Deep features extraction for robust fingerprint spoofing attack detection

• Autorzy: Souza de Gustavo Botelho , Silva Santos da Daniel Felipe , Gonçalves Pires Rafael , Nilceu Marana Aparecido , Paulo Papa Joao

Identyfikatory

DOI

10.2478/jaiscr-2018-0023

• Języki publikacji: EN

Abstrakty

EN

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

Słowa kluczowe

EN

restricted Boltzmann Machines; Deep Boltzmann Machines; deep learning; fingerprint spoofing detection; biometrics

• Wydawca: University of Social Sciences
• Czasopismo: Journal of Artificial Intelligence and Soft Computing Research
• Rocznik: 2019
• Tom: Vol. 9, No. 1
• Strony: 41--49
• Opis fizyczny: Bibliogr. 19 poz., rys.

Twórcy

autor

Souza de Gustavo Botelho

autor

Silva Santos da Daniel Felipe

autor

Gonçalves Pires Rafael

autor

Nilceu Marana Aparecido

autor

Paulo Papa Joao

Bibliografia

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Uwagi

Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2019).