Study on fingerprint authentication systems using convolutional neural networks

Abstract

This paper presents a study on the efficiency of using convolutional neural networks for biometric security systems. Fingerprint identification in images is mainly treated and a VisualGeometryGroup-16 architecture integrated in a Siamese network is used. The Siamese network uses two metrics to determine the similarity between two input images, each metric having a certain experimentally determined threshold. The datasets for training, validation and testing were taken from three sources to increase diversity and contain synthetic changes with different levels of alteration. They are processed in such a manner that they can be received as input for the network. This study aims to establish whether using neural networks is reliable for a biometric security system.