An automatic scheme for 3D face recognition

A novel system for 3D face recognition is presented in this paper. For preprocessing, firstly, we reduce the noise and move spikes from all the 3D faces. Secondly, we use Iterative Closet Point (ICP) to align all 3D face with the first person, and then for each face, we find the nose tip. Once the nose tip is successfully found, we crop a region, which is defined by a sphere radius of 100 mm centered at the nose tip. Depth image are constructed using the region subsequently. In the recognition process, we firstly extract intrinsic discriminative information embedded in 3D faces using Gabor filter, and then Patched Weighted Discriminant Locality Preserving Projections (PWDLPP), which is ameliorated by Principle Component Analysis (PCA) to eliminate redundancy, is used into those Gabor faces. Recognition is achieved by using a Nearest Neighbor (NN) classifier finally.