Under the influence of factors such as the variations in illumination, facial expressions and noises, the accuracy of many current face recognition methods is not very satisfactory. A face recognition method named FW-SRC isproposed which is based on the fusion of wavelet packet sub-images (FW) and Sparse Representation-based Classification (SRC) via the combining research and analysis of Principal Component Analysis (PCA), wavelet packet decomposition (WPD) of images and SRC. The proposed method extracted features from the weighted fusion images of wavelet packet sub-images and then constructed the feature space by the weighted fusion images, and constructed sparse dictionary by projection of samples on the feature space, at last classified the faces by SRC. Experimental results on the Yale B, AR and CMU PIE face databases for the robustness test show that the proposed method improves the accuracy for face recognition and it has a good performance.