A Novel Approach For Finger Vein Verification Based on Self-Taught Learning
This addresses biometric security for user verification, but it is incremental as it builds on existing methods with a new feature learning approach.
The paper tackled finger vein authentication by learning representative features using autoencoders and modeling finger veins with a Gaussian distribution, achieving performance comparable to state-of-the-art on the SDUMLA-HMT benchmark.
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 autoencoders. We model the user finger vein using a Gaussian distribution. Experimental results show that our algorithm perform like a state-of-the-art on SDUMLA-HMT benchmark.