University of Twente Student Theses

Login

Optimization of a patch-based finger vein verification with a convolutional neural network

Arican, Tugce (2019) Optimization of a patch-based finger vein verification with a convolutional neural network.

This is the latest version of this item.

[img] PDF
3MB
Abstract:Finger veins are accepted as unique for each person, and since finger veins are below skin, they are more resistant to forgery. In this paper, a patch-based approach using a convolutional neural network is explored. The patch-based approach increases the number of labeled data, and helps against brightness variations, yet, at the same time, it introduces its own issues such as determining the patch properties, combining the patches, and registration of the image pairs. This research proposes an optimisation to the patch based finger vein verification approach by addressing these issues. The patch-based system has achieved 0.3\% of equal error rate and 0.999 area under the curve on UTFVP and 6.6\% of equal error rate and 0.9692 AUC on SDUMLA-HMT after proposed optimisations.
Item Type:Essay (Master)
Faculty:EEMCS: Electrical Engineering, Mathematics and Computer Science
Subject:54 computer science
Programme:Computer Science MSc (60300)
Link to this item:http://purl.utwente.nl/essays/77591
Export this item as:BibTeX
EndNote
HTML Citation
Reference Manager

 

Repository Staff Only: item control page