Statistical shape modelling for expression-invariant face analysis and recognition

Abstract

Paper introduces a 3-D shape representation scheme for automatic face analysis and identification, and demonstrates its invariance to facial expression. The core of this scheme lies on the combination of statistical shape modelling and non-rigid deformation matching. While the former matches 3-D faces with facial expression, the latter provides a low-dimensional feature vector that controls the deformation of model for matching the shape of new input, thereby enabling robust identification of 3-D faces. The proposed scheme is also able to handle the pose variation without large part of missing data. To assist the establishment of dense point correspondences, a modified freeform-deformation based on B-spline warping is applied with the help of extracted landmarks. The hybrid iterative closest point method is introduced for matching the models and new data. The feasibility and effectiveness of the proposed method was investigated using standard publicly available Gavab and BU-3DFE datasets, which contain faces with expression and pose changes. The performance of the system was compared with that of nine benchmark approaches. The experimental results demonstrate that the proposed scheme provides a competitive solution for face recognition.