As a non-invasive optical imaging technique, optical coherence tomography (OCT) has proven promising for automatic fingerprint recognition system (AFRS) applications. Diverse approaches have been proposed for OCT-based fingerprint presentation attack detection (PAD). However, considering the complexity and variety of PA samples, it is extremely challenging to increase the generalization ability with the limited PA dataset. To solve the challenge, this paper presents a novel supervised learning-based PAD method, denoted as ISAPAD, which applies prior knowledge to guide network training and enhance the generalization ability. The proposed dual-branch architecture can not only learns global features from the OCT image, but also concentrate on layered structure feature which comes from the internal structure attention module (ISAM). The simple yet effective ISAM enables the proposed network to obtain layered segmentation features belonging only to Bonafide from noisy OCT volume data directly. Combined with effective training strategies and PAD score generation rules, ISAPAD obtains optimal PAD performance in limited training data. Domain generalization experiments and visualization analysis validate the effectiveness of the proposed method for OCT PAD.

翻译：作为一种非侵入式光学成像技术，光学相干断层成像(OCT)已被证明在自动指纹识别系统(AFRS)中具有应用前景。针对基于OCT的指纹呈现攻击检测(PAD)，业界已提出多种方法。然而，考虑到呈现攻击样本的复杂性和多样性，在有限的数据集上提升模型的泛化能力极具挑战性。为解决这一难题，本文提出一种新颖的基于监督学习的PAD方法，命名为ISAPAD，该方法通过引入先验知识指导网络训练以增强泛化能力。所提出的双分支架构不仅能从OCT图像中学习全局特征，还能聚焦于来自内结构注意力模块(ISAM)的层状结构特征。该简洁而有效的ISAM模块使所提网络能够直接从含噪的OCT体数据中提取仅属于真实指纹的层状分割特征。结合有效的训练策略与PAD分数生成规则，ISAPAD在有限训练数据条件下获得了最优的PAD性能。域泛化实验与可视化分析验证了所提方法在OCT PAD任务中的有效性。