Neuro Fuzzy Model for Human Face Expression Recognition

Transcription

1 IOSR Joural of Computer Egieerig (IOSRJCE) ISSN : Volume 1, Issue 2 (May-Jue 2012), PP Neuro Fuzzy Model for Huma Face Expressio Recogitio Mr. Mayur S. Burage 1, Prof. S. V. Dhopte 2 1 M.E.(Scholar), PRMI Egieerig & Techology,Badera(MH) 2 Associate Prof, PRMI Egieerig & Techology,Badera(MH) Abstract -- This paper preset a approach to recogize huma face expressio ad emotios based o some fuzzy patter rules. Facial features for this specially eye ad lips are extracted a approximated ito curves which represets the relatioship betwee the motio of features ad chage of expressio. This paper focuses the cocepts like face detectios, ski color segmetatio, face features extractios ad approximatio ad fuzzy rules formatio. Coclusio based o fuzzy patters ever bee accurate but still our itesio is to put more accurate results. Key words -- Face Detectio, Ski Color Segmetatio, Face Futures, Curve Formatio ad Approximatio, Fuzzy Patters. I. Itroductio: Facial expressio aalysis has bee attracted cosiderable attetio i the advacemet of huma machie iterface sice it provides atural ad efficiet way to commuicate betwee humas [2]. Some applicatio area related to face ad its expressio icludes persoal idetificatio ad access cotrol, video phoe ad telecoferecig, foresic applicatio, huma computer applicatio [5]. Most of the facial expressio recogitio methods reported to date or focus o expressio category like happy, sad, fear, ager etc. For descriptio of detail face facial expressio, Face Actio Codig System (FACS) was desig by Ekma[8]. I FACS motio of muscles are divided ito 44 actio uits ad facial expressio are described by their combiatio. Sythesizig a facial image i model based image codig ad i MPEG-4 FAPs has importat clues i FACS. Usig MPEG-4 FAPs, differet 3D face models ca be aimated. Moreover, MPEG-4 high level expressio FAP allows aimatig various facial expressio itesities. However, the iverse problem of extractig MPEG-4 low ad high level FAPs from real images is much more problematic due to the fact that the face is a highly deformable object [1]. II. Literature Review Desiger of FACS, Ekma himself as poited out some of these actio uits as uatural type facial movemets. Detectig a uit set of actio uits for specific expressio is ot guarateed. Oe promisig approach for recogizig up to facial expressios itesities is to cosider whole facial image as sigle patter [4]. Kimura ad his colleagues have reported a method to costruct emotioal space usig 2D elastic et model ad K-L expasios for real images [7]. Their model is user idepedet ad gives some usuccessful results for ukow persos. Later Ohba proposed facial expressio space employig priciple compoet aalysis which is perso depedat [9]. III. Proposed Method: This project cosists of followig phases: 3.1. Face detectio based o ski color 3.2. Face extractio ad ehacemet 3.3. Face features extractio 3.4. Curve formatio usig Bezier curve Fuzzy Patters 3.6. Experimet Results 3.1 Face Detectio Based o Ski Color: Ski color plays a vital role i differetiatig huma ad o-huma faces. From the study it is observe that ski color pixels have a decimal value i the rage of 120 to 140. I this project, we used a trial ad error method to locate ski color ad o ski color pixels. But may of the times, system fails to detect whether a image cotais huma face or ot (i.e. for those images where there is a ski color backgroud).a image is segmeted ito ski color ad o-ski color pixels with the equatios 1 Page

2 120 Pxy eq were Pxy = pixel at positio xy The ski pixels values are set to 1(i.e. #FFFF) ad o ski pixels are set to 0(i.e. 0000). The pixels are collected ad set as per equatio If 3 lim i 1 ( 120 Pxy 140) = eq3.1.2 Else 1 3 lim i 1 ( 140 Pxy 120) = eq were = total umber of pixels of iput image The resultat image becomes as Origial Image Fig (Phase I) Ski ad o-ski pixels 3.2 Face Extractio ad Ehacemet Literature review poit out that, FACS system techique is based o face features extractios like eye, ose, mouth, etc. I this project, we miimize the umber of features (i.e. oly eyes ad mouth) but give the more weightage for fuzzy rules formatios from these extracted features. Face extractios cosist of followig steps Let W ad H are the width ad height of ski ad o-pixel image as show i fig Read the pixel at positio (0,H/2) which is a middle of i.e. left side of image. Travers a distace D 1 = W/6 i horizotal directio to get the start boudary pixel of ski regio. Travers a distace D 2 = H/6 from a pixel positio (W/6, H/2) i upward directios. Same may do i dowward directio ad locate the poits X 1, X 2. Travers a distace D 3 =W/3 from the poit X 1 ad locate the poit X 3. Same do from the poit x 2 ad locate the poit X 4. Crop the square image as show. 2 Page

3 Fig Face recogitio After face extractio white regio pixels (i.e. ski pixels) are filled with ski color. A resultat image with ski color ad after ehacemet becomes as Fig image with ski color ad after dimesio ehacemet 3.3 Face Features Extractio Huma face is made up of eyes; ose, mouth ad chie etc. there are differeces i shape, size, ad structure of these orgas. So the faces are differs i thousads way. Oe of the commo methods for face expressio recogitio is to extract the shape of eyes ad mouth ad the distiguish the faces by the distace ad scale of these orgas. The face feature extractios cosist of followig steps Let W ad H are width ad height of a image show i Fig Mark pixel P i (W/2, H/2) as cetre of image. Travers a distace H/8 from the pixel P i towards upward ad mark a poit K 1. Travers a distace W/3 from the poit K 1 towards leftward ad mark a poit K 2. Travers a distace H/10 towards dowward from the poit K 2 ad mark a poit K 3. Travers a distace W/4 from the poit K 3 towards right ad mark the poit K 4. Travers a distace H/10 from the poit K 4 toward up ad mark the poit K 5. Same steps are repeated for extractig the right eye ad mark the poit N 2, N 3, N 4, ad N5. Travers a distace H/8 from the poit P i towards dowward ad mark the poit M 1. Travers a distace W/6 towards left ad right from the poit M 1 ad marks the poit M 2 ad M 3. Start with the poit M 2 traverse a distace H/10 towards dowward ad mark the poit M 4. Travers a distace W/6 from the poit M 4 towards right ad mark the poit M 5. Same may do from poit M 5 ad mark the poit M 6. Travers the distace H/10 from M 6 towards up that meets to the poit M 3. See the below image. Fig Feature Extractio Dist P i - K 1 H/8 Dist K 1 K 2 Dist M 1 M 2 Dist M 1 M 3 Dist M 4 M 5 Dist M 5 M 6 W/3 Dist K 2 K 3 Dist K 4 K 5 Dist N 2 N 3 Dist N 4 N 5 Dist M 2 M 4 Dist M 1 M 5 Dist M 3 M 6 H/10 Dist K 3 K 4 Dist K 5 K 2 Dist N 3 N 4 3 Page

4 Dist N 5 N 2 W/4 Neuro Fuzzy Model for Huma Face Expressio Recogitio 3.4 Curve formatio usig Bezier curve Eyes ad mouth as show i fig are located ad extracted. Bezier curve formed from this eyes ad mouth as per the equatio Q t = i=0 P i B i, t.eq Where each term i the sum is the product of bledig fuctio B i, (t) ad the cotrol poit P i. The B i, (t) is called as Berstei polyomials ad are defied by B i, t = C i t i 1 t i eq Where C i is the biomial co-efficiet give by: C i =! i!( i)...eq Left Eye Left Eye Bezier Curve Right Eye Right Eye Bezier Curve Mouth Mouth Bezier Curve Fig Bezier Curve Oce the Bezier curve formed features poits are located as show i below image. Left Eye Right Eye Mouth Fig Feature Poit Locatio The feature poit distace for left ad right eye is measured with 6 i=2 Z= (e H i siw i e H i cosw i )..eq i For left eye H i = L i ad for right eye H i. = R i. The feature poit distace for mouth is measured with Z = (e H i siw i /2 e H i cosw i /2)...eq i 2 i=1 were Z = feature poit distace = umber of feature poits A expressio id geerated from a average of Z ad Z as below. id= (Z+Z )/2.eq Page

5 3.5 Fuzzy Patters It is foud that expressio recogitio from the still image ever gives a correct output. A oe expressio id may also false ito more tha oe expressio domai. This project forms some fuzzy patters for expressios. See the set theory diagram below 3.6 Experimet Results Fig Fuzzy Expressio Patters Table Result Aalysis 5 Page

6 IV. Coclusio This paper proposes a ew approach for recogizig the category of facial expressio a estimatig the degree of cotiuous facial expressio chage from time sequetial images. This approach is based o persoal idepedet average facial expressio model. V. Future Work It is foud that, curret system fails to recogize a expressio of images cotaiig ski color backgroud ad multiple faces i sigle image. A strog system is required to diagose a expressio of such image. Refereces [1] Y. Yacoob ad L.S. Davis, Recogizig huma facial expressios from log image sequeces usig optical flow, IEEE Tras. Patter Aalysis & Machie Itelligece, Vol. 18, No 6, pp , [2] P. Ekma ad W. Friese, Facial Actio Codig System, Cosultig Psychologists Press, [3] K. Aizawa ad T. S. Huag, Model-based image codig: Advaced video codig techiques for very low bit-rate applicatios, Proc. IEEE, Vol. 83, No. 2, pp , [4] S. Kimura ad M. Yachida, Facial expressio recogitio ad its degree estimatio, Proc. Computer Visio ad Patter Recogitio, pp , [5] K. Ohba, G. Clary, T. Tsukada, T. Kotoku, ad K. Taie, Facial expressio commuicatio with FES, Proc. Iteratioal Coferece o Patter Recogitio, pp , [6] M.A. Bhuiya ad H. Hama, Idetificatio of Actors Draw i Ukiyoe Pictures, Patter Recogitio, Vol. 35, No. 1, pp , [7] M. B. Hmid ad Y.B. Jemaa, Fuzzy Classificatio, Image Segmetatio ad Shape Aalysis for Huma Face Detectio. Proc. Of ICSP, vol. 4, [8] M. Wag, Y. Iwai, M. Yachida, Expressio Recogitio from Time-Sequetial Facial Images by use of Expressio Chage Model, Proc. Third IEEE Iteratioal Coferece o Automatic Face ad Gesture Recogitio, pp , [9] M. I. Kha ad M. A. Bhuiya, Facial Expressio recogitio for Huma-Machie Iterface, ICCIT, Page