Pose-invariant face recognition using a 3D deformable model

• Published in: Pattern recognition Vol. 36; no. 8; pp. 1835 - 1846
• Main Authors: Wai Lee, Mun, Ranganath, Surendra
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.08.2003
• Subjects: 3D deformable face model; 3D face shape representation; 3D face texture representation; Face recognition; Face synthesis; Pose estimation; 3D deformable face model; 3D face shape representation; Face synthesis; Face recognition; Pose estimation; 3D face texture representation
• ISSN: 0031-3203; 1873-5142
• DOI: 10.1016/S0031-3203(03)00008-6

The paper proposes a novel, pose-invariant face recognition system based on a deformable, generic 3D face model, that is a composite of: (1) an edge model, (2) a color region model and (3) a wireframe model for jointly describing the shape and important features of the face. The first two submodels are used for image analysis and the third mainly for face synthesis. In order to match the model to face images in arbitrary poses, the 3D model can be projected onto different 2D viewplanes based on rotation, translation and scale parameters, thereby generating multiple face-image templates (in different sizes and orientations). Face shape variations among people are taken into account by the deformation parameters of the model. Given an unknown face, its pose is estimated by model matching and the system synthesizes face images of known subjects in the same pose. The face is then classified as the subject whose synthesized image is most similar. The synthesized images are generated using a 3D face representation scheme which encodes the 3D shape and texture characteristics of the faces. This face representation is automatically derived from training face images of the subject. Experimental results show that the method is capable of determining pose and recognizing faces accurately over a wide range of poses and with naturally varying lighting conditions. Recognition rates of 92.3% have been achieved by the method with 10 training face images per person.