Abstract

The paper proposes a novel, pose-invariant face recogI#TfA system based on a deformable,geform 3D face model, that is a composite of: (1) anedg model, (2) a color regrf model and (3) a wireframe model for jointlydescribing the shape and important features of the face. The #rst two submodels are used forimag analysis and the third mainly for face synthesis. In order to match the model to faceimagy in arbitrary poses, the 3D model can be projected onto di#erent 2D viewplanes based on rotation, translation and scale parameters, therebygrebyf:Ik multipleface-imag templates (in di#erent sizes and orientations). Face shape variationsamong people are taken into account by the deformation parameters of the model. Given an unknown face, its pose is estimated by modelmatching and the system synthesizes faceimagj of known subjects in the same pose. The face is then classi#ed as the subject whose synthesizedimag is most similar. The synthesizedimagh are gref#k#j using a 3D face representation scheme which encodes the 3D shape and texture characteristics of the faces. This face representation is automatically derived fromtraining faceimag: of the subject. Experimental results show that the method is capable ofdetermining pose and recog##fA: faces accurately over a wide rang ofposes and with naturallyvarying liging conditions. Recogions. rates of92.3% have been achieved by the method with 10training faceimagk per person.

Citations