3-D Shape Matching for Face Analysis and Recognition

Abstract

Abstract:The aims of this paper are to introduce a 3-D shape matching scheme for automatic face recognition and to demonstrate its invariance to pose and facial expressions. The core of this scheme lies on the combination of non-rigid deformation registration and statistical shape modelling. While the former matches 3-D faces regardless of facial expression variations, the latter provides a low-dimensional feature vector that describes the deformation after the shape matching process, thereby enabling robust identification of 3-D faces. In order to assist establishment of accurate dense point correspondences, an isometric embedding shape representation is introduced, which is able to transform 3-D faces to a canonical form that retains the intrinsic geometric structure and achieve shape alignment of 3-D faces independent from individual's facial expression. The feasibility and effectiveness of the proposed method was investigated using standard publicly available Gavab and BU-3DFE databases, which contain faces expressions and pose variations. The performance of the system was compared with the existing benchmark approaches and it demonstrates that the proposed scheme provides a competitive solution for the face recognition task with real-world practicality.

3-D Face Recognition Using Geodesic-Map Representation and Statistical Shape Modelling

“…In this paper, we propose a new geodesic-map representation for 3-D faces, which is an extension of original work proposed by Quan et al [19]. The proposed method preserves the intrinsic geometrical information related to the identity of the face.…”

“…Since the models have learnt non-rigid deformation from faces across different identities in the training set and can be adapted to match the deformation in the new dataset, the TPS warping techniques described in Section 4 is no longer needed. Furthermore the use of the proposed method speeds up the whole fitting process for the new dataset and saves up to 70% computation time on average compared with the widely used modified ICP registration [27] [19]. A few examples of the fitting results generated using the LPP-based method are shown in Figure 6.…”