A Lightweight and Noise-Robust Method for Internal OCT Fingerprint Reconstruction

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.