The creation of personalised 3D characters has evolved to provide a high degree of realism in both appearance and animation. Further to the creation of generic characters the capabilities exist to create a personalised character from images of an individual. This provides the possibility of immersing an individual into a virtual world. Feature detection, particularly on the face, can be used to

greatly enhance the realism of the model. To address this innovative contour based templates are used to extract an individual from four orthogonal views providing localisation of the face. Then adaptive facial feature extraction from multiple views is used to enhance the realism of the model

Boyle, Eamonn

Molloy, Derek

Murphy, Noel

Uscilowski, Bartlomiej

English

The creation of personalised 3D characters has evolved to provide a high degree of realism in both appearance and animation. Further to the creation of generic characters the capabilities exist to create a personalised character from images of an individual. This provides the possibility of immersing an individual into a virtual world. Feature detection, particularly on the face, can be used to\ud
greatly enhance the realism of the model. To address this innovative contour based templates are used to extract an individual from four orthogonal views providing localisation of the face. Then adaptive facial feature extraction from multiple views is used to enhance the realism of the model

Name not available

USING FACIAL FEATURE EXTRACTION TO ENHANCE THECREATION OF 3D HUMAN MODELSEamonn Boyle†, Bartlomiej Uscilowski‡, Derek Molloy†, Noel Murphy‡†Vision System Group ‡Centre for Digital Video ProcessingDublin City University Dublin City UniversityDublin 9, Ireland Dublin 9, Irelande-mail: {eamonn.boyle, bartek.uscilowski, derek.molloy, noel.murphy}@eeng.dcu.ieABSTRACTThe creation of personalised 3D characters has evolved to providea high degree of realism in both appearance and animation. Fur-ther to the creation of generic characters the capabilities exist tocreate a personalised character from images of an individual. Thisprovides the possibility of immersing an individual into a virtualworld. Feature detection, particularly on the face, can be used togreatly enhance the realism of the model. To address this innova-tive contour based templates are used to extract an individual fromfour orthogonal views providing localisation of the face. Thenadaptive facial feature extraction from multiple views is used toenhance the realism of the model.1. INTRODUCTIONThe development of personalised interactive computer games andthe creation of realistic humans to populate virtual worlds is a chal-lenge that is currently seeing significant advances with differentapplications for the construction of models being continually de-veloped. These techniques have two main foci; the creation ofhighly realistic models using animation tools [6] and the develop-ment of photo realistic models from images [4]. In addition theprocess of creating realistic human models has shifted from a re-search topic involving the use of high specification equipment incontrolled environments to creation of highly realistic models us-ing off-the-shelf technologies. This is illustrated using the EyeToycamera for the Sony PlayStation2 which facilitates many interac-tive applications including the creation of a 3D model of an in-dividual’s head facilitating the immersion of the individual in theparticular game.Creation of models for immersion in virtual worlds and gameshave different requirements. In the gaming environments it is par-ticularly advantageous to modify an underlying model because themoves in the games are often dependent on the size of the charac-ter and predefined animation, for example “take three steps left”.While the complete reconstruction of the individual from a seriesof images can provide a highly realistic model for use in virtualworlds, its realism may be compromised by constraints within thegaming environment.To accurately modify an underlying model or create 3D mod-els it is necessary to have more than one view otherwise it is im-possible to extract accurate 3D information. Four orthographicimages of the individual are captured using the capture process de-scribed in [2, 4, 8]. This provides sufficient information to textureand modify the underlying model to take on the appearance of theindividual and in particular the four views provide complete andunoccluded views of the head. Our method extends the researchby Lee and Hilton by using deformable B-spline templates [1] thatare constrained to accurately extract the individual from their en-vironment and to localise the head.The face of an individual provides significant detail that is im-portant in determining the quality and realism of the model that isproduced. Thus in developing any technique it is important thatthe face is well detailed. This can be achieved by finding corre-spondences between features on the face of the individual and theface of the model. The predominant features on an individual’sface are the nose the eyes and the mouth, which provide geomet-ric dependencies and constraints for precise face localisation. Weuse these dependencies to accurately position the individual’s faceon the model in order to enhance the realism of the 3D humanmodel. However the locations of these features are commonly usedin other applications, e.g. the normalised facial image for the cre-ation of the MPEG-7 Face Recognition (FR) descriptor is obtainedusing the predefined eye locations [5]. Although the MPEG-7 FRdescriptor creation requires only the frontal view of the face wepropose to use both frontal and side views to enhance the auto-mated localisation of the features and for texturing the underlyingmodel.There are a number of different approaches to automated fa-cial feature extraction which use either gray-scale images or colourimages of faces. The deformable templates [1] are among the mostpopular techniques such as symmetry operator, Hough transform,Gabor Wavelet Transform (GWT) [3] and Artificial Neural Net-works (ANN) [9]. Other methods are based on eigenvectors, fish-erfaces, fuzzy logic, Hidden Markov Model, stereovision or Inde-pendent Component Analysis (ICA). All of these algorithms workon the frontal view images of faces, usually under specified light-ing conditions, constant resolution or deal only with good qual-ity images. We propose the colour based segmentation technique,with no predefined conditions for the facial feature localisation inthe frontal view images and the shape validation method from theside views.The structure of the system for creation a personalised 3Dmodel is presented in figure 1. Elements of the process are de-scribed in details in the following sections, firstly the image cap-turing and fitting the template for extraction the full body contourfollowed by description of facial features extraction techniques.Finally the texturing process is outlined and some results of thepersonalized models are presented.4 OrthogonalImages- Full Body ContourExtraction?FacialRegionExtraction?FacialComponentsLocalisation??3D BodyModelTexturing3D FacialModelTexturingr -Personalized3D ModelFig. 1. Facial features localization algorithm2. IMAGE CAPTURE AND TEMPLATE FITTINGFour images of the individual are captured from a real uncon-strained environment. The images are captured with the stationarycamera and between each capture the individual rotates 90o. Thisenables the capture of the four orthographic projections. In this ap-proach the individual stands approximately 3m from the camera.The user adopts the pose in figure 2. In this figure the individ-ual is required to stand with their legs apart and their arms raised.This pose is necessary to accurately locate of the crotch and thearmpits. In addition to this the individual should look directly atthe camera or little above the camera. This is to ensure that all thefeatures of an individual’s face are visible which is important forthe extraction of the facial features.(a) (b)Fig. 2. Example of the capture images.It is necessary to have accurate segmentation of the individualfrom the background to ensure that the background is not texturedto the underlying model. This is achieved using the template infigure 3(b). The template is essentially an active B-spline snake(closed contour) that is continually minimising while being con-strained and attracted to strong edges. The actual template is basedon the Active Contour Model (ACM) proposed by Kass et al. [7].One of the initial requirements for using ACMs is that the contourmust be initialised in the vicinity of object to be extracted. Thisis realised by subtracting the front and back views and subtractingthe side views. This enables the estimation of the bounding box,see figure 3(a) that is used to scale the template for a particularindividual.(a) (b)Fig. 3. Results of subtraction and the initial template with theconstraints.The initial template that is used for the front and back viewsis shown in the figure 3(b). The constraints to force the contour tobehave in the desired manner are included. The main constraintswere necessary between the two legs and under the arms becausesometimes the contours converged to the same edge. In figure 3(b)the constraints are indicated using the dashed lines between thelegs and under the arms. The arrows at the end of these lines areused to indicate that the control points cannot move towards eachother. In addition to this the control points marked A, B and Care constrained in the direction that they can move, i.e. they canonly move in the three directions indicated on the figure. This is toensure that they will accurately locate the armpits and the crotch.3. FACE LOCALISATIONSince the facial region is well localised by the final body contour itcan be used for the precise localisation of the facial features. In thecase of the frontal view of the face the colour segmentation is ap-plied to the facial region and the facial components are found. Thesegmentation is carried out in three steps: an initial segmentationfollowed by feature extraction and classification.The initial segmentation is based on the Recursive ShortestSpanning Tree (RSST) algorithm which is an automated hierar-chical segmentation algorithm providing homogeneous connectedregions with an easy to define granularity. The feature extractionemploys the Expectation-Maximisation (EM) algorithm to obtainthe accurate features boundaries. This is based on the observationthat the histogram of the facial features such as eyes, eyebrows orlips can be modelled with the Gaussian distribution and these fea-tures differ in colour from the rest of the facial region which is skincoloured. Thus they can be extracted using this colour segmenta-eye valley)nose tip9mouth valleyﬀchin cliffﬀ(a) (b)Fig. 4. Facial features locations in (a) front view and (b) side viewhead image.tion technique. Simple heuristics are added to classify the regionsof the particular facial components which are based on the colourfeatures and geometric dependencies between the facial compo-nents.The eyes and the centre of the lips localised with the RSSTand the EM algorithm are illustrated in figure 4(a).Other techniques for the frontal view facial feature extractioncan be applied instead of the colour segmentation. The deformabletemplates as proposed by Yuille et al. [1] provide a good alterna-tive for the localisation of the facial features if the initial positionof the eye and mouth templates can be placed close to the features.This can be easily achieved since the precise location of the face isobtained from the body contour extracted in section 2.The feature location in the side views of the head is achievedby scanning the shape of the head from the top to the bottom andsearching for the valleys and peaks in the head boundary usinggradient analysis. When considering the right hand side view ofthe head as shown in figure 4(b), the nose tip can be found as thepeak on the right hand side boundary, the eyes should be placedin the valley above the nose peak and the lips form small hillsbelow the nose tip. The valley of the chin provides informationabout the bottom boundary of the face, which in general can notbe reliably located in the front view because of shadow. If the earin the side view of the head is visible and not covered with hair itslocation can be found using the segmentation technique used forthe features extraction from frontal view images.4. TEXTURING AND PERSONALISING THE MODELTo texture the underlying model four silhouettes corresponding tothe captured images are generated. These are used for the estab-lishment of correspondences between the captured images and theunderlying model. The approach is based on feature extraction al-gorithm in [4]. The establishment of features is essential to enablethe accurate texturing of the model. Having the correspondencesenables the texturing of the model on a part-by-part basis ensuringthat scale of the different body parts is preserved. In the texturingalgorithm proposed in [2] the normal vectors for each tri-face ofthe model are used to texture the model. The major limitation withthis approach is that the face is not accurately textured and thisreduces the realism of the model.To overcome the limitation of this approach the facial featuresare located on the face of the model and on the face in the capturedimages. These are used to align the individuals face with that ofthe model. These features are indicated in figure 4 and the geo-metric relationships are shown in figure 5. These relationships anddistances are used for scaling and validation.The distance between the eyes de (see figure 5(a)) is used forscaling the texturing image in the horizontal direction. This dis-(a) (b)Fig. 5. The locations of the facial features and the distances be-tween them used for deformating the underlying model.tance is used also for the creation of the MPEG-7 FR descriptorand is essential for defining the size of the head [5]. The verticalsize of the texturing image is adjusted using the distance betweenthe eyes and the centre of the mouth dm. The three points repre-senting eyes and mouth locations are used to calculate the centre ofthe facial region and to position the facial texture on the underlyingmodel in the front view.The side view images deliver information required for the en-hancement of the head model viewed from the sides of the head.As shown in figure 5(b), the triangle drawn between the eye, themouth and the ear is used for determining the scaling factor for sideimages. The distance between the ear and the eye dd determinesthe depth of the head model whilst the distance between mouth andeye should be equal to the distance dm in the frontal view imageand can be used for the validation of the vertical dimensions of theimage. In case of ear covered with hair and not visible the headboundary is used for finding the depth of the head model.Once the facial information has been aligned the texturingtechnique in [2] is used to texture the facial model.5. RESULTSThe results obtained using the proposed method to enhance the re-alism of the human models are presented in figure 6 and figure 7.It can be clearly seen that the quality of the models that use the fa-cial features in the texturing of the model provide superior results.The images in figure 2 are used to texture the underlying model infigure 6. In this figure the complete model is textured using the in-formation from the four orthogonal views. Figure 6(a) shows themodel textured without using the facial features to position andscale the facial texture and figure 6(b) shows the model with thealigned facial features.In figure 7 the results for a second set of images are combinedto texture the same underlying model. In this set of results only theupper body an the head are shown and the difference can be easilyseen when the features on the model are positioned accurately. Infigure 7(a) the frontal image is shown. In figure 7 (b) the simplytextured model is shown and figure 7 (c) and (d) shows two viewsof the textured model when the facial features are used to positionthe facial texture.The enhanced model still does not provide high detail of theeye regions, although this technique can be used to texture themodel using a separate high resolution facial image or mappingeach of the facial feature separately.(a) (b)Fig. 6. The created human model with (a) misplaced facial featuresand (b) aligned facial features.6. CONCLUSIONS AND FUTURE WORKThe approach for enhancing the realism of the 3D human modelemploying the facial features localisation that is presented in thispaper shows that the realism of the model can be substantially in-creased by considering the facial features when texturing the facialregions. It is believed that the presented method can improve therealism of the 3D human models for low-resolution devices suchas PDAs or mobile phones providing a low-cost solution to the cre-ation of personalised 3D models without requiring costly full 3Dreconstruction.The experiments provide promising results as they show im-provement in the realism of the models when the texturing facialimage is placed and scaled accurately. Further improvements tothe realism of the model could be achieved by mapping each ofthe facial components separately, since the current technique, al-though improving the realism, still does not provide sufficient de-tail to warrant facial animation. The extraction of complete bodyand facial information from a single set of images would be ad-vantageous for many applications. An alternative to achieve thehigher level of detail is to capture a separate facial image and touse this to increase the realism of the model.To gauge the quality of the model it is intended to also pro-vide objective measurement of the realism of the 3D human modelusing the face recognition descriptors in MPEG-7 and to comparethe reconstructed face with real photos of the individual.7. REFERENCES[1] Blake and M. Isard. Active Contours. Springer Verlag, 1998.[2] Eamonn Boyle. Generation and animation of virtual humans.In IWSSIP04, pages 143–146, Sep 2004. Poznan, Poland.[3] R. Chelappa, C. Wilson, and S. Sirohey. Human and machinerecognition of faces: A survey. Proceedings of the IEEE,83(5), May 1995.(a) (b)(c) (d)Fig. 7. The created human model with misplaced facial features.[4] A. Hilton, D. Beresford, T. Gentils, R. Smith, and W. Sun.Virtual people: Capturing human models to populate virtualworlds. In IEEE International Conference on Computer Ani-mation, pages 174–185, 1999.[5] ISO/IEC JTC1/SC29/WG11, N4980. Overview of the MPEG-7 Standard, Jul 2002.[6] N. Kalra, N.M. Thalmann, L. Moccozet, G. Sannier, A. Aubel,and D. Thalmann. Real-time animation of virtual humans.IEEE Computer Graphics and Applications, 18(5):42–56,1998.[7] M. Kass, A. Watkin, and D. Terzopoulos. Snakes: Activecontour models. International Journal of Computer Vision,1(4):231–331, 1987.[8] W. Lee, T. Goto, and N.M. Thalmann. Making h-anim bodies.In Avatars2000, Nov 2000. Lausanne, Switzerland.[9] M.J.T. Reinders, R.W.C. Koch, and J.J. Gebrands. Locatingfacial features in image sequences using neural networks. In IIInt. Conf. on Automatic Face and Gesture Recognition, pages230–235, 1997. Killington, USA.

Using facial feature extraction to enhance the creation of 3D human models

The creation of personalised 3D characters has evolved to provide a high degree of realism in both appearance and animation. Further to the creation of generic characters the capabilities exist to create a personalised character from images of an individual. This provides the possibility of immersing an individual into a virtual world. Feature detection, particularly on the face, can be used to
greatly enhance the realism of the model. To address this innovative contour based templates are used to extract an individual from four orthogonal views providing localisation of the face. Then adaptive facial feature extraction from multiple views is used to enhance the realism of the model

Using facial feature extraction to enhance the creation of 3D human models

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