CNN Patch--Based Voting for Fingerprint Liveness Detection

Amirhosein Toosi, Sandro Cumani, Andrea Bottino

2017

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 Harvard Style

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 - Volume 1: IJCCI, ISBN 978-989-758-274-5, pages 158-165. DOI: 10.5220/0006582101580165


in Bibtex Style

@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 - Volume 1: IJCCI,},
year={2017},
pages={158-165},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006582101580165},
isbn={978-989-758-274-5},
}


in EndNote Style

TY - CONF

JO - Proceedings of the 9th International Joint Conference on Computational Intelligence - Volume 1: IJCCI,
TI - CNN Patch--Based Voting for Fingerprint Liveness Detection
SN - 978-989-758-274-5
AU - Toosi A.
AU - Cumani S.
AU - Bottino A.
PY - 2017
SP - 158
EP - 165
DO - 10.5220/0006582101580165