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

Stéphane Kouamo,Olaf Kouamo,Claude Tangha

doi:10.4236/jsea.2020.131001

Stéphane Kouamo, Olaf Kouamo + Show 1 more

Open Access

https://doi.org/10.4236/jsea.2020.131001

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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.

Highlights

Fingerprints form a very specific class of models with singular particularity and proven statistical characteristics
This algorithm extracts the minutiae of the fingerprint with their relative orientations and classifies them according to the different classes already established; make the correspondence between two templates by simple probabilities calculations from a deep neural network
In order to reduce false rejection while maintaining a high recognition rate, we propose a new algorithm based on the use of deep neural network to perform learning and using stage, that contribute to reduce false rejection rate according to the existing methods

Summary

Introduction

Fingerprints form a very specific class of models with singular particularity and proven statistical characteristics. The problems of fingerprint recognition seem to be much more constraining than other classical problems of form recognition (such as the recognition of manuscript characters) where neural networks have already been successfully applied [1] [2] [3]. The authors prefer that the system erroneously reject a pattern when it does not meet a number of predetermined correspondence criteria. This phenomenon tends to lead to an increase in false rejection rates when the fingerprint recognition system is designed for authentication [4] [5]

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