1. bookVolumen 9 (2019): Heft 1 (January 2019)
Zeitschriftendaten
License
Format
Zeitschrift
eISSN
2449-6499
Erstveröffentlichung
30 Dec 2014
Erscheinungsweise
4 Hefte pro Jahr
Sprachen
Englisch
access type Uneingeschränkter Zugang

Deep Features Extraction for Robust Fingerprint Spoofing Attack Detection

Online veröffentlicht: 20 Aug 2018
Volumen & Heft: Volumen 9 (2019) - Heft 1 (January 2019)
Seitenbereich: 41 - 49
Eingereicht: 13 Dec 2017
Akzeptiert: 20 Dec 2017
Zeitschriftendaten
License
Format
Zeitschrift
eISSN
2449-6499
Erstveröffentlichung
30 Dec 2014
Erscheinungsweise
4 Hefte pro Jahr
Sprachen
Englisch
Abstract

Biometric systems have been widely considered as a synonym of security. However, in recent years, malicious people are violating them by presenting forged traits, such as gelatin fingers, to fool their capture sensors (spoofing attacks). To detect such frauds, methods based on traditional image descriptors have been developed, aiming liveness detection from the input data. However, due to their handcrafted approaches, most of them present low accuracy rates in challenging scenarios. In this work, we propose a novel method for fingerprint spoofing detection using the Deep Boltzmann Machines (DBM) for extraction of high-level features from the images. Such deep features are very discriminative, thus making complicated the task of forgery by attackers. Experiments show that the proposed method outperforms other state-of-the-art techniques, presenting high accuracy regarding attack detection.

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