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.

Optical coherence tomography (OCT), as a non-invasive and high-resolution three-dimensional imaging technology, can capture biological tissue structure information under the skin of fingertips. This structure information facilitates stronger anti-spoofing capability of automatic fingerprint recognition systems (AFRSs), and the reconstructed internal fingerprint images based on the structural information are more robust against poor skin conditions. Various internal fingerprint reconstruction methods have been proposed, but these approaches often ignore the continuity of spatial structure information, have a large number of model parameters and are sensitive to noise. Specific to these problems, this paper proposes a lightweight and noise-robust point detection network (LNPDN) to reconstruct internal fingerprints. At first, by combining the ShuffleNet with the temporal shift module and self-attention, the continuity of spatial information is considered. Meanwhile, the previous refined tissue structural region segmentation task, which is highly affected by noise, is transformed into an easy noise-robust feature point detection mission. Then, these detected points are synthesized into a curve to represent the upper envelope of the viable epidermis by linear interpolation. Finally, internal fingerprint image is reconstructed by averaging those pixel values at a certain depth range below the envelope. The experimental results show the proposed feature point extraction model for the central vertex of ridge blocks reaches the F1-score value of 93.911%, and the average minimum point-segment distance between the proposed curve and the target curve is 1.475. It demonstrates that the proposed model can well extract the central vertex of the ridge blocks and the curve can reflect the location of the viable epidermis. We also compared the recognition capabilities of internal fingerprints extracted from 2138 OCT fingerprint volume data on the public OCT fingerprint benchmark dataset. Our method achieves the lowest equal error rate of 0.167%, with a relative reduction of 60.91% compared with state-of-the-art reconstruction methods.