Orthogonal View-Based Attention Network for Layer Segmentation of 3D OCT Fingerprints

Recently, optical coherence tomography (OCT) has been used to noninvasively image the 3D structure of fingertip skin at high resolution. Unlike traditional 2D sensors (e.g., infrared light or capacitive technologies), the friction ridge information in 3D OCT fingerprint measurements requires reconstruction through layer segmentation. Accurate layer segmentation is helpful for fingerprint recognition and antispoofing applications. OCT volumes contain information corresponding to different directions that naturally provide complementary views. Inspired by this fact, we propose a novel orthogonal view-based attention network called OVA-Net, which exploits orthogonal views to learn the complementary information implied in the 3D fingerprint structure. Specifically, 3D convolutions and an A-line-based attention module are proposed in the B-scan view to model the long short-term intraslice correlations, whereas their counterparts in the C-scan view aim to model interslice correlations. An optical flow-based attention module is also proposed in the B-scan view to extract correlations between B-scans, which complements the interslice correlation learned in the C-scan view. Features from orthogonal views are progressively incorporated into a fusion pipeline for 3D layer segmentation. The effectiveness of OVA-Net is comprehensively evaluated in terms of layer segmentation accuracy, fingerprint reconstruction quality, and recognition performance.