  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.
