Open AccessFingerprint Authenticity Classification Algorithm based-on Distance of Minutiae using Convolutional Neural Network
Author(s) -
Hariyanto Hariyanto,
Sarifuddin Madenda,
Sunny Arief Sudiro,
Tubagus Maulana Kusuma
Publication year - 2021
Publication title -
incomtech : jurnal telekomunikasi dan komputer/incomtech : jurnal telekomunikasi dan komputer
Language(s) - English
Resource type - Journals
eISSN - 2579-6089
pISSN - 2085-4811
DOI - 10.22441/incomtech.v11i3.13770
Subject(s) - minutiae , fingerprint (computing) , computer science , convolutional neural network , artificial intelligence , pattern recognition (psychology) , euclidean distance , construct (python library) , object (grammar) , authentication (law) , deep learning , artificial neural network , fingerprint recognition , computer vision , programming language , computer security
Fingerprint identification systems are vulnerable to attempted authentication fraud by creating fake fingerprints that mimic the live. This paper proposes method to detect whether a fingerprint is live fingerprint or fake fingerprint using Convolutional Neural Network (CNN). We construct a features database of distances among minutiaes of fingerprints, where the distance calculation is based-on Euclidean Distance. Furthermore, the distance features database that has been constructed is classified using the CNN. CNN is a deep learning method designed for machine learning processes so that computers recognize objects in an image and this method has capability classifying an object. The numerical results have shown that the best accuracy achieves 99.38% when the learning rate is 0.001 with the epoch of 100.