3D Facial Expression Modeling for Recognition

摘要

Current two-dimensional image based face recognition systems encounter difficulties with large variations in facial appearance due to the pose, illumination and expression changes. Utilizing 3D information of human faces is promising for handling the pose and lighting variations. While the 3D shape of a face does not change due to head pose (rigid) and lighting changes, it is not invariant to the non-rigid facial movement and evolution, such as expressions and aging effect. We propose a facial surface matching framework to match multiview facial scans to a 3D face model, where the (non-rigid) expression deformation is explicitly modeled for each subject, resulting in a person-specific deformation model. The thin plate spline (TPS) is applied to model the deformation based on the facial landmarks. The deformation is applied to the 3D neutral expression face model to synthesize the corresponding expression. Both the neutral and the synthesized 3D surface models are used to match a test scan. The surface registration and matching between a test scan and a 3D model are achieved by a modified Iterative Closest Point (ICP) algorithm. Preliminary experimental results demonstrate that the proposed expression modeling and recognition-by-synthesis schemes improve the 3D matching accuracy.