Fast Fingerprint Classification with Deep Neural Networks

Daniel Michelsanti, Andreea-Daniela Ene, Yanis Guichi, Rares Stef, Kamal Nasrollahi, Thomas B. Moeslund

Research output: Contribution to book/anthology/report/conference proceedingArticle in proceedingResearchpeer-review

30 Citations (Scopus)
2235 Downloads (Pure)

Abstract

Reducing the number of comparisons in automated fingerprint identification systems is essential when dealing with a large database. Fingerprint classification allows to achieve this goal by dividing fingerprints into several categories, but it presents still some challenges due to the large intra-class variations and the small inter-class variations. The vast majority of the previous methods uses global characteristics, in particular the orientation image, as features of a classifier. This makes the feature extraction stage highly dependent on preprocessing techniques and usually computationally expensive. In this work we evaluate the performance of two pre-trained convolutional neural networks fine-tuned on the NIST SD4 benchmark database. The obtained results show that this approach is comparable with other results in the literature, with the advantage of a fast feature extraction stage.
Original languageEnglish
Title of host publicationVISiGRAPP 2017 : Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications
EditorsFrancisco Imai, Alain Tremeau, Jose Braz
Volume5
PublisherSCITEPRESS Digital Library
Publication date2017
Pages202-209
ISBN (Print)978-989-758-226-4
DOIs
Publication statusPublished - 2017
EventInternational Conference on Computer Vision Theory and Applications - Porto, Portugal
Duration: 27 Feb 20171 Mar 2017
Conference number: 12
http://www.visapp.visigrapp.org

Conference

ConferenceInternational Conference on Computer Vision Theory and Applications
Number12
Country/TerritoryPortugal
CityPorto
Period27/02/201701/03/2017
Internet address

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