%0 Journal Article
%T Reduction of False Rejection in an Authentication System by Fingerprint with Deep Neural Networks
%A St¨¦phane Kouamo
%A Claude Tangha
%A Olaf Kouamo
%J Journal of Software Engineering and Applications
%P 1-13
%@ 1945-3124
%D 2020
%I Scientific Research Publishing
%R 10.4236/jsea.2020.131001
%X 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 <em>NIST</em>4 international data reference and on the S<em>OCFing</em> database.
%K Authentication
%K Fingerprint
%K False Rejection
%K Neural Networks
%K Pattern Recognition
%K Deep Learning
%U http://www.scirp.org/journal/PaperInformation.aspx?PaperID=98951