Deformation Analysis for 3D Face Matching

Current two-dimensional image based face recognition systems encounter difficulties with large facial appearance variations due to the pose, illumination and expression changes. Utilizing 3D information of human faces is promising to handle 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 face surface matching framework to take into account both rigid and non-rigid variations to match a 2.5D face image to a 3D face model. The rigid registration is achieved by a modified Iterative Closest Point (ICP) algorithm. The thin plate spline (TPS) model is applied to estimate the deformation displacement vector field, which is used to represent the non-rigid deformation. For the purpose of face matching, the non-rigid deformations from different sources are identified, which is formulated as a two-class classification problem: intra-subject deformation vs. inter-subject deformation. The deformation classification results are integrated with the matching distances to make the final decision. Experimental results on a database containing 100 3D face models and 98 2.5D scans with smiling expression show that the number of errors is reduced from 28 to 18.