Sweat Gland Extraction From Optical Coherence Tomography Using Convolutional Neural Network
Sweat Gland Extraction From Optical Coherence Tomography Using Convolutional Neural Network
复制标题
使用卷积神经网络从光学相干断层扫描中提取汗腺
DOI：
10.1109/tim.2022.3223077
复制
发表时间：
2023
影响因子：
5.6
通讯作者：
Liang Ronghua
中科院分区：
文献类型：
--
作者：
Zhang Yilong;Li Xiaojing;Wang Haixia;Wang Ruxin;Chen Peng;Liang Ronghua
汗孔作为指纹的三级特征，在指纹识别领域引起了人们的关注，并已成功应用于自动指纹识别系统中。当手指受到污染、干燥或损坏时，传统的表面汗孔变得不清晰或消失。这些不稳定因素对收集毛孔造成了重大挑战。皮下汗腺属于手指的内部组织，其稳定且不受外部干扰。本研究探讨从光学相干断层扫描（OCT）采集的指尖体积数据中提取皮下汗腺。首先，提出了一种改进的多任务V-Net，从OCT体数据中提取皮下汗腺。该网络具有用于特征提取的编码路径和分别用于提取汗腺边界和区域的两个解码路径。多任务方案的目的是加强边界和形状信息的汗腺，并防止错误提取所造成的干扰，从其他组织。其次，提出了三种映射方法来解决汗腺的不同空间方向的问题。这三种映射方法，即全局直接映射（GDM），局部直接映射（LDM）和圆柱拟合映射（CFM），被用来映射汗腺的表面指纹。在汗腺提取、映射和匹配方面进行了实验。定性和定量的结果表明，所提出的网络提取汗腺优于其他方法，LDM和CFM方法得到更准确的位置上的汗腺的表面指纹比GDM。在匹配实验中，双解码V-Net（DDVN）的等错误率（EER）达到0.58%，验证了汗腺的识别能力和所提出的网络的有效性。
As the Level 3 features of fingerprint, sweat pores have attracted attention in the field of fingerprint recognition and have been successfully applied to automatic fingerprint recognition systems. Traditional surface sweat pores become unclear or disappeared when the finger is contaminated, dried, or damaged. These unstable factors create major challenges in collecting sweat pores. Subcutaneous sweat glands belong to the internal tissues of fingers, which are stable and immune to external disturbances. This study investigated the extraction of subcutaneous sweat glands from fingertip volume data collected by optical coherence tomography (OCT). First, an improved multitask V-Net is proposed to extract subcutaneous sweat glands from OCT volume data. The network has an encoding path for features extraction and two decoding paths for extracting sweat gland boundaries and regions, respectively. The multitask scheme is designed to enhance the boundary and shape information of sweat glands and to prevent false extraction caused by interference from other tissues. Second, three mapping methods are proposed to address the problem of different spatial orientations of sweat glands. These three mapping methods, namely, global direct mapping (GDM), local direct mapping (LDM), and cylindrical fitting mapping (CFM), are used to map sweat glands to the surface fingerprint. Experiments are conducted in terms of sweat gland extraction, mapping, and matching. The qualitative and quantitative results show that the proposed network for sweat glands extraction outperforms other methods and that the LDM and CFM methods derive more accurate positions of sweat glands on the surface fingerprint than GDM. In the matching experiment, the equal error rate (EER) of dual-decoding V-Net (DDVN) reached 0.58%, which verified the recognition ability of sweat glands and the effectiveness of the proposed network.
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DOI：
10.1109/spac.2017.8304344
发表时间：
2017-12
期刊：
2017 International Conference on Security, Pattern Analysis, and Cybernetics (SPAC)
影响因子：
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作者：
Shuang Sun;Zhenhua Guo
通讯作者：
Shuang Sun;Zhenhua Guo
DOI：
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发表时间：
2007-08
期刊：
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作者：
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通讯作者：
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选择使用细节和毛孔进行指纹识别的参考高分辨率
DOI：
10.1109/tim.2010.2062610
发表时间：
2011-03
影响因子：
5.6
作者：
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通讯作者：
Luo, Nan
DOI：
10.1109/tsmc.2019.2957411
发表时间：
2021-09
期刊：
IEEE Transactions on Systems, Man, and Cybernetics: Systems
影响因子：
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作者：
Yuanrong Xu;Yao Lu;Guangming Lu;Jinxing Li;Dafan Zhang
通讯作者：
Yuanrong Xu;Yao Lu;Guangming Lu;Jinxing Li;Dafan Zhang
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发表时间：
2019-07
期刊：
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影响因子：
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作者：
Yuanrong Xu;Guangming Lu;Yao Lu;David Zhang
通讯作者：
Yuanrong Xu;Guangming Lu;Yao Lu;David Zhang