. he Kalman file as he movemen of he hand fom fame o fame o povide an auae saing poin o seah fo a sin olo egion, whih is he loses mah o he esimae. Insead of segmenaion he enie inpu image ino muliple sin egions and hen seleing he egion. File esimaed esuls ae used as he saing poin fo he seah fo a sin olo egion in subsequen fame. he measuemen veo onsiss of he loaion of he ene of he hand egion. heefoe he file esimae Hxˆ is he ene of a disane-based seah fo a sin olo pixel. Fis, we measue hand loaion and veloi in eah image fame. Hene, we define he sae veo as x : x =(x(), (), v x (), v ()) () Whee x(), (), v x (), v () shows he loaion of hand (x(), ()) and he veloi of hand (v x (), v ()) in he h image fame. We define he obsevaion veo o pesen he loaion of he ene of he hand deeed in he h fame. he sae veo x and obsevaion veo ae elaed as he following basi ssem equaion: x () = Φ x + Gw = Hx + v (3) Whee Φ is he sae ansiion maix, G is he diving maix, Φ is he obsevaion maix, w is ssem noise added o he veloi of he sae veo x and v is he obsevaion noise ha is eo beween eal and deeed loaion. Hee we assume appoximael unifom saigh moion fo hand beween wo suessive image fames beause he fame ineval is sho. hen Φ, G, and H ae given as follows: Φ = (4) G = (5) H = (6) he (x, oodinaes of he sae veo x oinide wih hose of he obsevaion veo defined wih espe o he image oodinae ssem. Also, we assume ha boh he ssem noise w and he obsevaion noise v ae onsan Gaussian noise wih zeo mean. hus he ovaiane maix fo w and v beome σ w I xand σ v I x espe, whee I x epesen a x ideni maix. Finall, we fomulae a Kalman file as whee K (7) = P H ( HP H + I x ) { x + K ( Hx ) } x (8) = Φ w = Φ( P K HP ) Φ + Q σ v P x x equal σ (9), he esimaed value of x fom,..., -, P equals Σ / σ v, Σ epesens he ovaiane maix of esimae eo of x, K is Kalman gain, and Q equals GG. hen he pedied loaion of he hand in he +h image fame is given as (x(+), (+)) of x +. If we need a pedied loaion afe moe han one image fame, we an alulae he pedied loaion as follows: { x + K ( Hx ) } m + m = Φ x () m m σ w ( Φ ) + Φ Q( ) m + m = Φ ( P KHP ) Φ σ v = P whee,...,, x m () + is he esimaed value of x + m fom P m + equals Σ + m / σ v, + m he ovaiane maix of esimae eo (a) () Σ epesens x + m. (b) (d) Figue 4: (a) Deeing hand's ene; (b) Compaing deeed and pedied hand loaion o deemine ajeoies; (), (d) Measuing hand's ene ajeoies. Deemining ajeoies. We obain hand ajeo b aing oespondenes of deeed hand beween suessive image fames. Suppose ha we dee hand enoid in he h image fame. We efe o hand's ' loaion as. Fis, we pedi he loaion P of P+ + hand in he nex fame (+) h image fame + wih he pedied loaion ' P +. Finding he bes ombinaion.

4 GVIP 5 Confeene, 9- Deembe 5, CICC, Caio, Egp 4. Gesue Reogniion Hidden Maov Models ae finie non-deeminisi sae mahines, whih have been suessfull applied o numeous appliaions. he onsis of a fixed numbe saes wih assoiaed oupu pobabili densi funions (pdfs) as well as ansiion pobabiliies a ij. Fo a oninuous HMM he pdf (o) of sae Sj is usuall given b a finie Gaussian mixue of he fom: L b ( o ) = N ( o, µ, ) () j m = b j Whee is he mixue oeffiien fo he m h mixue and N ( o, µ, ) is mulivaiae Gaussian densi wih mean veo An HMM λ ( π, a, b ) wih M saes is full desibed b µ and ovaiane maix. MxM-dimensional ansiion maix a, he M- dimensional oupu pdf veo b and he iniial sae disibuion veo π whih onsiss of he pobabiliies π P q = s ). Afe he model λ has been ained j = ( = j using he Baum-Welh algoihm feaue sequenes O = o,..., o an be soed aoding o ( o P( Oλ ) = P( O, Qλ) = π b ) a b ( o ) (3) allq q q q q q q, q,.., q = Hee, q is one of he saes fom Q, he se of saes, a ime. Usuall he lielihood P( O λ) is esimaed b he Viebi algoihm, whih is an appoximaion based on he mos liel sae sequene (q,,q ). Alhough simple in fom, he ime equiemen is exponenial. hans o he use of he DP ehnique, his an be ompued in linea ime in. Howeve when i omes o -DHMMs fomulaion, even he DP ehnique alone is no enough. One eseah dieion is he suual simplifiaion of he model, and he pseudo -DHMMs is one soluion. 4. Pseudo -DHMMs Consuion Desipion: Pseudo -DHMMs in his pape ae ealized as a veial onneion of hoizonal HMMs (λ,). Howeve i is no he onl one. In ode o implemen a oninuous fowad seah mehod and sequenial omposiion of gesue models, he fome pe has been used in his eseah. hee ae hee inds of paamees in he PDHMMs. Howeve, sine he hand image is wo-dimensional, we fuhe divided he Maov ansiion paamees ino supe-sae ansiion and sae ansiion pobabiliies; eah is denoed as a P( = l ),, l N and a l = + = = P( q + = j q i), i, j M ij = whee denoes a supe-sae whih oesponds o a HMMs λ, and q denoes a sae obseving a ime. he mode has N supe-saes and he HMMs λ, is defined as sandad HMM onsising of M saes. Evaluaion algoihm: Le us onside a h hoizonal fame, obsevaion fuue veo O = o,..., os,. his is a one-dimension feaue sequenes lie ha of O in Eq. (3). his is modeled b a HMM λ, wih lielihood P λ ). Eah HMM λ ma be egaded as ( O a supe-sae whose obsevaion is a hoizonal fame of saes. S P ( O λ ) = P( O, Qλ ) = π b ( o ) a b ( o ) (4) allq q q q q q s q, q,.., q s= Now le us onside a hand egion image, whih we define as a sequene of suh hoizonal fames as O = O, O,.., O. Eah fame will be modeled b a supe-sae o a HMM. Le Λ be a sequenial onaenaion of HMMs. hen he evaluaion of Λ given feaue sequene O of he sample image X is P( O Λ ) = P ( O ) a P ( O ) (5) R = whee i is assumed ha supe-sae poess sas onl one fom he fis sae. he funion is he supe-sae lielihood. Noe ha boh of he Eqs. (4) and (5) an be effeivel appoximaed b he Viebi soe. One immediae goal of he Viebi seah is he alulaion of he mahing lielihood soe beween O and HMM. he objeive funion fo an HMM is defined b he maximum lielihood as S ( O, λ ) = max a b ( o ) (6) Q s= qs qs P qs whee Q =q,q,,q s is a sequene of saes of λ, and a = π. O, λ ) is he similai soe beween wo qq q ( sequenes of diffeen lengh. he basi idea behind he effiien of DP ompuaion lies in fomulaing he expession ino a eusive fom δ s ( j) = maxδ s ( i) aijb j ( os), j =,..., M, s =,..., S, =,..., K i whee δ s ( j) denoes he pobabili of obseving he paial sequene o,..., o s in model along he bes sae sequene eahing he sae j a ime/sep s. Noe ha ( O, λ ) = δ S ( N ) whee N is he final sae of he sae sequene. he above eusion onsiues he DP in he lowe level suue of he PDHMM. he emaining DP in he uppe level of he newo is similal defined b D ( O, Λ ) = max a ( O, λ ) (7) = ha an similal be efomulaed ino a eusive fom. Hee denoes he pobabili of ansiion fom supesae o. Aoding o he fomulaion desibed s

5 GVIP 5 Confeene, 9- Deembe 5, CICC, Caio, Egp hus fa, a PDHMM add onl one paamee se, i.e., he supe-sae ansiions, o he onvenional HMM paamee ses. heefoe i is simple exension o onvenional HMM. Design of hand gesue models. Alhough hese pape [,] inodued he P-DHMMs and show pomising esuls, a fomal definiion of he model as well as deails of he algoihms used have been omied. Bu his appoah is fo he fis ime inodued o hand gesue eogniion famewo. Fo eah gesue hee is a P- DHMMs. Supe-saes Hand ROI paiion Figue 5: P-DHMM sae Fig. 5 shows a P-DHMM onsiss of 5 supe-saes and hei saes in eah supe-sae ha model he sequene of ows in he image. he opolog of he supe-sae model is a linea model, whee onl self ansiions and ansiions o he following supe-saes ae possible. Inside he supe-saes hee ae linea one dimension hidden Maov model o model eah ow. he sae sequene in he ows is independen of he sae sequenes of neighboing ows. Impovemen in ou ssem. One majo impovemen in ou ssem is he use of DC oeffiien as feaues insead of ga values of he pixels in he shif window whee mos of he image eneg is found. Insead of use an ovelap of 75% beween adjaen sampling windows [3], we have o onside he neighboing sampling of a sampling window. Suppose we allow a defomaion of up o ± d (d is a posiive inege) pixels in eihe X o Y dieions, we have o onside all he neighboing sampling wihin he disane d in ode o dee a possible defomaion. We use a shif window o impove he abili of he HMM o model he neighbohood elaions beween he sampling blos. Figue 6: Sampling window Blos exaion Sampling windows aining he hand model. Simila o neual newo, PHMMs an be ained b numbe of aining samples. Eah P-DHMM is ained b hand gesue in he daabase obained fom he aining se of eah of he gesue using he Baum-Welh algoihm. Inelligen seleion of aining images. Relaivel low eogniion ae of he lassial P-DHMMs fo hand gesue eogniion is, esseniall, aused b he inappopiae seleion of aining images and heefoe, la of some impoan infomaion. his infomaion is equied fo an adequae aining of model. In ou poposed soluion, he bes images fo model aining ae auomaiall seleed among he available images of eah subje. In he ase of ou hand gesue daabase, he poblem onsiss of hoosing 8 opimum aining images ou of 5 images available fo eah subje. Obviousl, he bes aining se is one ha onains images wih diffeen aspes of a subje hand gesue, aen in diffeen ondiions. In ode o onsu his aining se, we used he DC oeffiiens of he images, followed b he seleion of images wih he mos disinguishable oeffiiens fo P-DHMMs model aining. hus, fom he obained daa se, one an exa he mos impoan infomaion available in subje s images. he algoihm used fo deemining he bes aining images is as follows:. Sele one aining image andoml;. Compue he disane beween he DC veo of ohe images and he DC veo of he seleed image; 3. Sele as he seond aining image, he image ha has he bigges disane wih he fis aining image; 4. Fo all emaining images, obain he oveall disane beween eah image and seleed aining images using equaion (8); 5. Choose as he nex aining image, he image wih he bigges disane; 6. If hee is sill aining image, go o End. B appling his algoihm, one an expe ha he las image onain some infomaion, whih was no pesen in he pevious hand images.. Equaion used o alulae he disane beween DC veos of he i and j images is as follows: i j N D, = ( d ( n) d ( n)) (8) n= i Index of he nex aining image = agmax i (min (D i,j ))(9) whee N is he lengh of image veo (No. of ows No. of olumns). Gesue eogniion. he Viebi algoihm is used o deemine he pobabili of eah hand model. he image is eognized as he hand gesue, whose model has he highes poduion pobabili. Due o he suue of he P-DHMMs, he mos liel sae sequene is alulaed in wo sages. he fis sage is o alulae he pobabili ha ows of he individual j

6 GVIP 5 Confeene, 9- Deembe 5, CICC, Caio, Egp images have been geneaed b one-dimensional HMMs, ha ae assigned o he supe-sages of he P-DHMMs. hese pobabiliies ae used as obsevaion pobabiliies of he supe-saes of he P-DHMMs. Finall, on he level seond Viebi algoihm is exeued. he omplee double embedded Viebi algoihm is saed expliil in []. Alhough he P-DHMMs eognize hooses a model wih bes lielihood, bu we anno guaanee ha he paen is eall simila o he efeene gesue unless he lielihood value is high enough. A sample heshold fo he lielihood ofen does no wo. heefoe, we ae impovemen model ha ields he lielihood value o be used as a heshold. A gesue is eognized onl if he lielihood of he bes gesue model is highe han ha of he heshold model. 3 n (a) "Z" gesue (b) "H" gesue () "3" gesue (d) "Z" gesue (e)"a" gesue (f) "5" gesue Figue 8: Some images sequene in aing 5. Resuls of PDHMMs based gesue eogniion We esed ou eogniion ssem using ASL show in Fig.9. As he aining daa fo eah gesue is used 3 diffeen images of 36 gesues. he images of he same gesue wee aen a diffeen imes. he bes eogniion ae is 98% oveall esul. Expeimen shows ha he ssem an wo oel wih suffiien aining daa, o exend he aining daabase is onl a ompomise o edue he la of aining daa. Figue 7: A simple suue of he P-DHMMs - heshold model. he doed aows ae null ansiions. he model ansiion pobabili ino he heshold model p(m) is o saisf P(X G λ G )P(G)>P(X G λ M )P(M) () P(X M λ G )P(G)<P(X M λ M )P(M) () whee X G, X M, λ G, λ M denoe a gesue paen, a nongesue paen, he age gesue model and he heshold model, espeivel. I is impl ha a gesue should bes mah wih he oesponding gesue model and ha a non-gesue wih he heshold model, espeivel. Inequali () sa ha he lielihood of a gesue model should be geae han ha of he heshold model. 5. Expeimenal esuls In his seion we pesen he eogniion esuls of 36 gesues, whih inludes ASL lee spelling alphabe and digis (Fig. 9). We disuss esuls of eah of he seps involved befoe pesening he oveall esuls. 5. Resuls of aing he omplee ssem wos a abou 5 fames/se. If he hand moves oo fas Kalman file a his will no find hand pixels oel, he ae sas going ou of a due o he anslaion and sale paamees ge disoed values. Beside his if iniializaion is no good, he ae an no ahived he esul of aing and los of a. Figue 9: 36 ASL used fo hand esing 5.3 Compaison wih ohe appoahes No man vision-based appoahes have been epoed fo eal-ime gesue eogniion. I is P-DHMMs in he sense ha i is no a full onneed wo dimensions newo ha would lead an algoihm unning in exponenial ime. Compaed o emplae based mehods and -DHMMs, ou poposed ssem offes a moe flexible famewo fo gesue eogniion, and an be used moe effiienl in sale invaian ssems. he P- DHMMs appoah is poposed and he ompaison o - DHMMs is made. Boh of hem ae obus enough in single gesue o size envionmen, bu he P-DHMMs pefoms muh bee and failiaes when eogniion onain vaious gesues wih vaiaion of size. In ohe wolds, he P-DHMMs offes pomising poenial o solve diffiul hand gesues eogniion.

7 GVIP 5 Confeene, 9- Deembe 5, CICC, Caio, Egp 6. Conlusions We have developed a gesue eogniion ssem ha is shown o be obus fo ASL gesues. he ssem is full auomai and i wos in eal-ime. I is fail obus o bagound luse. he advanage of he ssem lies in he ease of is use. he uses do no need o wea a glove, neihe is hee need fo a unifom bagound. Expeimens on a single hand daabase have been aied ou and eogniion aua of up o 98% has been ahieved. We plan o exend ou ssem ino 3D aing. Cuenl, ou aing mehod is limied o D. We will heefoe invesigae a paie 3D hand aing ehnique using muliple ameas. Fous will also be given o fuhe impove ou ssem and he use of a lage hand daabase o es ssem and eogniion. Refeenes [] V.I. Pavlovi, R. Shama,.S. Huang. Visual inepeaion of hand gesues fo human-ompue ineaion, A Review, IEEE ansaions on Paen Analsis and Mahine Inelligene 9(7): , 997. [] J.Davis, M.Shah. Reognizing hand gesues. In Poeedings of Euopean Confeene on Compue Vision, ECCV: 33-34, 994. [3] D.J.uman, D. Zelze. Suve of glove-based inpu. IEEE Compue Gaphis and Appliaion 4:3-39, 994. [4] Sane,. and Penland. Real-ime Ameian Sign Language Reogniion fom Video Using Hidden Maov Models, R-375, MI Media Lab, 995. [5] R.Kjeldsen, J.Kende. Visual hand gesue eogniion fo window ssem onol, in IWAFGR: 84-88, 995. [6] M.Zhao, F.K.H. Que, Xindong Wu. Reusive induion leaning in hand gesue eogniion, IEEE ans. Paen Anal. Mah. Inell. (): 74-85, 998. [7] Heon-Ku Lee, Jin H. Kim. An HMM- based heshold model appoah fo gesue eogniion, IEEE ans. Paen Anal. Mah. Inell. (): , 999. [8] Ho-Sub Yoon, Jung Soh, Younglae J. Bae, Hun Seung Yang. Hand gesue eogniion using ombined feaues of loaion, angle, veloi, Paen Reogniion 34 : 49-5,. [9] R. Loon, A. W. Fizgibbon. Real-ime gesue eogniion using deeminisi boosing, Peeedings of Biish Mahine Vision Confeene,. [] K. Oa, Y. Sao and H.Koie. Real-ime Fingeip aing and Gesue Reogniion. IEEE Compue Gaphis and Appliaions: 64-7,. [] O.E. Agazzi and S.S.Kuo. Pseudo wo-dimensional hidden maov model fo doumen eogniion. A& ehnial Jounal, 7(5): 6-7, O, 993. [] F. Samaia. Fae eogniion using hidden maov models. Ph.D hesis, Engineeing Depamen, Cambidge Univesi, 994. [3] S.Eiele, S.Mule, G. Rogoll. High quali Fae Reogniion in JPEG Compessed Images. In Po IEEE Inena. Confeene on Image Poessing, Kobe, 999. [4] Chan Wa Ng, S. Rangganah. Real-ime gesue eogniion ssem and appliaion. Image and Vision ompuing (): 993-7,. [5] J.iesh, C.Malsbug, Classifiaion of hand posues agains omplex bagounds using elasi gaph mahing. Image and Vision Compuing, pp , [6] Y. Wu,. S. Huang. View-independen Reogniion of Hand Posues in Po. IEEE Conf. on CVPR', Vol II: 88-94,. [7] A.Ramamooh, N.Vaswani, S. Chaudhu, S. Banejee. Reogniion of Dnami hand gesues, Paen Reogniion 36: 69-8, 3. [8]. Que, F. owad a Vision-based Hand Gesue Inefae, Po. of VRS : 7-3, 994. [9]. Feeman, W.., Weissman, C.D. elevision onol b hand gesues, Po. of s IWAFGR , 995. [] G.R. Badsi, S. Claa. Compue Vison Fae aing Fo Use in a Peepual Use Inefae. Inel ehnolog Jounal Q'98, 998. [] E. Levin, R. Pieaini. Dnami plane waping fo opial haae eognion. Po. ICASSP 3, San Fansiso, CA, 49-5, 99. [] R. Chellapa, S. Chaejee. Classifiaion of exues using Gaussian Maov andom fields. IEE ans. ASSP 33 (4), , 985.