Subcutaneous sweat pore estimation from optical coherence tomography

Abstract

Sweat pore, one of the level 3 features of fingerprint, has attracted much attention in fingerprint recognition. Traditional sweat pores on surface fingerprint are unclear or blurred when fingers are stained or damaged. Subcutaneous sweat pores, as cross section of the sweat glands, are resistant to external interferences. With 3D fingertip information measured by optical coherence tomography (OCT), the subcutaneous sweat pore estimation from OCT volume data is investigated. First, an adaptive subcutaneous pore image reconstruction method is proposed. It utilizes the skin surface and viable epidermis junction as reference and realizes depth-adaptive pore image reconstruction. Second, a dilated U-Net combining the U-Net with dilated convolution is proposed for subcutaneous sweat pore extraction, which can prevent information loss of sweat pores caused by downsampling. To the best knowledge, it is the first time that subcutaneous sweat pore extraction is investigated and proposed. Experiments on subcutaneous pore image reconstruction and sweat pore extraction are both conducted. The qualitative and quantitative results show that the proposed adaptive method performs better in subcutaneous pore image reconstruction compared with the fix-depth method, and the dilated U-Net outperforms other methods on subcutaneous sweat pore extraction.This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

Recent Advances in Wearable Sensors for the Monitoring of Sweat: A Comprehensive Tendency Summary

“…This study exploits the essential difference between living and fake fingerprints, i.e., the secretion of sweat. Sweat hole detection by materials is used to complete biometrics, eliminating the need for complex feature extraction algorithms used in previous recognition steps [54][55][56][57][58]. This approach is expected to improve the reliability and security of existing fingerprint-recognition technologies.…”