<abstract xmlns="http://www.w3.org/1999/xhtml"><p>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.</p></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.

Deep Features Extraction for Robust Fingerprint Spoofing Attack Detection

UFSCar - Federal University of São Carlos

Journal of Artificial Intelligence and Soft Computing Research

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

{"article-title":"Deep Features Extraction for Robust Fingerprint Spoofing Attack Detection"}

Biometric systems have been widely considered as a synonym of security. However, in recent years, malicious people are violating them by presenting forged...

Deep Features Extraction for Robust Fingerprint Spoofing Attack Detection

Pubblicato online: 20 ago 2018

Pagine: 41 - 49

Ricevuto: 13 dic 2017

Accettato: 20 dic 2017

DOI: https://doi.org/10.2478/jaiscr-2018-0023

Parole chiave
Restricted Boltzmann Machines, Deep Boltzmann Machines, Deep Learning, Fingerprint Spoofing Detection, Biometrics

© 2019 Gustavo Botelho de Souza et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Deep Features Extraction for Robust Fingerprint Spoofing Attack Detection

Pubblicato online: 20 ago 2018

Pagine: 41 - 49

Ricevuto: 13 dic 2017

Accettato: 20 dic 2017

DOI: https://doi.org/10.2478/jaiscr-2018-0023

Parole chiaveRestricted Boltzmann Machines, Deep Boltzmann Machines, Deep Learning, Fingerprint Spoofing Detection, Biometrics

© 2019 Gustavo Botelho de Souza et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Parole chiave
Restricted Boltzmann Machines, Deep Boltzmann Machines, Deep Learning, Fingerprint Spoofing Detection, Biometrics