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dc.contributor.authorMejia-Herrera, Mateo
dc.contributor.authorBotero-Valencia, Juan
dc.contributor.authorHernández-García, Ruber
dc.date.accessioned2024-03-18T14:22:29Z
dc.date.available2024-03-18T14:22:29Z
dc.date.issued2024
dc.identifier.urihttp://repositorio.ucm.cl/handle/ucm/5241
dc.description.abstractBiometric characterization systems are generally used in safety-related applications because they allow the identification or verification of individuals based on human body traits. In recent years hand veins have become an attractive biometric trait due to their advantages compared with other classical biometric traits (i.e., fingerprints, iris, face). However, due to the number of possible architectures for feature extraction and individual identification, different combinations between such methods should be evaluated to give a baseline for further vein biometrics development. This work presents a comparative analysis for individual identification based on hand-vein biometrics, which combines four feature extraction techniques and three classic machine learning techniques using two main types of images. The results show the reliability of some combinations for hand-vein biometric identification achieving accuracy levels above 98% and an Equal Error Rate under 3.2%.es_CL
dc.language.isoenes_CL
dc.rightsAtribución-NoComercial-SinDerivadas 3.0 Chile*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/cl/*
dc.sourceLecture Notes in Networks and Systems, 822, 265-283es_CL
dc.subjectHand-Veinses_CL
dc.subjectVein feature extractiones_CL
dc.subjectBiometric systemses_CL
dc.subjectMachine learninges_CL
dc.titleComparative lightweight scheme for individual identification through hand-vein patternses_CL
dc.typeArticlees_CL
dc.ucm.indexacionScopuses_CL
dc.ucm.urispringerlink.ucm.elogim.com/chapter/10.1007/978-3-031-47721-8_18es_CL
dc.ucm.doidoi.org/10.1007/978-3-031-47721-8_18es_CL


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Atribución-NoComercial-SinDerivadas 3.0 Chile
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