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Use of Convolutional Neural Network for Fingerprint Liveness Detection

A.M. Chougule1 , M.A. Shah2

Section:Research Paper, Product Type: Journal Paper
Volume-7 , Issue-5 , Page no. 829-832, May-2019

CrossRef-DOI:   https://doi.org/10.26438/ijcse/v7i5.829832

Online published on May 31, 2019

Copyright © A.M. Chougule, M.A. Shah . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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IEEE Style Citation: A.M. Chougule, M.A. Shah, “Use of Convolutional Neural Network for Fingerprint Liveness Detection,” International Journal of Computer Sciences and Engineering, Vol.7, Issue.5, pp.829-832, 2019.

MLA Style Citation: A.M. Chougule, M.A. Shah "Use of Convolutional Neural Network for Fingerprint Liveness Detection." International Journal of Computer Sciences and Engineering 7.5 (2019): 829-832.

APA Style Citation: A.M. Chougule, M.A. Shah, (2019). Use of Convolutional Neural Network for Fingerprint Liveness Detection. International Journal of Computer Sciences and Engineering, 7(5), 829-832.

BibTex Style Citation:
@article{Chougule_2019,
author = {A.M. Chougule, M.A. Shah},
title = {Use of Convolutional Neural Network for Fingerprint Liveness Detection},
journal = {International Journal of Computer Sciences and Engineering},
issue_date = {5 2019},
volume = {7},
Issue = {5},
month = {5},
year = {2019},
issn = {2347-2693},
pages = {829-832},
url = {https://www.ijcseonline.org/full_paper_view.php?paper_id=4321},
doi = {https://doi.org/10.26438/ijcse/v7i5.829832}
publisher = {IJCSE, Indore, INDIA},
}

RIS Style Citation:
TY - JOUR
DO = {https://doi.org/10.26438/ijcse/v7i5.829832}
UR - https://www.ijcseonline.org/full_paper_view.php?paper_id=4321
TI - Use of Convolutional Neural Network for Fingerprint Liveness Detection
T2 - International Journal of Computer Sciences and Engineering
AU - A.M. Chougule, M.A. Shah
PY - 2019
DA - 2019/05/31
PB - IJCSE, Indore, INDIA
SP - 829-832
IS - 5
VL - 7
SN - 2347-2693
ER -

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Abstract

In recent years the biometric authentication systems are gaining popularity and became the integral part of security systems in every organization. Now a day’s spoof fingerprint detection is very important. There are several techniques proposed to tackle this problem. Liveness Detection is the method to detect real fingerprints. Since the emergence of deep learning the efficiency to solve this problem has been increased. In this paper we proposed a Convolution Neural Network (CNN) model which achieves average classification accuracy of around 93.12% on LivDet 2009, 85.16% on LivDet 2011, 86.76% on LivDet 2013, 82.20% on LivDet 2015 dataset.

Key-Words / Index Term

Convolutional Neural Network(CNN), Fingerprint Livness Detetction, Deep Learning, Livdet Dataset

References

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[2] O. Russakovsky, J. Deng, H. Su, J. Krause, S. Satheesh, S. Ma,Z. Huang, A. Karpa-thy, A. Khosla, M. Bernstein et al., “Imagenet large scale visual recognition challenge,International Journal of Computer Vision”, pp. 142, 2014.
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[4] G.L. Marcialis, A. Lewicke, B. Tan, P Coli, F. Roli, D. Grimberg, A. Congiu, A.Tidu, S Schuckers, and the LivDet 2009 Group, “First International Fingerprint Liveness Detection CompetitionLivDet 2009”, Proceedings of ICIAP, Sept 2009
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[7] V. Mura, L. Ghiani, G. L. Marcialis, F. Roli, D. A. Yambay, and S. A. Schuckers. LivDet 2015 _”fingerprint liveness detection competition 2015”. In IEEE 7th International Conference on Biometrics Theory, Applications and Systems, pages 16, 2015.