Unimodal analysis of finger-vein (FV) and finger dorsal texture (FDT) has been investigated intensively for personal recognition. Unfortunately, it is not robust to segmentation error and noise. Motivated by distribution trait of FV and FDT in a finger, we present a multimodal recognition method, called weighted sparse fusion for identification (WSFI), which uses FV and FDT images with fusion applied at the pixel level. Firstly, a new fused test sample, a weighted sum of FV and FDT images per-pixel, is obtained, the weight values are computed according to the reconstruction error of each FV and FDT pixels. And a new dictionary associated with the fused test sample is constructed in the same manner. Secondly, for every new fused test sample and the dictionary associated with it, the sparse representation based classification (SRC) is implemented for recognition. Experiments show that comparing with state-of-art techniques, our method achieves significant improvement in terms of accuracy rate (AR) equal error rate (EER).