Face modeling for recognition
3D human face models have been widely used in applications such as facial animation, video compression/coding, augmented reality, head tracking, facial expression recognition, human action recognition, and face recognition. Modeling human faces provides a potential solution to identifying faces with variations in illumination, pose, and facial expression. We propose a method of modeling human faces based on a generic face model (a triangular mesh model) and individual facial measurements containing both shape and texture information. The modeling method adapts a generic face model to the given facial features, extracted from registered range and color images, in a global-to-local fashion. It iteratively moves the vertices of the mesh model to smoothen the non-feature areas, and uses the 2.5D active contours to refine feature boundaries. The resultant face model has been shown to be visually similar to the true face. Initial results show that the constructed model is quite useful for recognizing nonfrontal views.