A New Approach to External and Internal Fingerprint Registration With Multisensor Difference Minimization

Yu, Yang; Wang, Haixia; Chen, Peng; Zhang, Yilong; Guo, Zhenhua; Liang, Ronghua

doi:10.1109/tbiom.2020.3007289

Cited by 13 publications

(1 citation statement)

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“…Optical coherence tomography (OCT) is a non-invasive imaging technique that can acquire information about 0-3 mm under the skin surface, and has become popular in three-dimensional (3D) fingerprint capturing [11][12][13]. It obtains high-resolution, 3D volume data of finger, which contains information of biological tissues such as epidermis, dermis, and sweat gland [14,15], and so on. The sweat glands measured by OCT are presented between skin surface and viable epidermis junction.…”

Section: Introductionmentioning

confidence: 99%

Subcutaneous sweat pore estimation from optical coherence tomography

Ding

Wang

Chen

et al. 2021

IET Image Processing

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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.

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