In this paper, we propose a new method to enhance the performance of face recognition under varying lighting condition. We try to combine strengths of illumination normalization, global and local features, feature-level and score-level fusion. Specially, we introduce two main contributions: 1) Firstly, we propose a feature-level fusion based on global and local Local binary patterns (LBP) features. Kernel PCA (KPCA) is used to reduce the dimension of the combined features. Then these features are used as input of SVM classifier; and 2) we further improve significantly the performance of face recognition by applying score-level fusion between global and local LBP features based SVM. An optimal method based Particle Swarm Optimization (PSO) is used to find optimal weights to fuse the aforementioned information at score-level. The experiment results on Korean face database demonstrate that our proposed methods outperform standard global feature, local feature and other well-know methods. Specifically, the best recognition rate is 100% for indoor images and 94.5% for outdoor images.