Vision Paper: Hyperspectral Analysis of Finger Skin Reflectance for Resilient Biometric Systems

Vision Paper: Hyperspectral Analysis of Finger Skin Reflectance for Resilient Biometric Systems
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DOI:
10.1109/bigdata59044.2023.10386372
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发表时间:
2023-12
期刊:
2023 IEEE International Conference on Big Data (BigData)
影响因子:
--
通讯作者:
Emanuela Marasco
Emanuela Marasco
中科院分区:
其他
文献类型:
--
作者:
Emanuela Marasco

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高光谱成像(HSI)通过数百个连续的波长间隔传递信息,优于RGB和多光谱成像的能力。这种新兴技术可以实时监测材料的空间分辨光谱信息。本文探讨了使用HSI分类来分析人体手指皮肤的光谱结构。人类皮肤的光谱反射率被认为在个体之间存在显着差异,但是找到人类皮肤反射率的典型特征及其在人群中的分布是开放的研究问题。通过光谱仪从不同人群中获得的皮肤光谱被证明是散布的,因此使用它的系统不会受到种族的挑战。现有的相关研究仅使用反射率计来探索人类皮肤反射率的特征。而不是使用生物化学,这项研究使设计基于图像的手工制作的功能,以确定其代表性。虽然缺乏生化成分限制了对外在因素的鉴别验证,但反射率具有作为鉴别因素的潜力。作为第一个使用HSI来评估反射率的研究,我们专注于确定这种潜力的程度,包括其在机器学习应用中用于身份验证的潜在用途。
Hyperspectral imaging (HSI) outperforms the ability of RGB and multi-spectral imaging by conveying information through hundreds of contiguous wavelength intervals. This emerging technology can enable real-time monitoring of spatially resolved spectral information of materials. This paper explores the use of HSI classification to analyze the spectral structure of the human skin of fingers. The spectral reflectance of human skin is believed to vary significantly between individuals, but finding a typical signature for human skin reflectance and what its distribution is across a population are open research questions. The skin spectra acquired through a spectrograph from a diverse population are proven to be interspersed, thus the system using it would not be challenged by ethnicity. Existing related studies are using spectrophotometers only to explore features of human skin reflectance. Rather than use biochemistry, this research enables the design of image-based hand-crafted features to determine its representation. Although the lack of a biochemical component limits identity verification to extrinsic factors, reflectance has the potential as an identifying factor. As the first research to use HSI to assess reflectance, we focus on determining the extent of that potential, including its potential use in machine learning applications for identity verification.