Face Shape Recovery and Recognition Using a Surface Gradient Based Statistical Model

Mario Castelán, Edwin R. Hancock

Abstract: In previous work [5] we have identified the gradient of the surface as the best representation for constructing Cartesian models of faces. This representation proved capable of capturing variations in facial shape over a sample of training data. The resulting statistical model can be fitted to Lambertian data using a simple non-exhaustive parameter adjustment procedure. In this paper we test the ability of the surface gradient-based model in two directions. First, we deal with non-lambertian images. Second, we use the model for face recognition purposes. Experiments with real world images suggest that the surface gradient model with the proposed parameter search can be used for accurate face shape recovery, showing a potential for face recognition applications.