A Novel Approach For Finger Vein Verification Based on Self-Taught Learning

Mohsen Fayyaz; Masoud PourReza; Mohammad Hajizadeh Saffar; Mohammad Sabokrou; Mahmood Fathy

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arXiv · 1508.03710

A Novel Approach For Finger Vein Verification Based on Self-Taught Learning

Mohsen Fayyaz·Masoud PourReza·Mohammad Hajizadeh Saffar·Mohammad Sabokrou·Mahmood Fathy

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Abstract

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.

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Mohsen Fayyaz, Masoud PourReza, Mohammad Hajizadeh Saffar, Mohammad Sabokrou, Mahmood Fathy. 2015-08-15. A Novel Approach For Finger Vein Verification Based on Self-Taught Learning. https://arxiv.org/abs/1508.03710

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