With the great development in information technology, the commonly used biometrics have becomevery easy to hack such as face, voice and fingerprint etc. This has led to the discovery of new horizons in thisfield. One of the safest biometrics of today is finger vein- But this technique faces some unique difficulties, the mostcommonly used being that the vein pattern is difficult to remove because finger vein images are always low in quality,significantly hampered the feature extraction and classification stages. For this purpose, professional algorithmsmust be considered with the conventional hardware for capturing finger- vein images using Red Surface MountedDiode ( SMD) led. For capturing images, Canon 750D camera with micro lens is used. For high quality imagesthe integrated micro lens is used, and with some adjustments it can also obtain finger print. Features extractionwas used by a combination of Hierarchical Centroid and Histogram of Gradients. Results were evaluated with KNearest Neighbor and Deep Neural Networks using 6 fold stratified cross validation. Results displayed improvementas compared to three latest benchmarks in this field that used 6- fold validation ( 5- samples for training and onesample for test) for SDUMLA- HMT. The work novelty is owing to the hardware design of the sensor within thefinger- vein recognition system to obtain, simultaneously, highly secured recognition with low computation time,finger vein and finger print at low cost, unlimited users for one device and open source.