Single biometric identification technology can be easily affected by various external factors, thus the recognition rate and stability are poor. A palmprint and palmvein image fusion recognition algorithm based on super-wavelet domain, namely NSCT-NBP, was proposed. Firstly, palmprint and palmvein images were decomposed by using Non-Subsampled Contourlet Transform (NSCT), then the obtained low-frequency and high-frequency sub-images were respectively merged by using the regional energy and image self-similarity principle. Secondly, the texture features were extracted from the fused images by using Neighbor based Binary Pattern (NBP), thus the eigenvector was got. Finally, the similarity of the fused images was calculated by Hamming distance of the feature vectors, to get Equal Error Rate (EER). The experiments were conducted on PolyU and a self-built database, the experimental results show that the lowest EER of NSCT-NBP algorithm were 0.72% and 0.96%, the identification time were only 0.0530 s and 0.0871 s. Compared with the current best palmprint-palmvein fusion method based on wavelet transform and Gabor filter, the EER of the two databases were reduced by 4% and 36.8%, respectively. The NSCT-NBP algorithm can effectively fuse the texture features of the palmprint-palmvein images and has good recognition performance. The fusion of palmprint-palmvein features can enhance the security of the recognition system.