TELKOMNIKA Telecommunication, Computing, Electronics and Control
Deep fingerprint classification network

TELKOMNIKA Telecommunication, Computing, Electronics and Control
Deep fingerprint classification network

Classification
Deep learning
Fingerprint

Fingerprint is one of the most well-known biometrics that has been used for
personal recognition. However, faked fingerprints have become the major
enemy where they threat the security of this biometric. This paper proposes an
efficient deep fingerprint classification network (DFCN) model to achieve
accurate performances of classifying between real and fake fingerprints. This
model has extensively evaluated or examined parameters. Total of 512 images
from the ATVS-FFp_DB dataset are employed. The proposed DFCN achieved
high classification performance of 99.22%, where fingerprint images are
successfully classified into their two categories. Moreover, comparisons with
state-of-art approaches are provided.

Abdulsattar M. Ibrahim, Abdulrahman K. Eesee, Raid Rafi Omar Al-Nima

http://journal.uad.ac.id/index.php/TELKOMNIKA

Nov 25, 2020

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