  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.
