A deformation model to reduce the effect of expressions in 3D face recognition

Abstract

In 3D face recognition, most work utilizes the rigid parts of face surfaces for matching to exclude the distortion caused by expressions. However, across a broad range of expressions, the rigid parts may not always be uniform and cover large parts of faces. On the other hand, the non-rigid regions of face surfaces also contain useful information for recognition. In this paper, we include the non-rigid regions besides the rigid parts for 3D face recognition. A deformation model is proposed to deform the non-rigid regions to the shapes that are more similar between intra-personal samples but less similar between inter-personal samples. Together with the rigid regions, the deformed parts make samples more discriminable so that the effect of expressions is reduced. The first part of our model uses the target gradient fields from enrolled samples to depress the distortion of the non-rigid regions. The gradient field works in the differential domain. According to the Poisson equation, a smooth deformed shape can be computed by a linear system. The second part of the model is the definition of a surface property that determines the deformation ability of different face regions. Unlike the target gradient fields that improve the similarity of intra-personal samples, the original topology and surface property can keep inter-personal samples sufficiently dissimilar. Our deformation model can be used to improve existing 3D face recognition methods. Experiments are carried out on FRGC and BU-3DFE databases. There are about 8–10% improvements obtained after applying this deformation model to the baseline ICP method. Compared with other deformation models, the experimental results show that our model has advantages on both recognition performance and computational efficiency.