One of the main issues of face recognition is to extract features from face images, which include both local and global features. We present a novel method to perform feature fusion at the feature level. First, global features are extracted by principal component analysis (PCA), while local features are obtained by active appearance model (AAM) and Gabor wavelet transform (GWT). Second, two types of features are fused by weighted concatenation. Finally, Euclidean and feature distances of fused features are applied to carry out a nearest neighbor classifier. The method is evaluated by the recognition rates and computation cost over two face image databases [AR (created by A. Martinez and R. Benavente) and SJTU-IPPR (Shanghai JiaoTong University-Institute of Image Processing and Pattern Recognition)]. Compared with PCA and elastic bunch graph matching (EBGM), the presented method is more effective. Though the recognition rate of the presented method is not as good as nonlinear feature combination (NFC), low computation cost is its superiority. In addition, experimental results show that the novel method is robust to variations over time, expression, illumination, and pose to a certain extent.