Fast Fingerprint Classification with Deep Neural Networks

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

Publikation: Bidrag til bog/antologi/rapport/konference proceedingKonferenceartikel i proceedingForskningpeer review

10 Citationer (Scopus)
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Resumé

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.
OriginalsprogEngelsk
TitelVISiGRAPP 2017 : Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications
RedaktørerFrancisco Imai, Alain Tremeau, Jose Braz
Vol/bind5
ForlagSCITEPRESS Digital Library
Publikationsdato2017
Sider202-209
ISBN (Trykt)978-989-758-226-4
DOI
StatusUdgivet - 2017
BegivenhedInternational Conference on Computer Vision Theory and Applications - Porto, Portugal
Varighed: 27 feb. 20171 mar. 2017
Konferencens nummer: 12
http://www.visapp.visigrapp.org

Konference

KonferenceInternational Conference on Computer Vision Theory and Applications
Nummer12
LandPortugal
ByPorto
Periode27/02/201701/03/2017
Internetadresse

Fingerprint

Feature extraction
Identification (control systems)
Classifiers
Neural networks
Deep neural networks

Citer dette

Michelsanti, D., Ene, A-D., Guichi, Y., Stef, R., Nasrollahi, K., & Moeslund, T. B. (2017). Fast Fingerprint Classification with Deep Neural Networks. I F. Imai, A. Tremeau, & J. Braz (red.), VISiGRAPP 2017: Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (Bind 5, s. 202-209). SCITEPRESS Digital Library. https://doi.org/10.5220/0006116502020209
Michelsanti, Daniel ; Ene, Andreea-Daniela ; Guichi, Yanis ; Stef, Rares ; Nasrollahi, Kamal ; Moeslund, Thomas B. / Fast Fingerprint Classification with Deep Neural Networks. VISiGRAPP 2017: Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications. red. / Francisco Imai ; Alain Tremeau ; Jose Braz. Bind 5 SCITEPRESS Digital Library, 2017. s. 202-209
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title = "Fast Fingerprint Classification with Deep Neural Networks",
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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Michelsanti, D, Ene, A-D, Guichi, Y, Stef, R, Nasrollahi, K & Moeslund, TB 2017, Fast Fingerprint Classification with Deep Neural Networks. i F Imai, A Tremeau & J Braz (red), VISiGRAPP 2017: Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications. bind 5, SCITEPRESS Digital Library, s. 202-209, International Conference on Computer Vision Theory and Applications, Porto, Portugal, 27/02/2017. https://doi.org/10.5220/0006116502020209

Fast Fingerprint Classification with Deep Neural Networks. / Michelsanti, Daniel; Ene, Andreea-Daniela; Guichi, Yanis; Stef, Rares; Nasrollahi, Kamal; Moeslund, Thomas B.

VISiGRAPP 2017: Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications. red. / Francisco Imai; Alain Tremeau; Jose Braz. Bind 5 SCITEPRESS Digital Library, 2017. s. 202-209.

Publikation: Bidrag til bog/antologi/rapport/konference proceedingKonferenceartikel i proceedingForskningpeer review

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Michelsanti D, Ene A-D, Guichi Y, Stef R, Nasrollahi K, Moeslund TB. Fast Fingerprint Classification with Deep Neural Networks. I Imai F, Tremeau A, Braz J, red., VISiGRAPP 2017: Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications. Bind 5. SCITEPRESS Digital Library. 2017. s. 202-209 https://doi.org/10.5220/0006116502020209