For finger-vein recognition, many successful methods, such as Line Tracking (LT), Maximum Curvature (MC) and Wide Line Detector (WL), have been proposed. Among these, LT has a very slow matching and feature-extraction phase, and LT, MC and WL are translation and rotation dependent. Moreover, we show in the paper, they are affected by noise. To overcome these drawbacks, we propose using popular feature descriptors widely used for several Computer Vision or Pattern Recognition (CVPR) problems in the literature. The CVPR descriptors we test include Histogram of Oriented Gradients (HOG), Fourier Descriptors (FD), Zernike Moments (ZM), Local Binary Patterns (LBP) and Global Binary Patterns (GBP), which have not been applied to the finger-vein recognition problem before. We compare these descriptors against LT, MC, and WL and evaluate their running times, performance and resilience against noise, rotation and translation. We report that the LT and WL methods accuracy are comparable to each other and WL gives the best accuracy, LT method's speed is the slowest. Our results indicate that WL can be used together with ZM and GBP in case of rotation and noise, respectively.