DEEP FEATURES EXTRACTION FOR ROBUST FINGERPRINT SPOOFING ATTACK DETECTION - Souza, Gustavo Botelho de; Silva Santos, Daniel Felipe da [UNESP]; Pires, Rafael Goncalves; Marana, Aparecido Nilceu [UNESP]; Papa, Joao Paulo [UNESP] (Sciendo, 2019)
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.
Keywords: Restricted Boltzmann Machines; Deep Boltzmann Machines; Deep Learning; Fingerprint Spoofing Detection; Biometrics