Abstract
In this study, an automatic algorithm has been presented based on a convolutional neural network (CNN) employing U-net. An ellipsoid and an ellipse were applied for approximation of a three-dimensional sweat duct and en face sweat pore at the different depths, respectively. The results demonstrated that the length and the diameter of the ellipsoid can be used to quantitatively describe the sweat ducts, which has a potential for estimating the frequency of resonance in millimeter (mm) wave and terahertz (THz) wave. In addition, projection-based sweat pores were extracted to overcome the effect that the diameters of en face sweat pores depend on the depth. Finally, the projection-based image of sweat pores was superposed with a maximum intensity projection (MIP)-based internal fingerprint to construct a hybrid internal fingerprint, which can be applied for identification recognition and information encryption.
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