Rapid Joint Extraction of Internal Fingerprints and Sweat Pores From OCT Volumes via Depth Projection

Optical Coherence Tomography (OCT) has emerged as a powerful technique for capturing subcutaneous internal fingerprints (IF) and internal sweat pores (ISP), offering enhanced security over surface biometrics. However, existing methods typically rely on slice-by-slice B-scan segmentation, which are computationally intensive, sensitive to speckle noise, and incapable of simultaneously extracting IF and ISP. To address these limitations, this paper proposes REI-Net, a novel end-to-end network designed for the rapid and joint extraction of binarized IF and ISP directly from raw 3D OCT volumes. Unlike traditional extraction methods based on contour segmentation, REI-Net employs depth projection to shift the processing direction from B-scan to en-face. The architecture features a two-stage design: a Depth Compressor and a Plane Refiner. The Compressor incorporates a novel Depth-Breadth Separation Block (DBSB), which decouples volumetric feature extraction into a structural depth stream and a topological breadth stream based on Mamba. Subsequently, the Plane Refiner reconstructs fine-grained details via full-scale skip connections. Extensive experiments on the ZJUT-EIFD dataset demonstrate that REI-Net achieves state-of-the-art performance, yielding the highest NFIQ 2.0 quality scores for IF and the lowest false detection rates for ISP. Notably, the total inference time is reduced to just 605ms, which is orders of magnitude faster than existing methods, thereby constitutes a critical step toward practical, real-time OCT-based biometric systems.