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.

CNN Patch-Based Voting for Fingerprint Liveness Detection / Toosi, Amirhosein; Cumani, Sandro; Bottino, Andrea. - STAMPA. - (2017). ((Intervento presentato al convegno 9th International Joint Conference on Computational Intelligence (IJJCI 2017) tenutosi a Funchal, Madeira Portugal nel 1-3 November 2017.

CNN Patch-Based Voting for Fingerprint Liveness Detection

TOOSI, AMIRHOSEIN;CUMANI, SANDRO;BOTTINO, ANDREA
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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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11583/2680586
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