AN IMPROVED 3D FACE RECOGNITION METHOD BASED ON NORMAL MAP

This chapter presents a face recognition method comparing a 3D facial model, to a gallery of previously enrolled faces. The geometrical features in the input mesh are represented by a bidimensional matrix, the normal map, storing local curvature data as the rgb components of a color image. The robustness of the proposed approach to facial expressions has been improved by a weighted mask automatically calculated for every subject in the gallery using a set of specific expressive variations. As comparison between normal maps is executed in a 2D space, the computational cost of this method is low. We present the results of our method on a 3d database of human faces, featuring different races, sex, ages, and expressions.