3D Face recognition using Corresponding Point Direction Measure and depth local features

A new scheme for 3D face recognition is presented in this paper. Firstly, we use Iterative Closet Point (ICP) to align all 3D faces with the first 3D face. Secondly, we reduce noise, especially the noise which in front of the face, and remove the spikes. Then we detect the nose tip point. 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. Then we use the Corresponding Point Direction Measure (CPDM) to matching the 3D face with the gallery 3D faces and get the score. At the same time, we use the region to construct depth image, and get the Gabor feature, LBP feature, principle component of the depth image. Finally, we fuse the CPDM result, Gabor feature, LBP feature, and principle component of depth image to finish the recognition. This paper presents a new method for matching 3D face and a new scheme for 3D face recognition. Experiments demonstrated the efficiency and effectiveness of the new method.