Fingerprint recognition systems, as any other biometric system, can be subject to attacks, which are usually carried out using artificial fingerprints. Several approaches to discriminate between live and fake fingerprint images have been presented to address this issue. These methods usually rely on the analysis of individual features extracted from the fingerprint images. Such features represent different and complementary views of the object in analysis, and their fusion is likely to improve the classification accuracy. However, very little work in this direction has been reported in the literature. In this work, we present the results of a preliminary investigation on multiview analysis for fingerprint liveness detection. Experimental results show the effectiveness of such approach, which improves previous results in the literature.

On Multiview Analysis for Fingerprint Liveness Detection / Toosi, Amirhosein; Cumani, Sandro; Bottino, ANDREA GIUSEPPE. - STAMPA. - LNCS:(2015), pp. 143-150. (Intervento presentato al convegno CIARP 2015 - XX Iberoamerican Congress on Pattern Recognition tenutosi a Montevideo, Uruguay nel November 9-12, 2015) [10.1007/978-3-319-25751-8_18].

On Multiview Analysis for Fingerprint Liveness Detection

TOOSI, AMIRHOSEIN;CUMANI, SANDRO;BOTTINO, ANDREA GIUSEPPE
2015

Abstract

Fingerprint recognition systems, as any other biometric system, can be subject to attacks, which are usually carried out using artificial fingerprints. Several approaches to discriminate between live and fake fingerprint images have been presented to address this issue. These methods usually rely on the analysis of individual features extracted from the fingerprint images. Such features represent different and complementary views of the object in analysis, and their fusion is likely to improve the classification accuracy. However, very little work in this direction has been reported in the literature. In this work, we present the results of a preliminary investigation on multiview analysis for fingerprint liveness detection. Experimental results show the effectiveness of such approach, which improves previous results in the literature.
2015
978-3-319-25750-1
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11583/2615845
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