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Authors: Daniel Michelsanti ; Andreea-Daniela Ene ; Yanis Guichi ; Rares Stef ; Kamal Nasrollahi and Thomas B. Moeslund

Affiliation: Aalborg University, Denmark

Keyword(s): Fingerprint Classification, Transfer Learning, Convolutional Neural Networks.

Related Ontology Subjects/Areas/Topics: Computer Vision, Visualization and Computer Graphics ; Features Extraction ; Image and Video Analysis

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.

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Paper citation in several formats:
Michelsanti, D.; Ene, A.; Guichi, Y.; Stef, R.; Nasrollahi, K. and Moeslund, T. (2017). Fast Fingerprint Classification with Deep Neural Networks. In Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2017) - Volume 5: VISAPP; ISBN 978-989-758-226-4; ISSN 2184-4321, SciTePress, pages 202-209. DOI: 10.5220/0006116502020209

@conference{visapp17,
author={Daniel Michelsanti. and Andreea{-}Daniela Ene. and Yanis Guichi. and Rares Stef. and Kamal Nasrollahi. and Thomas B. Moeslund.},
title={Fast Fingerprint Classification with Deep Neural Networks},
booktitle={Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2017) - Volume 5: VISAPP},
year={2017},
pages={202-209},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006116502020209},
isbn={978-989-758-226-4},
issn={2184-4321},
}

TY - CONF

JO - Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2017) - Volume 5: VISAPP
TI - Fast Fingerprint Classification with Deep Neural Networks
SN - 978-989-758-226-4
IS - 2184-4321
AU - Michelsanti, D.
AU - Ene, A.
AU - Guichi, Y.
AU - Stef, R.
AU - Nasrollahi, K.
AU - Moeslund, T.
PY - 2017
SP - 202
EP - 209
DO - 10.5220/0006116502020209
PB - SciTePress