<p>Finger Vein Recognition (FVR) is an intrinsic biometric trait which can authenticate life. With the increase in cybercrime, FVR has gained popularity due to its applications in presentation attacks. Finger vein images are prone to low contrast, which can affect the system’s performance. To increase the system’s performance, we propose a technique where we use three simple and robust features viz. Local Binary Pattern (LBP), Histogram of Orientated Gradients (HOG), and Gabor feature. We separately classify these features of an finger vein ROI using K nearest neighbor (KNN) classifiers. We fuse these classifiers and monitor the overall performance. Finally, we evaluate and test the proposed system strength using two performance metrics, namely accuracy and equal error rate. The experimentation has been done on three publicly available finger vein dataset namely, SDUMLA, FV-USM, MMCBNU_6000. The proposed method outperforms other state-of-the-art techniques by achieving lowest identification equal error rate of 0.039% on the SDUMLA dataset, 0.064% on the FV-USM dataset, and 0.037% on the MMCBNU_6000 dataset, without using any enhancements. It also outperforms other state-of-the-art techniques on the data sets in terms of classification accuracy. To the best of our knowledge this is the first such work in this direction.</p>