Reduction of False Rejection in an Authentication System by Fingerprint with Deep Neural Networks

作     者：Stéphane Kouamo Claude Tangha Olaf Kouamo 

作者机构：Department of Computer Science University of Yaounde I Yaounde Cameroon Department of Mathematics and Statistics Université Denis Didérot (Paris VII) Paris France 

出 版 物：《Journal of Software Engineering and Applications》 (软件工程与应用（英文）)

年 卷 期：2020年第13卷第1期

页      面：1-13页

学科分类：0809[工学-电子科学与技术（可授工学、理学学位）] 08[工学] 

主　　题：Authentication Fingerprint False Rejection Neural Networks Pattern Recognition Deep Learning 

摘      要：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.