Fingerprint Presentation Attack Detector Using Global-Local Model
Haozhe Liu Wentian Zhang Feng Liu Haoqian Wu Linlin Shen
The vulnerability of automated fingerprint recognition systems (AFRSs) to
presentation attacks (PAs) promotes the vigorous development of PA detection
(PAD) technology. However, PAD methods have been limited by information loss
and poor generalization ability, resulting in new PA materials and fingerprint
sensors. This paper thus proposes a global-local model-based PAD (RTK-PAD)
method to overcome those limitations to some extent. The proposed method
consists of three modules, called: 1) the global module; 2) the local module;
and 3) the rethinking module. By adopting the cut-out-based global module, a
global spoofness score predicted from nonlocal features of the entire
fingerprint images can be achieved. While by using the texture
in-painting-based local module, a local spoofness score predicted from
fingerprint patches is obtained. The two modules are not independent but
connected through our proposed rethinking module by localizing two
discriminative patches for the local module based on the global spoofness
score. Finally, the fusion spoofness score by averaging the global and local
spoofness scores is used for PAD. Our experimental results evaluated on LivDet
2017 show that the proposed RTK-PAD can achieve an average classification error
(ACE) of 2.28% and a true detection rate (TDR) of 91.19% when the false
detection rate (FDR) equals 1.0%, which significantly outperformed the
state-of-the-art methods by $\sim$10% in terms of TDR (91.19% versus 80.74%).