Asymmetric 3D/2D face recognition based on LBP facial representation and canonical correlation analysis

In the recent years, 3D face recognition has emerged as a major solution to deal with the unsolved issues for reliable 2D face recognition, i.e. lighting condition and viewpoint variations. However, 3D method is currently limited by its registration and computation cost. In this paper, we propose to investigate a solution named asymmetric face recognition scheme, enrolling people in 3D environment but performing identification in 2D. The goal is to limit the use of 3D data to where it really helps to improve recognition performances. In our approach, local binary patterns (LBP) is used as an efficient facial representation for both 2D texture images and 3D range images. A weighted Chi square distance is used as matching score between the 2D LBP facial representations; canonical correlation analysis (CCA) is applied to learn the mapping between LBP-based range face images (3D) and LBP facial texture images (2D). Both matching scores are further fused to obtain the final result. Compared with the traditional 2D/2D algorithms, the proposed asymmetric face recognition scheme achieves better accuracy; while avoiding the high cost of data acquisition and computation in 3D/3D approaches.