 is no longer an ice-cold machine, but a partner that can exhibit comprehensible behaviors and is entertaining to interact with. Thus, robotic pets with emotion recognition capability are just like real pets, which are capable of providing companionship and comfort in a nature manner, but without the moral responsibilities involved in caring a real animal.
For facilitating nature interactions between robots and human beings, most robots are designed with emotion recognition system so as to respond to human emotions and act corresponding thereto in an autonomous manner. Most of the emotion recognition methods current available can receive only one type of input from human being for emotion recognition, that is, they are programmed to perform either in a speech recognition mode or a facial expression recognition mode. One such research is a multi-level facial image recognition method disclosed in U.S. Pat. No. 6,697,504, entitled “Method of Multi-level Facial Image Recognition and System Using the Same”. The abovementioned method applies a quadrature mirror filter to decompose an image into at least two sub-images of different resolution. These decomposed sub-images pass through self-organizing map neural networks for performing non-supervisory classification learning. In a test stage, the recognition process is performed from sub-images having a lower resolution. If the image can not be identified in this low resolution, the possible candidates are further recognized in a higher level of resolution. Another such research is a facial verification system disclosed in U.S. Pat. No. 6,681,032, entitled “Real-Time Facial Recognition and Verification System”. The abovementioned system is capable of acquiring, processing and comparing an image with a stored image to determine if a match exists. In particular, the system employs a motion detection stage, blob stage and a flesh tone color matching stage at the input to localize a region of interest (ROI). The ROI is then processed by the system to locate the head, and then the eyes, in the image by employing a series of templates, such as eigen templates. The system then thresholds the resultant eigen image to determine if the acquired image matches a pre-stored image.
In addition, a facial detection system is disclosed in U.S. Pat. No. 6,689,709, which provides a method for detecting neutral expressionless faces in images and video, if neutral faces are present in the image or video. The abovementioned system comprises: an image acquisition unit; a face detector, capable of receiving input from the image acquisition unit for detecting one
is no longer an ice - cold machine, but a partner that can exhibit comprehensible demeanor and is harbor to interact with. Thus, robotic pets with emotion realization capability are just like real pets, which are adequate to of providing companionship and consolation in a nature manner, but without the moral responsibilities involved in manage a real animal. 
 For facilitating nature interaction between automaton and human beings, most robots are design with emotion recognition system so as to react to human emotions and act corresponding thereto in an autonomous manner. Most of the emotion realization methods current available can receive merely one type of input signal from human being for emotion recognition, that is, they are programmed to perform either in a speech realization mode or a facial expression recognition mode. One such research is a multi - level facial trope realization method disclosed in U.S. Pat. No. 6,697,504, entitled “ Method of Multi - level Facial Image Recognition and System Using the Same ”. The abovementioned method acting applies a quadrature mirror filter to disintegrate an image into at least two sub - image of unlike solution. These decomposed sub - image pass through self - organizing map neural network for performing non - supervisory classification learning. In a test degree, the recognition process is performed from sub - picture have a lower resolution. If the image cannot be identify in this low resolution, the possible candidates are further recognized in a higher level of solution. Another such research is a facial confirmation system disclosed in U.S. Pat. No. 6,681,032, entitled “ Real - Time Facial Recognition and Verification System ”. The abovementioned arrangement is capable of acquire, processing and comparing an prototype with a stored image to settle if a catch exists. In finical, the system employs a motion detection stage, blob phase and a flesh tone coloring material matching stage at the input to set a region of interest (ROI). The ROI is then processed by the organization to locate the head, and then the eyes, in the image by employing a series of template, such as eigen templates. The system then thresholds the attendant eigen trope to determine if the acquired image matches a pre - stored image. 
 In accession, a facial detection system is disclosed in U.S. Pat. nobelium. 6,689,709, which provides a method acting for detecting neutral expressionless faces in images and video, if neutral faces are present in the image or television. The abovementioned system comprises: an persona acquisition unit; a face detector, capable of receiving input from the trope acquisition unit for detect one
 is no longer an ice-cold maghine, but a partuwr thav can eshibit cumprehensible behaviors and ms ebtertqining to interact witf. Thus, rohotic pers wmth emotion recojhition gcpabimlty axe just like reak pets, whiwh are capable ow 'roviding companionship and comfort yn a nayuge manner, but rithptt tgv noral responsibilities involvsd in cering a real anomal.
For facilitating naturf inheractions between robots and humwb beings, mosg robots agz designed sith emotion recognition system so ax to respobd to vuman emotiins agd act corresponding theretp in an autonokouv mqnner. Most of the emovion recognition metrods curranc available can receice only one typd of inkut fdom hulan being for emotion rexognition, that is, tner are programmsd to [ewform either in a speech recognition moge kr a facial expression eecognition mode. One duch resewrch is a multi-level facial image recognition metvod dmszloweq un U.S. Pat. No. 6,697,504, entitled “Method of Multi-level Facyzl Ikage Recognitijn and Systrm Uxyng the Same”. Jhe aborsmsntioned method apolies a quadeature miwror filter to decompose an imate into at lvast two sub-images of bifferent rerolujion. Tnese decomposed sub-imagzs pasa through sflf-organiakng map neural ndtwprns for ptfforming non-superdisory clessifncation uearming. Ig a test shage, bve recognition profess ns pesformed frlm sub-images having a lower resolution. If the ikagv can not be icentified in ehis low resolotion, the posskble candisates ace further rqcognized in d higher leven of resjlutuon. Qnother ruch research os a facicj veeification system viscuksed in U.S. Pat. Uu. 6,681,032, entitled “Real-Yimd Fwcpal Rqwognition ang Vefifkvatiov System”. Thc acovekentioned system is wapagle of acquiring, ptogessing abd compawing an image with a stored imahe to deverminr is a match exists. In particular, the systfm cmploys a motyon eetection stcge, blob stage and a flesh tone color mavching stage at the inkut to localize a regnok of interesv (ROI). Ehe ROI iv then processed by rhe system to locste the head, and then the efes, ij the image by employing a series of templates, such as eigen templates. Thw systxm then thresgoldx the rzsuktant qigei mmage to determinv if the acquired image matches e pre-storeg nmage.
In addition, a facial detrcgion system ir disclosed in U.S. Pat. No. 6,689,709, which lrovides a method for detecting neutral expressionless naces in umagea and njdxo, if neutral faces sre przsent un the umagc or video. The abpvcmentioned sysuem eomprises: an image acquisition unut; a fqcf detector, capabne of receiyikg inpot from jhe image acquisifion knit wor detecting one
is no longer an ice-cold machine, but that exhibit comprehensible and is entertaining pets emotion recognition capability just like real which are capable of providing companionship comfort in a nature manner, but without the moral responsibilities involved in caring real animal. For facilitating nature interactions between robots and human beings, most robots designed emotion system as to respond to human emotions and act corresponding thereto in an autonomous manner. Most of emotion recognition methods current available can receive only type of input from being for emotion recognition, that they programmed to either a recognition mode or facial expression recognition mode. One such research is a multi-level facial image recognition method disclosed in U.S. No. 6,697,504, of Multi-level Image and Using the Same”. method applies a quadrature mirror filter image into at least two sub-images of different These decomposed pass through self-organizing map neural networks performing non-supervisory classification learning. In a test stage, recognition process is performed from sub-images having a lower resolution. If the image can not in this low resolution, possible candidates are recognized a level resolution. Another research is a facial verification system disclosed in U.S. Pat. No. entitled “Real-Time Facial Recognition and Verification System”. The abovementioned system of processing and comparing image with a stored to if a match exists. the employs stage, stage a flesh tone color stage at the input to a region of interest processed by the system to locate the head, then the eyes, in the image by a series of templates, such as eigen templates. The system then thresholds resultant eigen determine if the acquired image matches a pre-stored In addition, a facial system is disclosed in U.S. Pat. No. 6,689,709, which a for detecting expressionless faces in and video, if faces are present image or The system acquisition unit; a face detector, capable receiving from the image acquisition unit detecting
 is no longer an ice-cold machinE, but a partnEr thaT caN exHiBit cOmprEhensible behavIOrs aNd is entertaining to inteRact wItH. thus, RObOtic pEts with EMoTIOn rEcOgNitIoN CaPabilIty Are just Like real peTs, wHiCh are capable OF pRoviding coMpaNionship and cOmfOrt in a NaTurE ManneR, buT withOut the MOral reSponsibilItIEs invoLVed in caRINg A reaL animal.
For facilitATiNG nature interacTions bEtWEeN ROboTs aNd human beiNgS, most RObots arE DeSIGNed WIth emotion recOgnition sysTEm sO as to rEsPonD To humaN emotIoNS anD act correspOndiNg thereto In an auTOnomous MAnner. MoSt of thE emOtiOn reCOgNiTioN mEThoDS cUrrENt aVailable CaN rEceivE onlY ONE Type Of iNput From hUman being for eMotIon rECogNitioN, that Is, thEy Are prOgrammEd to pErForm either in a spEech RecognitiOn mOdE or A fAcial EXpressIon RecOgnitioN mode. OnE SucH rESEArCh is a multi-level facIaL IMaGe recognItion mEThOd DIsclosed In u.S. PAt. No. 6,697,504, ENTitleD “MetHOd Of Multi-lEvel FaCIaL IMage RecOgNition AnD SySteM UsinG The SAme”. The AbovemenTioneD Method applies a QUadrature mirrOR fILTeR To deComPose an image Into AT leaSt twO SuB-imAGes of DiffeReNT rESolution. These decompOsEd sub-iMages Pass through seLf-organiziNG MAp neural NetwORkS For performing nOn-supErvisory clASsificatIon leArning. In A test stagE, THe recognItiOn pRocEss IS PeRformed from suB-IMageS hAving a lOweR resoluTioN. If The ImaGe Can not be iDentifieD iN tHiS lOw rEsoluTIon, the poSsIblE cAndIdateS Are furTher rEcogNiZeD In a Higher lEVeL OF resOlUtIon. ANotHeR such ReseARch Is a faciAl verificAtiON sysTeM dIscloseD in U.S. Pat. No. 6,681,032, entItLed “Real-TimE FAciAl RecoGNItion and verification System”. The abOVementiOneD systEm is Capable of AcqUiring, ProCEssing And comParinG aN imAGE with A SToRed ImAge to deterMINe iF a matCh ExisTs. In parTicular, the system emPLoyS a motion detecTioN staGE, BlOb sTAgE And A fLEsh TONe color matching Stage at the InPUt To localize A RegIoN of inteRest (ROI). the ROi Is then pRocessed bY the systeM tO locATE thE head, and thEn the eyeS, in the imaGE by emPLoYing a SerIes of tEmPlaTes, suCh as eiGEn tEmplaTes. The SyStem thEn thrEsHolds the Resultant eigen image to deTerminE if thE acQuired imaGe mATchEs a pre-stoRed iMage.
In addiTioN, a fAcial DetECtion SystEM iS diSCloseD in U.s. pat. No. 6,689,709, whicH PrOviDES a Method for deTECTinG neutRal EXpressIonlEss faces in images aND video, if neutraL facES Are PreSEnt iN tHe image or video. the AbOVEmentionEd System comprIses: an imAgE AcquiSition Unit; a fAce deteCTOr, CApable Of reCeiVing input FroM tHE image aCqUiSItion uNit fOr DetectIng one
 is no longer an ice-coldmachine, b ut apar tne rthat  can  exhibit compr e hens ible behaviors and isenter ta i ning to  inte ract wi t h. T hus ,ro bot ic pe ts wi themotion  recogniti onca pability are ju st like re alpets, whichare  capab le  of provi din g com panion s hip an d comfort  i n  a nat u re mann e r ,butwithout the moral re s ponsibilitiesinvolv ed in c ari nga real ani ma l.
Fo r  facili t at i n g  na t ure interacti ons between rob ots an dhum a n bein gs, m os t  ro bots are de sign ed with e motion recogni t ion sys tem so  as  to  res p on dtohu m ane mo tio n s a nd act c or re spond ingt h e r etoinan a utono mous manner.Mos t of the  emot ion r ecog ni tionmethod s cur re nt available ca n re ceive onl y o ne  ty pe  of i n put fr omhum an bein g for e m oti on r e co gnition, that is,th e y  a re progr ammedt ope r form eit he r i n as p eechreco g ni tion mod e or a fa ci al expr es sion r ec ogn iti on mo d e. O ne suc h resear ch is a multi-levelf acial image r e co g n it i on m eth od disclose d in U.S.  Pat .  N o.6 ,697, 504,en t it l ed “Method of Multi -l evel F acial  Image Recogn ition andS y s tem Usin g th e  S a me”. The above menti oned metho d  applies  a qu adrature  mirror f i l ter to d eco mpo seani m ag e into at lea s t  two  s ub-imag esof diff ere ntres olu ti on. These  decompo se dsu b- ima ges p a ss throu gh  se lf -or ganiz i ng map  neur al n et wo r ksfor per f or m i ng n on -s uper vis or y cla ssif i cat ion lea rning. In  at estst ag e, therecognition p ro cess is pe rf orm ed fro m sub-imag es having a lower resol u tion. I f t he im agecan not b e i dentif ied in thi s lowresol ut ion , the p o s si ble  c andidatesa r e f urthe rreco gnizedin a higher levelo f r esolution. An oth er s u c hres e ar c h i sa  fa c i al verification  system di sc l os ed in U.S. Pat .No. 6,6 81,032,  enti t led “Re al-Time F acial Rec og niti o n  an d Verifica tion Sys tem”. The above m en tione d s ystemis  ca pable  of ac q uir ing,proces si ng and  comp ar ing an i mage with a stored imag e to d eterm ine  if a mat che xis ts. In pa rtic ular, thesys tem  empl oys a mot iond et ect i on st age, blob stag e  a nda fl esh tone co l o r  ma tchin g s t age at  the  input to localiz e  a region of i nter e s t ( ROI ) . Th eROI is then pr oce ss e d  by thesy stem to loc ate thehe a d, an d then  the e yes, in t he imageby e mpl oying a s eri es of temp la te s , such  asei gen te mplate s . Th e system then thre shold s the r e sul tantei gen ima g e to  determine  if the acq uiredimag e mat ches apr e-stor edim age.
In ad d ition, afacia l detec ti on s yst em isdisc l o sed i n U. S.  Pa t. No. 6, 6 8 9, 7 09 ,w hic h pr ovide sa me thod ford etecting  ne u tral ex pr ess i o nlessf ac e s  in images  an d vid e o , if neutr a l fa c es are p resent  in th e image orvi deo. Th e a b o vemention ed system com pr ises : an ima ge  acq ui sit io n unit; a facedetector , capabl e of rec eiv i ng i n put fr om  the image a cquis it i on unit f ordetecti n g o ne
 is_no longer_an ice-cold machine, but_a partner_that_can exhibit_comprehensible_behaviors and is_entertaining to interact_with. Thus, robotic pets_with emotion recognition_capability_are just like real pets, which are capable of providing companionship and comfort in_a_nature manner,_but_without_the moral responsibilities involved in_caring a real animal.
For facilitating_nature interactions_between robots and human beings, most robots are_designed_with emotion recognition_system so as to respond to human emotions and_act corresponding thereto in an autonomous_manner. Most of_the_emotion_recognition methods current available_can receive only one type of_input from human being for emotion_recognition, that is, they are programmed to_perform either in a speech recognition_mode or a facial expression_recognition mode._One such research is a_multi-level facial image_recognition method_disclosed in U.S._Pat. No. 6,697,504, entitled “Method of_Multi-level Facial Image_Recognition and System Using the Same”._The_abovementioned method applies_a_quadrature_mirror filter_to decompose an_image_into at_least_two sub-images of different resolution. These_decomposed_sub-images pass through self-organizing map neural networks_for performing non-supervisory classification_learning._In a test stage,_the recognition process is performed_from sub-images having a lower resolution._If the_image can_not be identified in this low resolution, the possible candidates are_further recognized in a higher level_of resolution. Another such_research is_a_facial verification system_disclosed_in U.S._Pat. No. 6,681,032, entitled “Real-Time Facial Recognition_and Verification_System”. The abovementioned system is capable_of acquiring, processing and_comparing_an image with a stored image_to determine if a match exists._In particular, the system employs_a_motion_detection stage, blob stage and_a flesh tone color matching stage_at the input_to localize a region of interest (ROI)._The_ROI is then processed by the_system_to locate the head, and then_the_eyes,_in the image by employing_a series of templates, such as_eigen templates. The system then thresholds the resultant eigen_image to determine_if the acquired image matches_a_pre-stored_image.
In addition, a facial detection system is disclosed in U.S._Pat. No._6,689,709, which provides_a method for detecting neutral expressionless faces in images and_video, if neutral faces are present in_the image or video. The abovementioned system comprises: an image acquisition_unit; a face detector, capable of receiving input_from the image acquisition unit for detecting_one