A new scheme for 3D face recognition

A novel system for 3D face recognition is presented in this paper. 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. Then the depth image is projected into Gabor-based Supervised Locality Sensitive Discriminant Analysis (GISLSDA) space, which is improved by Gabor wavelet and Two-Directional Two Dimensions Principal Component Analysis (2D²PCA). Recognition is achieved by using a Nearest Neighbor (NN) classifier finally. This method is robust to changes in facial expressions and poses. The experimental results show that the new algorithm outperforms the other popular approaches reported in the literature and achieves much higher accurate recognition rate.