3D to 2D: Facial intrinsic shape description maps

The face recognition based on 3D facial data overcomes the difficulties sensitive to illumination and pose variations in 2D face recognition systems. However, the high computational complexity restricts its practical applications. To simplify the description for 3D face data, a novel strategy to map 3D face data to 2D ones, called 2D intrinsic shape description map, was proposed in this paper. With the strategy, each 3D facial surface was firstly mapped homeomorphically onto a 2D lattice which keeps the local geometrical features based on the constraint discrete conformal. Then, a 2D intrinsic shape description map was obtained by combining 3D facial geometrical structure and appearance feature for simplifying 3D face representation and for verifying the recognition. The proposed strategy was compared to state-of-the-art 3D face recognition algorithms in the FRGC 2.0 and GavabDB database. The results show that the proposed strategy offers the rank-one rate of 90.6% when the pose change is greater than 60°, higher 5.9% than that of the existing method. Moreover, the single matching time is 7.89 s. As the strategy transforms the 3D face recognition into the 2D image recognition, it effectively reduces the complexity of data description and shows higher computation efficiency and robustness to a pose change.