Weightiness image partition in 3D face recognition

In this paper we present a novel algorithm suitable to improve the accuracy of 3D face recognition. In the proposed algorithm, we represent the 3D points by point signatures and partition the facial data into fifteen regions according to ¿three courtyards and five eyes¿ theory in pencil sketch on facial image in Chinese traditional art. Then in each partition we use ICA getting eigenvalues of feature and structure character and depth information to represent the 3D facial data. We assign different weightiness to each sub-image according to the result of sub-image variety. In order to match incomplete data under structural constraints, we proposed a reformative robust structural Hausdorff distance to handle these possible cases. Experiments on FRGC v2.0 data set show that the proposed algorithm is robust and effective to 3D face with expression, lighting and expression variance.