Automatic Asymmetric 3D-2D Face Recognition

3D Face recognition has been considered as a major solution to deal with unsolved issues of reliable 2D face recognition in recent years, i.e. lighting and pose variations. However, 3D techniques are currently limited by their high registration and computation cost. In this paper, an asymmetric 3D-2D face recognition method is presented, enrolling in textured 3D whilst performing automatic identification using only 2D facial images. The goal is to limit the use of 3D data to where it really helps to improve face recognition accuracy. The proposed approach contains two separate matching steps: Sparse Representation Classifier (SRC) is applied to 2D-2D matching, while Canonical Correlation Analysis (CCA) is exploited to learn the mapping between range LBP faces (3D) and texture LBP faces (2D). Both matching scores are combined for the final decision. Moreover, we propose a new preprocessing pipeline to enhance robustness to lighting and pose effects. The proposed method achieves better experimental results in the FRGC v2.0 dataset than 2D methods do, but avoiding the cost and inconvenience of data acquisition and computation of 3D approaches.