3D Novel Face Sample Modeling for Face Recognition

In this paper, we present a method to generate novel realistic 3D face model using a model trained from real 3D face data. 3D face samples are an important data platform for model training, algorithm design. Subject to the constraint of acquisition technology, sizes of current 3D face databases are relatively small and insufficient. The presented method is used to solve this problem. First real 3D face data are collected as training sample and a parametric global model is learned based on them. Then a local model is established based on surface quilting. We use the global model to build a novel coarse face model. Then, we condition the local model with the global model. With appropriate choices of local and global models it is possible to reliably generate novel realistic 3D face data that do not correspond to any individual in the training data. Finally we apply our model to face recognition to examine the meaning of our work.