Master Assignment
A Patch-based approach to finger vein verification with a convolutional neural network
Type: Master M-CS
Period: Apr, 2018 - Feb, 2019
Student: Arıcan, T. (Tugce, Student M-CS)
Date final project: February 26, 2019
Supervisors:
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.