Three-dimensional facial surface modeling applied to recognition

Applications related to game technology, law-enforcement, security, medicine or biometrics are becoming increasingly important, which, combined with the proliferation of three-dimensional (3D) scanning hardware, have made that 3D face recognition is now becoming a promising and feasible alternative to 2D face methods. The main advantage of 3D data, when compared with traditional 2D approaches, is that it provides information that is invariant to rigid geometric transformations and to pose and illumination conditions. One key element for any 3D face recognition system is the modeling of the available scanned data. This paper presents new 3D models for facial surface representation and evaluates them using two matching approaches: one based on Support Vector Machines and another one on Principal Component Analysis (with a Euclidean classifier). Also, two types of environments were tested in order to check the robustness of the proposed models: a controlled environment with respect to facial conditions (i.e. expressions, face rotations, etc) and a noncontrolled one (presenting face rotations and pronounced facial expressions). The recognition rates obtained using reduced spatial resolution representations (a 77.86 % for non-controlled environments and a 90.16% for controlled environments, respectively) show that the proposed models can be effectively used for practical face recognition applications.