Combining Statistics of Geometrical and Correlative Features for 3D Face Recognition

In this paper, we present a new method for face recognition using range data. The proposed method is based on both global statistics of geometrical features and local statistics of correlative features of facial surfaces. Firstly, we analyze the performances of common geometrical representations by using global histograms for matching. Secondly, we propose a new method to encode the relationships between points and their neighbors, which are demonstrated to own great power to represent the intrinsic structure of facial surfaces. Finally, the two kinds of features are supposed to be complementary to some extent, and the combination of them is proven to be able to improve the recognition performance. All the experiments are performed on the full 3D face dataset of FRGC 2.0 which is the largest 3D face database so far. Promising results have demonstrated the effectiveness of our proposed method.