- Tytuł:
- 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 - Powiązania:
- https://bibliotekanauki.pl/articles/91725.pdf
- Data publikacji:
- 2019
- Wydawca:
- Społeczna Akademia Nauk w Łodzi. Polskie Towarzystwo Sieci Neuronowych
- Tematy:
-
restricted Boltzmann Machines
Deep Boltzmann Machines
deep learning
fingerprint spoofing detection
biometrics - Opis:
- 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
- Źródło:
-
Journal of Artificial Intelligence and Soft Computing Research; 2019, 9, 1; 41-49
2083-2567
2449-6499 - Pojawia się w:
- Journal of Artificial Intelligence and Soft Computing Research
- Dostawca treści:
- Biblioteka Nauki
Artykuł