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.
@article{arxiv.1508.03710,
title = {A Novel Approach For Finger Vein Verification Based on Self-Taught Learning},
author = {Mohsen Fayyaz and Masoud PourReza and Mohammad Hajizadeh Saffar and Mohammad Sabokrou and Mahmood Fathy},
journal= {arXiv preprint arXiv:1508.03710},
year = {2015}
}
Comments
4 pages, 4 figures, Submitted Iranian Conference on Machine Vision and Image Processing