End-to-End Surface and Internal Fingerprint Reconstruction From Optical Coherence Tomography Based on Contour Regression

Abstract

Optical coherence tomography (OCT), as a non-destructive and high-resolution imaging technique, has been used to collect 3D fingertip data, which contains surface and internal fingerprints. Methods have been proposed for OCT fingerprint reconstruction. However, these methods have complex processing flow and are time consuming. In this paper, an end-to-end convolutional neural network based surface and internal fingerprint reconstruction method is proposed. A simple yet effective contour regression module is proposed and integrated in the network for direct estimation of contours of stratum corneum and viable epidermis junction from noisy OCT volume data, thus greatly simplify the processing flow. The proposed network further integrates multi-task learning with conventional segmentation task as auxiliary task and contour regression task as main task to facilitate the feature extraction and improve the robustness of the network. Depthwise separable convolution is adapted to a light-weight network for network computation complexity reduction. To the best of our knowledge, it is the first time that an end-to-end method is proposed for surface and internal fingerprint extraction from noisy OCT volume data. Experiments and comparisons are carried out in terms of contour estimation accuracy, fingerprint quality, fingerprint matching performance and computation efficiency. Compared with conventional method, the proposed method utilizes only 6% of original network parameters and 0.7% of original computation time, but achieves comparably results. Fingerprint by depth proves the accuracy and robustness of contour regression than pixel-wise layer segmentation. The proposed method is noise-insensitive, process-simple and time-efficient for OCT fingerprint reconstruction, which is significant for real time application in Automated Fingerprint Recognition Systems.