A novel approach for Finger Vein verification based on self-taught learning

In this paper, we propose a method for user Finger Vein Authentication (FVA) as a biometric system. Using the discriminative features for classifying theses finger veins is one of the main tips that make difference in related works, thus we propose to learn a set of representative features, based on auto-encoders. We model the represented users' finger vein structure using a Gaussian distribution. Experimental results show that our method performs like a state-of-the-art method on SDUMLA-HMT benchmark.