Aiming at the identification problem with poor robustness based on current finger-knuckle-print recognition methods, a finger-knuckle-print recognition method using non-subsampled Shearlet transform (NSST) and Tetrolet energy features is proposed in this paper. First, histogram equalization is used to adjust the gray level of the image to reduce the influence of uneven light distribution on the recognition system. Second, the NSST and its inverse transform are used to obtain the reconstructed image after denoising, and Tetrolet transform is performed on it to establish the energy surface of low-frequency image. Finally, the energy difference surface is obtained by subtracting the energy surface of different images, and the variance of the surface is further calculated. Based on this, the classification and recognition of different finger joint print images are carried out. The experiment results on the HKPU-FKP, IIT Delhi-FK, and HKPU-CFK databases and their noise databases show that the correct recognition rate of the method is 98.0392%, the shortest recognition time is 0.0497 s, and the lowest equal error rate is 2.5646%. Compared with other algorithms, the algorithm improves the performance of the finger-knuckle-print recognition system, which is feasible and effective.