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CNN Patch--Based Voting for Fingerprint Liveness Detection

Topics: Applications: Image Processing and Artificial Vision, Pattern Recognition, Decision Making, Industrial and Real World applications, Financial Applications, Neural Prostheses and Medical Applications, Neural based Data Mining and Complex Information Processing, Neural Network Software and Applications, Applications of Deep Neural networks, Robotics and Control Applications; Deep Learning

Authors: Amirhosein Toosi ; Sandro Cumani and Andrea Bottino

Affiliation: Politecnico di Torino, Italy

Keyword(s): Fingerprint Spoofing, Deep Learning, Fingerprint Segmentation, Patch based Classification.

Abstract: Biometric identification systems based on fingerprints are vulnerable to attacks that use fake replicas of real fingerprints. One possible countermeasure to this issue consists in developing software modules capable of telling the liveness of an input image and, thus, of discarding fakes prior to the recognition step. This paper presents a fingerprint liveness detection method founded on a patch--based voting approach. Fingerprint images are first segmented to discard background information. Then, small--sized foreground patches are extracted and processed by a well--know Convolutional Neural Network model adapted to the problem at hand. Finally, the patch scores are combined to draw the final fingerprint label. Experimental results on well--established benchmarks demonstrate a promising performance of the proposed method compared with several state-of-the-art algorithms.

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Paper citation in several formats:
Toosi, A.; Cumani, S. and Bottino, A. (2017). CNN Patch--Based Voting for Fingerprint Liveness Detection. In Proceedings of the 9th International Joint Conference on Computational Intelligence (IJCCI 2017) - IJCCI; ISBN 978-989-758-274-5; ISSN 2184-3236, SciTePress, pages 158-165. DOI: 10.5220/0006582101580165

@conference{ijcci17,
author={Amirhosein Toosi. and Sandro Cumani. and Andrea Bottino.},
title={CNN Patch--Based Voting for Fingerprint Liveness Detection},
booktitle={Proceedings of the 9th International Joint Conference on Computational Intelligence (IJCCI 2017) - IJCCI},
year={2017},
pages={158-165},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006582101580165},
isbn={978-989-758-274-5},
issn={2184-3236},
}

TY - CONF

JO - Proceedings of the 9th International Joint Conference on Computational Intelligence (IJCCI 2017) - IJCCI
TI - CNN Patch--Based Voting for Fingerprint Liveness Detection
SN - 978-989-758-274-5
IS - 2184-3236
AU - Toosi, A.
AU - Cumani, S.
AU - Bottino, A.
PY - 2017
SP - 158
EP - 165
DO - 10.5220/0006582101580165
PB - SciTePress

- Science and Technology Publications, Lda.
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