Consiglio Nazionale delle Ricerche

Tipo di prodottoArticolo in rivista
TitoloFingerprint classification based on deep learning approaches: Experimental findings and comparisons
Anno di pubblicazione2021
FormatoElettronico
Autore/iMilitello C.; Rundo L.; Vitabile S.; Conti V.
Affiliazioni autoriInstitute of Molecular Bioimaging and Physiology, Italian National Research Council (IBFM-CNR), 90015 Cefalù, Italy. Department of Radiology, University of Cambridge, Cambridge CB2 0QQ, UK; lr495@cam.ac.uk Department of Biomedicine, Neuroscience and Advanced Diagnostics (BiND), University of Palermo, 90127 Palermo, Italy; Faculty of Engineering and Architecture, University of Enna KORE, 94100 Enna, Italy
Autori CNR e affiliazioni
  • CARMELO MILITELLO
Lingua/e
  • inglese
AbstractBiometric classification plays a key role in fingerprint characterization, especially in the identification process. In fact, reducing the number of comparisons in biometric recognition systems is essential when dealing with large-scale databases. The classification of fingerprints aims to achieve this target by splitting fingerprints into different categories. The general approach of fingerprint classification requires pre-processing techniques that are usually computationally expensive. Deep Learning is emerging as the leading field that has been successfully applied to many areas, such as image processing. This work shows the performance of pre-trained Convolutional Neural Networks (CNNs), tested on two fingerprint databases--namely, PolyU and NIST--and comparisons to other results presented in the literature in order to establish the type of classification that allows us to obtain the best performance in terms of precision and model efficiency, among approaches under examination, namely: AlexNet, GoogLeNet, and ResNet. We present the first study that extensively compares the most used CNN architectures by classifying the fingerprints into four, five, and eight classes. From the experimental results, the best performance was obtained in the classification of the PolyU database by all the tested CNN architectures due to the higher quality of its samples. To confirm the reliability of our study and the results obtained, a statistical analysis based on the McNemar test was performed.
Lingua abstractinglese
Altro abstract-
Lingua altro abstract-
Pagine da750
Pagine a-
Pagine totali21
RivistaSymmetry (Basel)
Attiva dal 2009
Editore: Molecular Diversity Preservation International - Basel
Lingua: inglese
ISSN: 2073-8994
Titolo chiave: Symmetry (Basel)
Titolo proprio: Symmetry. (Basel)
Titolo abbreviato: Symmetry (Basel)
Numero volume della rivista13
Fascicolo della rivista5
DOI10.3390/sym13050750
Verificato da referee-
Stato della pubblicazionePublished version
Indicizzazione (in banche dati controllate)
  • Scopus (Codice:2-s2.0-85105621804)
  • ISI Web of Science (WOS) (Codice:000654616500001)
Parole chiavefingerprint classification; deep learning; convolutional neural networks; fingerprint features
Link (URL, URI)http://www.scopus.com/inward/record.url?eid=2-s2.0-85105621804&partnerID=q2rCbXpz
Titolo parallelo-
Licenza-
Scadenza embargo-
Data di accettazione26/04/2021
Note/Altre informazioni-
Strutture CNR
  • IBFM — Istituto di bioimmagini e fisiologia molecolare
Moduli/Attività/Sottoprogetti CNR-
Progetti Europei-
Allegati
Symmetry 2021, 13, 750
Tipo documento: application/pdf