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%).

翻译：自动指纹识别系统（AFRSs）对呈现攻击（PAs）的脆弱性促进了呈现攻击检测（PAD）技术的蓬勃发展。然而，现有PAD方法受限于信息丢失和泛化能力不足，难以应对新型PA材料和指纹传感器。本文因此提出一种基于全局-局部模型的PAD方法（RTK-PAD），在一定程度上克服上述局限。该方法包含三个模块：1）全局模块；2）局部模块；3）反思模块。通过采用基于剪切的全局模块，可从整个指纹图像的非局部特征预测全局欺骗分数；而基于纹理修复的局部模块则从指纹图像块获得局部欺骗分数。这两个模块并非独立，而是通过所提出的反思模块相连——该模块根据全局欺骗分数为局部模块定位两个最具判别性的图像块。最终，通过平均全局与局部欺骗分数得到融合欺骗分数用于PAD。在LivDet 2017数据集上的实验结果表明：当误检率（FDR）为1.0%时，所提出的RTK-PAD方法可实现2.28%的平均分类误差（ACE）和91.19%的真实检测率（TDR），在TDR指标上比当前最优方法显著提升约10%（91.19%对比80.74%）。