Robust face recognition from 2D and 3D images using structural Hausdorff distance
This paper presents a recognition system that is invariant to both viewing directions and facial expressions. This system is based on both 3D range data as well as 2D grey-level facial images. An irregular 2D mesh labeled by 12 landmarks and a 3D region labeled by four landmarks are defined in each face for feature extraction. Nodes of the 2D mesh are described by Gabor filter responses and 3D points are represented by Point signatures. A subset of mesh nodes (feature nodes), from which discriminating and expression-invariant 2D features can be extracted, is automatically selected for each subject. In the face library, each subject is represented, using both 2D and 3D features, by a frontal face with a neutral facial expression. To classify test faces under varying views or varying facial expressions, a robust Structural Hausdorff Distance is proposed to handle the possible case of matching incomplete data under structural constraints. The best matched model is determined based on the linear integration of matching results in 2D and 3D domains. Good experimental results have been obtained based on our database (involving 80 persons with different facial expressions and viewpoints).
face recognition structural hausdorff distance range data gabor filter responses point signature