TITLE:
Reduction of False Rejection in an Authentication System by Fingerprint with Deep Neural Networks
AUTHORS:
Stéphane Kouamo, Claude Tangha, Olaf Kouamo
KEYWORDS:
Authentication, Fingerprint, False Rejection, Neural Networks, Pattern Recognition, Deep Learning
JOURNAL NAME:
Journal of Software Engineering and Applications,
Vol.13 No.1,
January
31,
2020
ABSTRACT: Faultless authentication of individuals by fingerprints results in high false rejections rate for rigorously built systems. Indeed, the authors prefer that the system erroneously reject a pattern when it does not meet a number of predetermined correspondence criteria. In this work, after discussing existing techniques, we propose a new algorithm to reduce the false rejection rate during the authentication-using fingerprint. This algorithm extracts the minutiae of the fingerprint with their relative orientations and classifies them according to the different classes already established; then, make the correspondence between two templates by simple probabilities calculations from a deep neural network. The merging of these operations provides very promising results both on the NIST4 international data reference and on the SOCFing database.