Internal Structure Attention Network for Fingerprint Presentation Attack Detection from Optical Coherence Tomography
This work addresses the problem of detecting presentation attacks in fingerprint recognition systems for security applications, but it is incremental as it builds on existing OCT-based PAD methods with a novel architectural enhancement.
The paper tackles the challenge of improving generalization in fingerprint presentation attack detection (PAD) using optical coherence tomography (OCT) by proposing ISAPAD, a supervised learning method that applies prior knowledge and a dual-branch architecture to focus on layered structure features, achieving optimal PAD performance with limited training data.
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.