3D Face recognition has emerged in the recent years as a major solution to deal with the unsolved issues for reliable 2D face recognition, namely lighting condition and viewpoint variations. However, the 3D approach is currently limited by its cost of registration and computational complexity. In this paper, we propose to investigate an asymmetric face recognition solution, i.e. enrolling people in 3-D but performing authentication in 2-D. The goal is to limit the use of 3-D data to where it really helps to improve recognition performances. In our approach, Local Binary Patterns (LBP) are used as an efficient facial representation both for 2D texture and 3D range facial images. A weighted Chi square distance is computed as the matching score between 2D LBP facial texture images Canonical Correlation Analysis (CCA) is introduced to learn the mapping between LBP-based range facial image (3D) andLBP facial texture image (2D). Both matching scores are further fused to obtain the final result. Compared with the traditional 2D-2D algorithms, our LBP and CCA-based asymmetric face recognition solution scheme achieves better performance while avoiding the registration cost and computational complexity in 3D-3D approaches.