Binarized Internal Fingerprint Reconstruction From Optical Coherence Tomography Based on Image Region Regression

Internal fingerprint reconstruction is critical for bridging traditional fingerprint recognition with Optical Coherence Tomography (OCT)-based techniques. However, current reconstructed internal fingerprints often suffer from low ridge-valley contrast, noise interference, and ridge adherence issues. Traditional fingerprint enhancement techniques address these challenges but involve reconstructing 3D OCT fingerprints into 2D internal fingerprints, followed by enhancement. This two-step approach leads to module inconsistencies and difficulties in parameter setting during the enhancement process. To overcome these limitations, we for the first time propose a novel method that directly reconstructs binarized internal fingerprints. The proposed method employs an image region regression module that directly treats ridge blocks within B-scan images as regional units for regression, yielding 1D feature vectors representing ridges and valleys. Additionally, leveraging the continuity of information between adjacent B-scan images, a window adjustment function is introduced to refine the regression values, ensuring more stable binarized internal fingerprints. Experiments were conducted on publicly available OCT fingerprint benchmark datasets to compare the minutiae extraction and matching performance. The binarized internal fingerprints obtained by the proposed method achieved the highest mean NFIQ2 score. Based on the NBIS software compared to existing OCT internal fingerprint reconstruction methods, the proposed method achieved the lowest Equal Error Rate (EER) of 0.78%. In addition, compared to traditional fingerprint enhancement methods, the proposed method attained the highest F1-score for minutiae extraction at 72.39%. It also achieved the lowest EER and represented a 37.1% reduction compared to the best existing result.