论文标题
基于OCT的指纹表现攻击检测和重建的统一表示方法
A Uniform Representation Learning Method for OCT-based Fingerprint Presentation Attack Detection and Reconstruction
论文作者
论文摘要
光学相干断层扫描（OCT）对指纹成像的技术为捕获皮肤层深度信息的能力而为指纹识别开辟了新的研究潜力。如果可以充分利用深度信息，则可以开发健壮和高安全性自动指纹识别系统（AFRSS）。然而，在现有研究中，基于深度信息的表现攻击检测（PAD）和地下指纹重建被视为两个独立的分支，从而导致AFRS构建的高计算和复杂性。首先，我们设计了一个新型的语义分割网络，该网络仅通过基于OCT的指纹的真实手指切片训练，以从这些切片（也称为B型扫描）中提取多个地下结构。从网络中得出的潜在代码直接用于有效检测PA，因为它们包含丰富的地下生物学信息，该信息与PA材料独立，并且对未知PA具有强大的鲁棒性。同时，采用了分段的地下结构来重建多个地下2D指纹。通过使用基于传统2D指纹的现有成熟技术，可以轻松实现识别。广泛的实验是在我们自己已建立的数据库上进行的，该数据库是最大的基于OCT的指纹数据库，具有2449卷。在PAD任务中，我们的方法可以从最先进的方法中提高0.33％的ACC。对于重建性能，我们的方法以0.834 miou和0.937 pa的形式达到了最佳性能。通过与表面2D指纹的识别性能进行比较，我们提出的方法对高质量地下指纹重建的有效性得到了进一步证明。
The technology of optical coherence tomography (OCT) to fingerprint imaging opens up a new research potential for fingerprint recognition owing to its ability to capture depth information of the skin layers. Developing robust and high security Automated Fingerprint Recognition Systems (AFRSs) are possible if the depth information can be fully utilized. However, in existing studies, Presentation Attack Detection (PAD) and subsurface fingerprint reconstruction based on depth information are treated as two independent branches, resulting in high computation and complexity of AFRS building.Thus, this paper proposes a uniform representation model for OCT-based fingerprint PAD and subsurface fingerprint reconstruction. Firstly, we design a novel semantic segmentation network which only trained by real finger slices of OCT-based fingerprints to extract multiple subsurface structures from those slices (also known as B-scans). The latent codes derived from the network are directly used to effectively detect the PA since they contain abundant subsurface biological information, which is independent with PA materials and has strong robustness for unknown PAs. Meanwhile, the segmented subsurface structures are adopted to reconstruct multiple subsurface 2D fingerprints. Recognition can be easily achieved by using existing mature technologies based on traditional 2D fingerprints. Extensive experiments are carried on our own established database, which is the largest public OCT-based fingerprint database with 2449 volumes. In PAD task, our method can improve 0.33% Acc from the state-of-the-art method. For reconstruction performance, our method achieves the best performance with 0.834 mIOU and 0.937 PA. By comparing with the recognition performance on surface 2D fingerprints, the effectiveness of our proposed method on high quality subsurface fingerprint reconstruction is further proved.
