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 language | English |
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Title of host publication | VISiGRAPP 2017 : Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications |
Editors | Francisco Imai, Alain Tremeau, Jose Braz |
Volume | 5 |
Publisher | SCITEPRESS Digital Library |
Publication date | 2017 |
Pages | 202-209 |
ISBN (Print) | 978-989-758-226-4 |
DOIs | |
Publication status | Published - 2017 |
Event | International Conference on Computer Vision Theory and Applications - Porto, Portugal Duration: 27 Feb 2017 → 1 Mar 2017 Conference number: 12 http://www.visapp.visigrapp.org |
Conference
Conference | International Conference on Computer Vision Theory and Applications |
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Number | 12 |
Country/Territory | Portugal |
City | Porto |
Period | 27/02/2017 → 01/03/2017 |
Internet address |