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Thomas, T.J. (2000) Locally-Connected Neural Network for Fingerprint Recognition. Proceedings of the IASTED International Conference, Intelligent Systems and Control, Honolulu, 2000, 431-441.

has been cited by the following article:

• 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.