Evaluation of 3D face recognition using registration and PCA

We introduce a novel technique for face recognition by using 3D face data that has been reconstructed from a stereo camera system. The face data consists of a dense 3D mesh of vertices describing the facial shape and geometry as well as a 2D texture map describing the facial appearance of each subject. We propose a recognition algorithm based on two steps: The first step involves the cleaning of the facial surface followed by a registration and normalization to a standard template face. The second step involves the creation of a PCA model and the use of a reduced dimensionality face space for the calculation of facial similarity. We use this technique on 3D surface and texture data comprising 83 subjects. Our results demonstrate the wealth of 3D information on the face as well as the importance of standardization and noise elimination in the datasets.