Real-time normalization and feature extraction of 3D face data using curvature characteristics

The method of normalization and real-time feature extraction of 3D face data (range data) is presented. The step of normalization of range data is performed first using the symmetry of the defined facial section pattern and characteristics of changes of the pattern according to head rotations. Normalization of the data for head rotations can not only give strong constraints on the positions official features but also reduce the dimension of parameters used in the deformable template matching. Facial features are found in a range image, which is obtained by projection of the normalized range data, using the deformable templates of eyes, nose and mouth. For reliable feature detection, surface curvatures which can represent a local surface shape are used in this step. We define the energy functions of each template and the conditions of major control points using curvature information. Finally, the facial features are positioned in 3D space by back-mapping to the original range data. The back-mapping is the inverse process of getting the facial range image.